WO2025050118A1 - Computational exploration of the global microbiome for antibiotic discovery - Google Patents

Abstract

Novel antibiotics are needed to combat the antibiotic-resistance crisis. Presented herein are machine learning-based approaches for predicting antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset that can include metagenomes and prokaryotic genomes from environmental and host-associated habitats to create a comprehensive catalog comprising distinct, non-redundant peptides, the majority of which are novel. This platform provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences. To validate predictions, 100 AMPs were synthesized and tested against clinically relevant drug-resistant pathogens and human gut commensals, both in vitro and in vivo. Many of the synthesized peptides were active, with a large number of them targeting pathogens. The presently described computational approach can identify millions of prokaryotic AMP sequences, opening new avenues for antibiotic discovery.

Description

COMPUTATIONAL EXPLORATION OF THE GLOBAL MICROBIOME FOR

ANTIBIOTIC DISCOVERY

GOVERNMENT SUPPORT

[0001] This invention was made with government support under GM138201 awarded by the National Institutes of Health and HDTRA1- 18- 1-0041, HD TRA 1-21-1-0014, and HDTRA1-21-1-0014 awarded by the Defense Threat Reduction Agency. The government has certain rights in the invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] The present application claims the benefit of priority to U.S. Provisional App. No. 63/579,834, filed August 31, 2023, the entire contents of which are incorporated herein by reference.

BACKGROUND

[0003] Antibiotic-resistant infections are becoming increasingly difficult to treat with conventional therapies¹. Indeed, such infections currently kill 1.27 million people per year². Therefore, there is an urgent need for novel methods for antibiotic discovery. Computational approaches have been recently developed to accelerate our ability to identify novel antibiotics, including antimicrobial peptides (AMPs)^{3 9}. Recently, proteome mining approaches have even been developed to identify antimicrobial agents in extinct organisms in an attempt to further expand our repertoire of known antimicrobials¹⁰.

[0004] AMPs, found in all domains of life^{11 14}, are short sequences (here operationally defined as 10-100 amino acid residues¹⁵) capable of disturbing microbial growth^12,15. AMPs most commonly interfere with cell wall integrity and cause cell lysis^{12 16}. Natural AMPs can originate by proteolysis^{4 17}, by non-ribosomal synthesis¹⁸, or they can be encoded within the genome 19.

[0005] Bacteria live in an intricate balance of antagonism and mutualism in natural habitats. AMPs play an important role in modulating such microbial interactions and can displace competitor strains, facilitating cooperation²⁰. For instance, pathogens such as Shigella spp.²¹, Staphylococcus spp.²², Vibrio cholerae¹³ , and Listeria spp.²⁴‘² produce AMPs that eliminate competitors (sometimes from the same species), allowing them to occupy their niche.

[0006] AMPs hold promise as potential therapeutics and have already been used clinically as antiviral drugs e.g., enfuvirtide and telaprevir²⁶). AMPs that exhibit immunomodulatory properties are currently undergoing clinical trials²⁷, as are AMPs that may be used to address yeast and bacterial infections²⁸ (e.g., pexiganan, LL-37, PAC-113). Although most AMPs display broad-spectrum activity, some are only active against closely related members of the same species or genus²⁹. Such AMPs are more targeted agents than conventional broad-spectrum antibiotics^30,31. Furthermore, contrary to conventional antibiotics, the evolution of resistance to many AMPs occurs at low rates and is not related to cross-resistance to other classes of widely used antibiotics^4,32,33.

[0007] The application of metagenomic analyses to the study of AMPs has been limited due to technical constraints, primarily stemming from the challenge of distinguishing genuine protein-coding sequences from false positives³⁴.

BRIEF DESCRIPTION OF THE FIGURES

[0008] FIG. 1A demonstrates how assembled 63,410 publicly available metagenomes were assembled from diverse habitats. A modified version of Prodigal³⁴, which can also predict smORFs (30-300 bp), was used to predict genes on the resulting metagenomic contigs as well as on 87,920 microbial genomes from ProGenomes2⁴³. Macrel⁴² was applied to the 4,599,187,424 predicted smORFs to obtain 863,498 non-redundant c_AMPs (see also Fig. 8). c AMPs were then hierarchically clustered in a reduced amino acids alphabet using 100%, 85%, and 75% identity cutoffs. We observed at 75% of identity 118,051 non-singleton clusters, and 8,788 of them were considered families (> 8 c AMPs). FIG. IB shows that only 9% of c_AMPs have detectable homologs in other small protein databases (SmProt 2⁵⁴, STsORFs⁹²), bioactive peptide databases (DRAMP⁴⁶ version 3.0, starPepDB 45k⁹³), and general protein datasets (GMGCvP⁵) - see also Fig. 9B. Also shown is the number of homologs in the AMPSphere in each database as well as the total. The number of homologs passing all quality tests, regardless their experimental evidence of translation/transcription is also shown along with the percentage it represents in the homologs identified. Note that some peptides have homologs in multiple databases and, thus, the total count is not the sum of the individual databases. FIG. 1C shows rarefaction curves showing how AMP discovery is impacted by sampling, with most of the habitats presenting steep sampling curves. FIG. ID illustrates how sharing of c AMPs between habitats is limited. The width of ribbons represents the proportion of the shared c AMPs in the habitat on the left. See also Figs. 9C- D.

[0009] FIG. 2A illustrates the number of AMPSphere candidates passing each of the tests proposed for quality. FIG. 2B shows the number of AMP candidates predicted as AMP by AMP prediction systems beyond Macrel (AMPScanner v2⁴⁹, ampir⁴⁰ - with the model for mature peptides, amPEPpy⁵⁰, APIN⁵¹ - with their proposed model, AI4AMP⁵², and AMPLify⁵³). Only a small portion of AMPSphere (<2%) cannot be co-predicted by any system other than Macrel⁴².

[0010] FIG. 3 A shows the distribution of positions (as a percentage of the length of the larger protein) from which the AMP homologs start their alignment. About 7% of c_AMPs are homologous to proteins from GMGCvl⁵⁵, with approximately one-fourth of the hits sharing start positions with the larger protein. FIG. 3B provides as an illustrative example of an AMP homologous to a full-length protein, AMP10.271_016 was recovered from three samples of human saliva from the same donor⁹⁴. AMP10.271 016 is predicted to be produced by Prevotella jejuni, sharing the start codon (bolded) of an NAD(P)-dependent dehydrogenase gene (WP_089365220.1), the transcription of which was stopped by a mutation (in red; TGG > TGA). (C) The distribution of AMPs per OG class (left) and their enrichment in comparison to full-length proteins from GMGCvl ³⁵ (right). OGs were classified into subgroups according to the number of c AMPs they were affiliated with. The OGs of unknown function represent the largest (2,041 out of 3,792 OGs) and most enriched (PKruskai = 2.66- 10'³⁹) class with homologs to c AMPs in GMGCvl⁵³. Interestingly, when considered individually, the number of c_AMP hits to unknown OGs was the lowest (PKruskai = 6 - 10'³). These results do not change when underrepresented OGs are excluded by using different thresholds (e.g, at least 10, 20, or 100 homologs per OG). [0011] FIG. 4A shows how, compared to other proteins, c AMPs in conserved genomic architectures tend to be closer to ribosomal machinery -related genes than families of proteins with different sizes (all length and small proteins with < 50 amino acids). FIG. 4B shows how proportion of c_AMPs in a genome context involving antibiotic resistance genes is lower than in other gene families. FIG. 4C illustrates how proportion of c AMPs in neighborhoods with antibiotic synthesis-related genes is very small (<0.25%). FIG. 4D illustrates the conserved genomic context of the gene encoding AMP10.015_426 is shown in different genomes (the tree on the left depicts the phylogenetic relationship of the genes homologous to it). This c_AMP is homologous to the ribosomal protein rpsH, and is found in the context of rpsH and other ribosomal protein genes.

[0012] FIG. 5A shows the fractions of AMPs (or AMP families) that are accessory (present in <50% of genomes from same species), shell (50-95%), or core (>95%). FIG. 5B illustrates of the lowest taxonomic level at which c_AMPs were annotated. In detail (right), the top 10 genera with the highest numbers of c AMPs included in AMPSphere. Animal - associated genera (e.g., I ‘revote I la, Faecalibacterium, CAG-llff) contribute the most c_AMPs, possibly reflecting data sampling. FIG. 4C illustrates that the PAMP per genus (calculated with c_AMPs in AMPSphere), it was observed the distribution of c_AMPs per phylum, with Bacillota A as the densest (the number of samples used to build the graph is shown above each box). FIG. 5D shows the detected taxa in AMPSphere, using the GTDB^67,68 reference tree. The gray bars show PAMP distribution with respect to taxonomy, with black bars representing the confidence interval of 95%. Bacillota A, Actinomycetota, and Pseudomonadota are the densest phyla in c AMPs. As a reference, the median of PAMP f°^r the presented genera is indicated by a magenta dashed line.

[0013] FIG. 6A provides the amino acid frequency in c AMPs from AMPSphere, AMPs from databases (DRAMP⁴⁶ version 3, APD3⁷¹, and DBAASP⁷⁰), and encrypted peptides⁴ (EPs) from the human proteome. FIG. 6B is a heat map with the percentage of secondary structure found for each peptide in three different solvents: water, 60% trifluoroethanol (TFE) in water, and 50% methanol (MeOH) in water. Secondary structure was calculated using BeStSel server⁹⁵. FIG. 6D illustrates the activity of c_AMPs assessed against ESKAPEE pathogens and human gut commensal strains. Briefly, 10⁶ CFU mL ¹ was exposed to c_AMPs two-fold serially diluted ranging from 64 to 1 pmol L'¹ in 96-wells plates and incubated at 37 °C for one day. After the exposure period, the absorbance of each well was measured at 600 nm. Untreated solutions were used as controls and minimal concentration values for complete inhibition were presented as a heat map of antimicrobial activities (pmol-L'¹) against 11 pathogenic and eight human gut commensal bacterial strains. All the assays were performed in three independent replicates and the heatmap shows the mode obtained within the two-fold dilutions concentration range studied. Gram-positive (+) and Gram-negative (-) bacteria are indicated as such on top panel C. FIG. 6D illustrates fluorescence values relative to polymyxin B (PMB, positive control) of the fluorescent probe l-(N-phenylamino)naphthalene (NPN) that indicate outer membrane permeabilization of A. baumannii ATCC 19606 cells. FIG. 6E provides fluorescence values relative to PMB (positive control) of 3,3 '-dipropylthiadi carbocyanine iodide [DiSC3-(5)], a hydrophobic fluorescent probe used to indicate cytoplasmic membrane depolarization of A. baumannii ATCC 19606 cells. Depolarization of the cytoplasmic membrane occurred with a slow kinetics compared to the permeabilization of the outer membrane and took approximately 20 min to stabilize.

[0014] FIG. 7A is a schematic of the skin abscess mouse model used to assess the anti-infective activity of the peptides against A. baumannii cells. FIG. 7B shows how peptides were tested at their MIC in a single dose one hour after the establishment of the infection. Each group consisted of three mice (n = 3) and the bacterial loads used to infect each mouse derived from a different inoculum. Statistical significance in (FIG. 7B) was determined using one-way ANOVA where all groups were compared to the untreated control group; P-values are shown for each of the groups. FIG. 7C provides how, to rule out toxic effects of the peptides, mouse weight was monitored throughout the experiment. Features on the violin plots represent median and upper and lower quartiles. Data in are the mean ± the standard deviation.

[0015] FIG. 8 provides density curves; the arbitrary density units not shown as all curves are independently normalized so the area under the curve is one. For each dataset and feature, the top 1% and bottom 1% of values were considered outliers and are not shown in the plot. Proportions of residues with small side chains [A, C, D, G, N, P, S, T, V] per c_AMP along with the proportions of basic residues [H, R, K] per c_AMP were also shown. The distributions of each feature were compared among the datasets using the Mann-Whitney test with multiple hypothesis testing corrected using Holm-Sidak. Almost all differences are significant (adjusted p-value < 0.05). The exceptions are: aliphatic index did not differ between the peptides from DRAMP version 3⁴⁶ database and the ones present in the positive training set used in Macrel⁴² (P ann = 0.71), AMPSphere peptides did not differ from the positive training set used in Macrel⁴² in the fraction of aromatic (PMann = 0.58), non-polar (PMann = 0.97), polar (Pwann = 0.97), and acidic (PMann = 0.69) residues; the instability index (PMann = 0.58) and the hydrophobicity (PMann = 0.31) of AMPSphere peptides also were not different from the positive training set used in Macrel⁴².

[0016] FIG. 9A illustrates how quality assessment of AMPSphere revealed most of the peptides passed at least one of the tests. The RNAcode test depends on gene diversity, which is very low for AMPSphere, and therefore, determined a low rate of positives among our candidates. FIG. 8B provides how c AMPs homologous to databases of validated bioactive peptides also showed a higher average quality of these datasets. FIG. 9C shows how the limited overlap of c AMPs among habitats argues in favor of using habitat groups to gain resolution. Note that the group of habitats with the highest paired overlaps belong to human body sites and samples from human guts and non-human mammalian guts. Only habitats with at least 100 samples were shown. FIG. 9D provides how it is also possible to observe the great proportion of rare genes in AMPSphere from different habitat groups, in which few genes are largely detected.

[0017] FIG. 10 illustrates how, to validate the clustering procedure using a reduced amino acid alphabet, samples of 1,000 peptides were randomly drawn from AMPSphere (excluding representative sequences) and aligned against their cluster representatives. Three different levels (I, II, and III) of clustering were tested. The E-values were computed per alignment and plotted against the corresponding alignment identity. The averaged proportion of significant alignments is shown in each graph.

[0018] FIG. 11 A illustrates minimal inhibitory concentration values for polymyxin B, a peptide antibiotic, and levofloxacin against all the strains tested. Polymyxin B and levofloxacin were used as positive controls in all antimicrobial assays. c_AMPs secondary structural tendency was analyzed using three different solvents. FIG. 11B shows analysis in water. FIG. 11C shows the analysis in a trifluoroethanol (TFE) and water mixture (3:2, V:V). FIG. 11D shows the analysis in a methanol (MeOH) and water mixture (1: 1, V:V). The experiments were carried out at 25 °C, and the circular dichroism spectra shown are an average of three accumulations obtained using a quartz cuvette with an optical path length of 1.0 mm, ranging from 260 to 190 nm at a rate of 50 nm min'¹ and a bandwidth of 0.5 nm. All peptides were tested at a concentration of 50 pmol L'¹, with respective baselines recorded prior to measurement. A Fourier transform filter was applied to minimize background effects.

[0019] FIG. 12A provides minimal inhibitory concentration values of the scrambled versions of five of the lead c_AMPs from AMPSphere tested against the same 11 pathogenic strains and eight gut commensal strains used to assess the activity of the c_AMPs. The scrambled peptides secondary structural tendency was analyzed using three different solvents. FIG. 12B shows analysis in water. FIG. 12C shows the analysis in a TFE and water mixture (3 :2, V:V). FIG. 12D shows the analysis in a MeOH and water mixture (1: 1, V:V). The experiments were carried out in the same conditions as the ones used for the c AMPs. A Fourier transform filter was applied to minimize background effects. FIG. 12E provides a heat map with the percentage of secondary structure found for each peptide in three different solvents: water, 60% TFE in water, and 50% MeOH in water. Secondary structure was calculated using BeStSel server⁹⁵.

[0020] FIG. 13 A shows fluorescence values relative to polymyxin B (PMB, positive control) of the fluorescent probe l-(N-phenylamino)naphthalene (NPN) that indicate outer membrane permeabilization of P. aeruginosa PAO1 cells. FIG. 13B provides fluorescence values relative to PMB (positive control) of 3,3 '-dipropylthiadicarbocyanine iodide [DiSC3- (5)], a hydrophobic fluorescent probe used to indicate cytoplasmic membrane depolarization of P aeruginosa PAO1 cells. FIG. 13C provides bacterial counts four days post infection, the c_AMPs were tested at their MIC in a single dose one hour after the establishment of the infection. Each group consisted of three mice (n = 3) and the bacterial loads used to infect each mouse were derived from a different inoculum. Statistical significance in was determined using one-way ANOVA where all groups were compared to the untreated control group; P-values are shown for each of the groups. FIG. 13D illustrates mouse weight throughout the experiment (mean ± the standard deviation). Features on the violin plots represent median and upper and lower quartiles.

SUMMARY

[0021] Provided herein are methods for forming a database-assisted platform providing one or more functional and/or physicochemical features of respective metagenomic-derived candidate antimicrobial peptides (AMPs), the methods comprising selecting one or more genomes or metagenomes for inclusion in the platform; using an NGS assembler to assemble reads in order to identify contigs from the genomes or metagenomes; from the identified contigs, predicting small open reading frames (smORFs); removing duplicate smORFs to yield non-redundant smORFs; and, predicting candidate AMPs from the non-redundant smORFs.

[0022] Also disclosed are methods of treating a microbial infection in a subject comprising administering to the subject a therapeutically effective amount of an AMP that has been identified according to any one of the disclosed methods for forming a database-assisted platform.

[0023] The present disclosure also provides methods comprising contacting a biofilm with an effective amount of an AMP that has been identified according to any one of the disclosed methods for forming a database-assisted platform.

[0024] Also provided are compositions comprising an AMP that has been identified according to any one of the disclosed methods for forming a database-assisted platform and pharmaceutically acceptable carrier, diluent, or excipient.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0025] The present invention may be understood more readily by reference to the following detailed description taken in connection with the accompanying examples, which form a part of this disclosure. It is to be understood that this invention is not limited to the specific products, methods, conditions or parameters described and/or shown herein, and that the terminology used herein is for the purpose of describing particular embodiments by way of example only and is not intended to be limiting of the claimed invention. [0026] The disclosures of each patent, patent application, and publication cited or described in this document are hereby incorporated herein by reference, in their entirety.

[0027] As employed above and throughout the disclosure, the following terms and abbreviations, unless otherwise indicated, shall be understood to have the following meanings.

[0028] In the present disclosure the singular forms “a”, “an”, and “the” include the plural reference, and reference to a particular numerical value includes at least that particular value, unless the context clearly indicates otherwise. Thus, for example, a reference to “a compound” is a reference to one or more of such compounds and equivalents thereof known to those skilled in the art, and so forth. Furthermore, when indicating that a certain chemical moiety “may be” X, Y, or Z, it is not necessarily intended by such usage to exclude other choices for the moiety.

[0029] When values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another embodiment. As used herein, “about X” (where X is a numerical value) preferably refers to ±10% of the recited value, inclusive. For example, the phrase “about 8” may refer to a value of 7.2 to 8.8, inclusive; as another example, the phrase “about 8%” may refer to a value of 7.2% to 8.8%, inclusive. Also, when the term “about” precedes a range, it is understood that the term modifies both recited endpoints and all points embraced within the range. For example, the phrase “about 1-10” is understood to mean “about 1 to about 10”, as well as “about x”, wherein x refers to any value between 1 and 10. Where present, all ranges are inclusive and combinable. For example, when a range of “1 to 5” is recited, the recited range should be construed as including ranges “1 to 4”, “1 to 3”, “1-2”, “1-2 & 4-5”, “1-3 & 5”, and the like. In addition, when a list of alternatives is positively provided, such listing can be interpreted to mean that any of the alternatives may be excluded, e.g., by a negative limitation in the claims. For example, when a range of “1 to 5” is recited, the recited range may be construed as including situations whereby any of 1, 2, 3, 4, or 5 are negatively excluded; thus, a recitation of “1 to 5” may be construed as “1 and 3-5, but not 2”, or simply “wherein 2 is not included.”

[0030] Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. In order to address this need, presented herein are machine learning-based approaches for predicting antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset that can include metagenomes and prokaryotic genomes from environmental and host- associated habitats to create the AMPSphere, a comprehensive catalog comprising distinct, non-redundant peptides, the majority of which are novel. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate predictions, 100 AMPs were synthesized and tested against clinically relevant drug-resistant pathogens and human gut commensals, both in vitro and in vivo. Many of the synthesized peptides were active, with a large number of them targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. The presently described computational approach can identify millions of prokaryotic AMP sequences, opening new avenues for antibiotic discovery.

[0031] The significance of small open reading frames (smORFs) has been historically overlooked in (meta)genomic analyses^{35 37}. In recent years, significant progress has been made in metagenomic analyses of human-associated smORFs^6,38. These advancements have incorporated machine learning (ML) techniques to identify smORFs encoding proteins belonging to specific functional categories^{39 42}. Notably, a recent study used predicted smORFs to uncover approximately 2,000 AMPs from metagenomic samples of human gut microbiomes⁶. Nevertheless, it is important to note that the human gut represents only a fraction of the overall microbial diversity, suggesting that there remains an immense potential for the discovery of AMPs from prokaryotes in the diverse range of habitats across the globe.

[0032] Pursuant to the present invention, ML was used to predict and catalog AMPs from the global microbiome as currently represented in public databases. By computationally exploring 63,410 publicly available metagenomes and 87,920 high-quality microbial genomes⁴³, a vast array of AMP diversity was uncovered. This resulted in the creation of the AMPSphere, a collection of 863,498 non-redundant peptide sequences, encompassing candidate AMPs (c AMPs) derived from (meta)genomic data. Remarkably, the majority of these c_AMP sequences had not been previously described. The present analysis revealed that these c AMPs were specific to particular habitats and were predominantly not core genes in the pangenome. [0033] Moreover, 100 c_AMPs were synthesized from AMPSphere and it was found that 79 were active, with 63 exhibiting antimicrobial activity in vitro against clinically significant ESKAPEE pathogens, which are recognized as public health concerns^44,45. These peptides were further compared to encrypted peptides (EPs), which are peptide sequences hidden in protein sequences and mined computationally^4,10, and demonstrated their ability to target bacterial membranes and were prone to adopting a-helical and P-structures. Notably, the leading candidates displayed promising anti -infective activity in a pre-clinical animal model. Together, the work disclosed herein provides a ML approach to identify new functional AMPs from the global microbiome.

[0034] Accordingly, provided herein are methods for forming a database-assisted platform providing one or more functional and/or physicochemical features of respective metagenomic-derived candidate antimicrobial peptides (AMPs), the methods comprising selecting one or more genomes or metagenomes for inclusion in the platform; using an NGS assembler to assemble reads in order to identify contigs from the genomes or metagenomes; from the identified contigs, predicting small open reading frames (smORFs); removing duplicate smORFs to yield non-redundant smORFs; and, predicting candidate AMPs from the non-redundant smORFs.

[0035] In some embodiments, the selection of the one or more genomes or metagenomes is according to criteria (i) whereby the genome or metagenome is tagged with taxonomy ID 408169 (for metagenome) or is a descendent of it in a taxonomic tree, (ii) whereby experiments with the genome or metagenome are listed as “METAGENOMIC”, or both (i) and (ii).

[0036] In some embodiments, metadata is curated from the selected one or more genomes or metagenomes to create groups based on similarity of habitat conditions. Habitat conditions include one or more of air, anthropogenic, aquatic, host-associated, alkaline pH, sediment, or terrestrial.

[0037] The selection of the one or more genomes or metagenomes can alternatively or additionally include assessing sample origin or other information relating to host species using an NCBI taxonomic identification number. [0038] The present methods may further comprise processing the assembled reads by trimming positions with a quality lower than a desired number, and discarding reads shorter than a specified number of base pairs, post trimming. The quality at which a given position is trimmed may be selected to be, for example, lower than 60, 55, 50, 45, 40, 35, 30, 25, 20, 15, or 10. The number of base pairs, post -trimming that represent a read to be discarded may be about 40 to about 100, such as about 40-90, 45-80, 50-75, 50-70, or 55-65, such as about 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100.

[0039] In some embodiments, metagenomes obtained from a host-associated microbiome may be passed through a filtering of reads mapping to the host genome, when available.

[0040] The NGS assembler that is used to assemble reads in order to identify contigs from the genomes or metagenomes may be, for example, optimized for metagenomes.

[0041] In some embodiments, the smORFs may be predicted from the identified contigs using prokaryotic gene recognition and translation initiation site identification. The prokaryotic gene recognition and translation initiation site identification may be, for example, Prodigal (PROkaryotic DYnamic programming Gene-finding Algorithm - see Hyatt, D., et al., BMC Bioinformatics 11, 119 (2010)), or a modified version thereof.

[0042] The length of the smORFs that are predicted from the contigs may be, for example, from about 20 to about 500 bp, such as 20-450, 25-400, 30-350, or 30-300 bp, or about 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, or 500 bp.

[0043] In certain embodiments, the candidate AMPs are predicted from the non- redundant smORFs using metagenomic AMP classification and retrieval. For example, the candidate AMPs may be predicted from the non-redundant smORFs using Macrel.

[0044] Optionally, singleton sequences may be removed from the predicted candidate AMPs. In some embodiments, singleton sequences are retained if they match a sequence from a data repository of antimicrobial peptides. Matching a sequence from a data repository of antimicrobial peptides can mean having at least or about 65, 70, 75, 80, or 85% amino acid identity to a sequence from a data repository, and/or an E-value of at least about 10'⁵ relative to a sequence from a data repository. The data repository may be, for example, the Data Repository of Antimicrobial Peptides (DRAMP)⁴⁶.

[0045] For purposes of further characterizing the candidate AMPs within the platform, candidate AMPs originating from a genomic database may assigned a taxonomy from the original genome. Alternatively or additionally, candidate AMPs originating from a metagenome may assigned a taxonomy predicted for the contig in which the candidate AMP was found.

[0046] The present methods may further comprise identifying potential structural configuration of a candidate AMP by using a secondary structure function from a calculation of a fraction of amino acids in the candidate AMP that tend to assume conformations of helix, turn, or sheet. This information may also be used to provide characterization of the candidate AMPs within the platform.

[0047] The candidate AMPs may be hierarchically clustered using a reduced amino acid alphabet and an identity cutoff of a preselected percentage. For example, the reduced amino acid alphabet may include about 5, 6, 7, 8, 9, 10, 11, or 12 amino acids. The identity cutoff may be about 70-100%, such as about 70, 75, 80, 85, 90, 95, or 100%. A sequential cutoff may be employed, such as a sequential cutoff of 100, 95, and 90%, or 100, 90, and 85%, or 100, 90, and 80%, or 100, 85, and 75%. Representative sequences of peptide clusters may be selected according to their length (selecting for the longest), with ties being broken by the alphabetical order. Optionally, the clustering procedure may be validated.

[0048] The present methods may further comprise synthesizing one or more of the identified candidate AMPs. Selection of candidate AMPs for synthesis may be according to criteria for solubility, criteria for synthesis, or both. For example, the criteria may be in accordance with those used in PepFun¹⁴⁵.

[0049] The synthesized candidate AMPs may individually be tested for antimicrobial activity to determine minimal inhibitory concentration (MIC). For example, the broth microdilution method may be used to determine MIC. The synthesized candidate AMPs may be subjected to circular dichroism assays, as described more fully infra. Membrane permeability with respect to a particular candidate AMP may be analyzed, for example, using the l-(N-phenylamino)naphthalene (NPN) uptake assay. In certain embodiments, ability of a candidate peptides to depolarize the cytoplasmic membrane may be assessed, for example, by measuring the fluorescence of the membrane potential -sensitive dye 3,3’- dipropylthiadicarbocyanine iodide [DiSC3-(5)].

[0050] Also disclosed are methods of treating a microbial infection in a subject comprising administering to the subject a therapeutically effective amount of an AMP that has been identified according to any one of the disclosed methods for forming a database-assisted platform. In some embodiments, the methods of treating a microbial infection comprises administering to the subject a therapeutically effective amount of an AMP according to any one or more of SEQ ID NOS: 1-100, provided below in Table 1.

TABLE 1

[0051] The present disclosure also provides compositions comprising an AMP that has been identified according to any one of the disclosed methods for forming a database-assisted platform and pharmaceutically acceptable carrier, diluent, or excipient. For example, the compositions may comprise an AMP according to any one or more of SEQ ID NOS: 1-100. In some embodiments of the present methods, two or more AMPs that have been identified according to any one of the disclosed methods for forming a database-assisted platform, such as two or more of SEQ IDS NOS: 1-100, may be administered to the subject or included in the present compositions.

[0052] As used herein, the phrase “therapeutically effective amount” refers to the amount of active agent (here, the antimicrobial peptide) that elicits the biological or medicinal response that is being sought in a tissue, system, animal, individual or human by a researcher, veterinarian, medical doctor or other clinician, which includes one or more of the following: (1) at least partially preventing the disease or condition or a symptom thereof; for example, preventing a disease, condition or disorder in an individual who may be predisposed to the disease, condition or disorder but does not yet experience or display the pathology or symptomatology of the disease;

(2) inhibiting the disease or condition; for example, inhibiting a disease, condition or disorder in an individual who is experiencing or displaying the pathology or symptomatology of the disease, condition or disorder (i.e., including arresting further development of the pathology and/or symptomatology); and

(3) at least partially ameliorating the disease or condition; for example, ameliorating a disease, condition or disorder in an individual who is experiencing or displaying the pathology or symptomatology of the disease, condition or disorder (i.e., including reversing the pathology and/or symptomatology).

[0053] The antimicrobial peptides that are administered, contacted with a biofilm, or included in a composition to the present disclosure may be provided in a composition that is formulated for any type of administration. For example, the compositions may be formulated for administration orally, topically, parenterally, enterally, or by inhalation (e.g., intranasally). The active agent may be formulated for neat administration, or in combination with conventional pharmaceutical carriers, diluents, or excipients, which may be liquid or solid. The applicable solid carrier, diluent, or excipient may function as, among other things, a binder, disintegrant, filler, lubricant, glidant, compression aid, processing aid, color, sweetener, preservative, suspensing/dispersing agent, tablet-disintegrating agent, encapsulating material, film former or coating, flavoring agent, or printing ink. Any material used in preparing any dosage unit form is preferably pharmaceutically pure and substantially non-toxic in the amounts employed. In addition, the active agent may be incorporated into sustained-release preparations and formulations. Administration in this respect includes administration by, inter alia, the following routes: intravenous, intramuscular, subcutaneous, intraocular, intrasynovial, transepithelial including transdermal, ophthalmic, sublingual and buccal; topically including ophthalmic, dermal, ocular, rectal and nasal inhalation via insufflation, aerosol, and rectal systemic. [0054] In powders, the carrier, diluent, or excipient may be a finely divided solid that is in admixture with the finely divided active ingredient. In tablets, the active ingredient is mixed with a carrier, diluent or excipient having the necessary compression properties in suitable proportions and compacted in the shape and size desired. For oral therapeutic administration, the active compound may be incorporated with the carrier, diluent, or excipient and used in the form of ingestible tablets, buccal tablets, troches, capsules, elixirs, suspensions, syrups, wafers, and the like. The amount of active agent(s) in such therapeutically useful compositions is preferably such that a suitable dosage will be obtained.

[0055] Liquid carriers, diluents, or excipients may be used in preparing solutions, suspensions, emulsions, syrups, elixirs, and the like. The active ingredient of this invention can be dissolved or suspended in a pharmaceutically acceptable liquid such as water, an organic solvent, a mixture of both, or pharmaceutically acceptable oils or fat. The liquid carrier, excipient, or diluent can contain other suitable pharmaceutical additives such as solubilizers, emulsifiers, buffers, preservatives, sweeteners, flavoring agents, suspending agents, thickening agents, colors, viscosity regulators, stabilizers, or osmo-regulators.

[0056] Suitable solid carriers, diluents, and excipients may include, for example, calcium phosphate, silicon dioxide, magnesium stearate, talc, sugars, lactose, dextrin, starch, gelatin, cellulose, methyl cellulose, ethyl cellulose, sodium carboxymethyl cellulose, microcrystalline cellulose, polyvinylpyrrolidine, low melting waxes, ion exchange resins, croscarmellose carbon, acacia, pregelatinized starch, crospovidone, HPMC, povidone, titanium dioxide, polycrystalline cellulose, aluminum methahydroxide, agar-agar, tragacanth, or mixtures thereof.

[0057] Suitable examples of liquid carriers, diluents and excipients, for example, for oral, topical, or parenteral administration, include water (particularly containing additives as above, e.g. cellulose derivatives, preferably sodium carboxymethyl cellulose solution), alcohols (including monohydric alcohols and polyhydric alcohols, e.g. glycols) and their derivatives, and oils (e.g. fractionated coconut oil and arachis oil), or mixtures thereof.

[0058] For parenteral administration, the carrier, diluent, or excipient can also be an oily ester such as ethyl oleate and isopropyl myristate. Also contemplated are sterile liquid carriers, diluents, or excipients, which are used in sterile liquid form compositions for parenteral administration. Solutions of the active agents can be prepared in water suitably mixed with a surfactant, such as hydroxypropylcellulose. A dispersion can also be prepared in glycerol, liquid polyethylene glycols, and mixtures thereof and in oils. Under ordinary conditions of storage and use, these preparations may contain a preservative to prevent the growth of microorganisms.

[0059] The pharmaceutical forms suitable for injectable use include, for example, sterile aqueous solutions or dispersions and sterile powders for the extemporaneous preparation of sterile injectable solutions or dispersions. In all cases, the form is preferably sterile and fluid to provide easy syringability. It is preferably stable under the conditions of manufacture and storage and is preferably preserved against the contaminating action of microorganisms such as bacteria and fungi. The carrier, diluent, or excipient may be a solvent or dispersion medium containing, for example, water, ethanol, polyol (for example, glycerol, propylene glycol, liquid polyethylene glycol and the like), suitable mixtures thereof, and vegetable oils. The proper fluidity can be maintained, for example, by the use of a coating, such as lecithin, by the maintenance of the required particle size in the case of a dispersion, and by the use of surfactants. The prevention of the action of microorganisms may be achieved by various antibacterial and antifungal agents, for example, parabens, chlorobutanol, phenol, sorbic acid, thimerosal and the like. In some instances, the antimicrobial peptides themselves may be sufficient to prevent contamination by microorganisms. In many cases, it will be preferable to include isotonic agents, for example, sugars or sodium chloride. Prolonged absorption of the injectable compositions may be achieved by the use of agents delaying absorption, for example, aluminum monostearate and gelatin.

[0060] Sterile injectable solutions may be prepared by incorporating the active agent in the pharmaceutically appropriate amounts, in the appropriate solvent, with various of the other ingredients enumerated above, as required, followed by filtered sterilization. Generally, dispersions may be prepared by incorporating the sterilized active ingredient into a sterile vehicle which contains the basic dispersion medium and the required other ingredients from those enumerated above. In the case of sterile powders for the preparation of sterile injectable solutions, the preferred methods of preparation may include vacuum drying and freeze drying techniques that yield a powder of the active ingredient or ingredients, plus any additional desired ingredient from the previously sterile-fdtered solution thereof.

[0061] Thus, an antimicrobial peptide may be in the present compositions and methods in an effective amount by any of the conventional techniques well-established in the medical field. For example, the administration may be in the amount of about 0.1 mg/day to about 500 mg per day. In some embodiments, the administration may be in the amount of about 250 mg/kg/day. Thus, administration may be in the amount of about 0.1 mg/day, about 0.5 mg/day, about 1.0 mg/day, about 5 mg/day, about 10 mg/day, about 20 mg/day, about 50 mg/day, about 100 mg/day, about 200 mg/day, about 250 mg/day, about 300 mg/day, or about 500 mg/day.

[0062] Also disclosed are methods comprising contacting a biofilm with an effective amount of an AMP that has been identified according to a method for forming a database- assisted platform according to the present disclosure. In some embodiments, the AMP comprises one or more of SEQ ID NOS: 1-100. Such methods may be effective to remove or reduce the presence of an unwanted biofilm, such as in hospitals or other medical settings, in sewer and filtration systems, in industrial settings, on equipment involved in food preparation or manufacture, in aquaculture or hydroponics, or in any other context that is prone to unwanted biofilm formation.

[0063] In accordance with the methods of treating a microbial infection in a subject or the methods comprising contacting a biofilm according to the present disclosure, microbes against which the present antimicrobial peptides are effective may be, for example, any unicellular organism, such as gram-negative bacteria, gram-positive bacteria, protozoa, viruses, bacteriophages, and archaea. The present peptides can have an antimicrobial effect with respect to any such microbe. Examples of bacteria against which the present compounds are effective to cause reduction in numbers include gram positive bacteria and gram negative bacteria, for example, Salmonella enterica, Listeria monocytogenes, Escherichia coli, Clostridium botulinum, Clostridium difficile, Campylobacter, Bacillus cereus, Vibrio parahaemolyticus, Vibrio cholerae, Vibrio vulnificus, Staphylococcus aureus, Yersinia enterocolitica, Shigella, Moraxella spp., Helicobacter, Stenotrophomonas, Bdellovibrio, Legi onella spp. (e.g., pneumophila), Neisseria gonorrhoeae, Neisseria meningitidis, Haemophilus influenzae, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Proteus mirabilis, Enterobacter cloacae, Enterococcus faecium, Serratia marcescens, Elelicobacter pylori, Salmonella enteritidis, Salmonella typhi, and combinations thereof. Examples of Salmonella enterica serovars that can be reduced using the compounds of the disclosure include, for example, Salmonella enteriditis, Salmonella typhimurium, Salmonella poona, Salmonella heidelberg, and Salmonella anatum. Exemplary viruses against which the present peptides are effective to cause reduction in numbers include coronaviruses, rhinoviruses, and influenza viruses.

Examples

[0064] The present invention is further defined in the following Examples. It should be understood that the examples, while indicating preferred embodiments of the invention, are given by way of illustration only, and should not be construed as limiting the appended claims. From the above discussion and the examples, one skilled in the art can ascertain the essential characteristics of this invention, and without departing from the spirit and scope thereof, can make various changes and modifications of the invention to adapt it to various usages and conditions.

Example 1 - Development of AMPSphere Platform, Including Characterization of AMPs

[0065] The present inventors developed AMPSphere, which incorporates c AMPs predicted with ML using Macrel⁴², a pipeline that uses random forests to predict AMPs from large peptide datasets with an emphasis on precision over recall. It was applied to 63,410 globally distributed publicly available metagenomes (Fig. 1 A) and 87,920 high-quality bacterial and archaeal genomes⁴³. Sequences present in a single sample were removed⁴², except when they had a significant match (defined as amino acids identity > 75% and E-value < I O ⁵) to a sequence in the AMP-dedicated database Data Repository of Antimicrobial Peptides (DRAMP) version 3.0⁴⁶. This resulted in 5,518,294 genes, 0.1% of the total predicted smORFs, coding for 863,498 non -redundant c AMPs (on average 37±8 residues long; Fig. lA and 8). Similar to validated sequences with antimicrobial activity^{42 47}-⁴⁸, c_AMPs from AMPSphere present a positive charge (4.7±2.6), high isoelectric point (10.9±1.2), amphiphilicity (hydrophobic moment, 0.6±0.1) and a potential to bind to membranes or other proteins (Boman index, 1.14±1.1). As expected, in general, the distribution of physicochemical properties of peptides from AMPSphere, DRAMP⁴⁶ version 3.0, and the positive training dataset used in Macrel⁴² are more similar to each other than to the negative training set (assumed to not be AMPs). Nonetheless, c_AMPs from AMPSPhere are on average longer (37±8 residues) than those in DRAMP⁴⁶ version 3.0 (28±22 residues) and we observed differences in the distribution of other features (e.g., charge, aliphaticity, amphipathicity, and isoelectric point, Fig. 8).

[0066] The quality of the smORF predictions was subsequently estimated, and 20% (172,840) of the c_AMP sequences were detected in independent publicly available metaproteomes or metatranscriptomes (Fig. 2 and 9A and Methods - Quality control of c AMPs) belonging to several habitats included in the AMPSphere, such as the human gut, plants, and others. All c_AMPs were then subejeted to a bundle of in silico quality tests (see topic Quality control of c AMPs). A subset of c_AMPs (9.2%, or 80,213 c_AMPs) passed all of them, and this subset is hereafter designated as high-quality. Testing with other AMP prediction systems (AMPScanner v2⁴⁹, the model for mature peptides in ampir⁴⁰, amPEPpy⁵⁰, APIN⁵¹, AI4AMP⁵², and AMPLify⁵³), it was observed that 98.4% (849,703 peptides) of AMPSphere c_AMPs were also predicted as AMPs by at least one other AMP prediction system. Approximately 15% (132,440 out of 863,498 peptides) of AMPSphere c_AMPs were co-predicted by all methods used.

[0067] Only 0.7% of the identified c AMPs (6,339 peptides) are homologous (operationally defined as amino acid identity > 75% and E-value < 10 ⁵) to experimentally validated AMP sequences in the DRAMP version 3.0⁴⁶. Moreover, most c_AMPs were also absent from protein databases not specific to AMPs (Fig. IB), such as the Small Proteins database (SmProt2)⁵⁴ or the Global Microbiome Gene Catalogue of canonical -length proteins (GMGCvl)⁵³, suggesting that c_AMPs represent an entirely novel region of peptide sequence space. In total, it was possible to find only 73,774 (8.5%) c_AMPs with homologs in any of the databases we considered. High-quality c AMPs were detected in public databases with higher frequency than general c_AMPs (2.5-fold, PHypergom. = 4.2- IO'²⁵⁰, Fig. IB), with 23,012 out of the 80,213 high-quality c_AMPs having a match in another database. However, it is notable that 76.4% (4,843 peptides out of 6,339) of those c_AMPs which have a homolog in DRAMP⁴⁶ version 3.0 (and, therefore, highly likely to be functional) are not high-quality c AMPs. Thus, while the quality tests did enrich for validated sequences, a failure to pass the tests is not a sufficient reason to conclude that the sequence is not active.

[0068] To put c AMPs in an evolutionary context, peptides were hierarchically clustered using a reduced amino acid alphabet of 8 letters⁵⁶. The three sequence clustering levels adopted identity cutoffs of 100%, 85%, and 75% (Fig. 10). At the 75% identity level, 521,760 protein clusters were obtained, of which 405,547 were singletons, corresponding to 47% of all c_AMPs from AMPSphere. A total of 78,481 (19.3%) of these singletons were detected in metatranscriptomes or metaproteomes from various sources, indicating that they were not artifacts. The large number of singletons suggests that most c_AMPs originated from processes other than diversification within families, which is the opposite of the hypothesized origin of full-length proteins, in which singleton families are rare⁵⁵. The 8,788 clusters with >8 peptides obtained at 75% of identity are hereafter named “families”, as in Sberro et al.³⁸. Among them, 6,499 were considered as high-quality families because they contained evidence of translation or transcription, or >75% of their sequences pass all in silica quality tests, regardless of whether experimental evidence is available (see Methods - AMP families). These high-quality families span 15.4% of the AMPSphere (133,309 peptides).

[0069] All the c_AMPs predicted can be accessed online,

The platform permits users to retrieve the peptide sequences, ORFs, and

predicted biochemical properties of each c_AMP (e.g., molecular weight, isoelectric point, and net charge at pH 7.0). The platform also provides the distribution across geographical regions, habitats, and microbial species for each c_AMP.

[0070] The AMPSphere spanned 72 different habitats, which were classified into eight high-level habitat groups, e.g.. soil/plant (36.6% of c_AMPs in AMPSphere), aquatic (24.8%), human gut (13%) - (Fig. 1A). Most of the habitats, except for the human gut, appeared to be far from saturated in terms of newly discovered c_AMPs (Fig. 1C). In fact, most AMPs were rare (median number of detections is 99, or 0.17% of the dataset; when restricted to high- quality c_AMPs, the median number of detections is 81, or 0.14% of the dataset), with 83.97% being observed in <1% of samples (Fig. 9). Only 10.8% (93,280) of c_AMPs were detected in more than one high-level habitat group (henceforth, “multi -habitat c_AMPs”); this fraction is 7.25-fold smaller than would be expected by a random assignment of habitats to samples (Ppermutation <1O'³⁰⁰, see infra). Even within high-level habitat groups, c_AMPs overlap between habitats much less frequently than expected by chance (2.4 to 192-fold less, Ppermutation<5.4 - IO'⁵⁰, see infra & Fig. ID).

[0071] Mutations in larger genes generate c AMPs as independent genomic entities. Many AMPs are generated post-translationally by the fragmentation of larger proteins¹⁷. For example, encrypted peptides (EPs) are computationally detected fragments from protein sequences within the human proteome and other proteomes, which have been shown to be highly active^4,10. EPs present diverse secondary structures and act on the membrane of bacterial cells, similar to known natural AMPs, but have different physicochemical features compared to known AMPs^4,33. AMP Sphere only considered peptides encoded by dedicated genes. Nonetheless, it was hypothesized that some of these have originated from larger proteins by fragmentation at the genomic level. To explore this, the AMPSphere c AMPs were aligned to the full-length proteins in GMGCvF⁵ and it was observed that about 7% (61,020) of them are homologous to a canonical -length protein (Fig. IB), with 27% of these hits sharing the start codon with the longer protein. This suggests early termination of full- length proteins as one mechanism for generating novel c AMPs (Fig. 3A and 3B).

[0072] To investigate the function of the full-length proteins homologous to AMPs, the matching proteins from GMGCvl⁵³ were mapped to orthologous groups (OGs) from eggNOG 5.0⁵⁷. Identified were 3,792 (out of 43,789) OGs significantly enriched (Puypergeom. <0.05, after multiple hypothesis corrections with the Holm-Sidak method) among the hits from AMPSphere. Although OGs of unknown function comprise 53.8% of all identified OGs, when considered individually, these OGs are, on average, smaller than OGs in other categories. Thus, despite each OG having a relatively small number of c_AMP hits, when compared to the background distribution of the OGs in GMGCvl⁵⁵, OGs of unknown function were the most enriched among the c_AMP hits, with an average enrichment of 10,857-fold (PMann <3.9- I O'⁴; Fig. 3C).

[0073] c_AMP genes may arise after gene duplication events. Next, the question was raised of whether c_AMPs would be predominantly present in specific genomic contexts. To investigate the functions of the neighboring genes of the c AMPs, they were mapped against 169,484 genomes included in a recent study⁵⁸. Atotal of 38.9% (21,465 out of 55,191) of c AMPs with more than two homologs in different genomes in the database showed phylogenetically conserved genomic context with genes of known function (see infra, Methods - Genomic context conservation analysis). This holds true for curated versions of the catalog: 35.32% high-quality c_AMPs and 32.06% high-quality c_AMPs with experimental evidence show conserved genomic neighbors. These conservation values were similar to that of 3,899,674 gene families with more than two homologs calculated de novo on the gene catalog (34.4%), indicating that the genomic location of c_AMPs is not random.

[0074] Despite being involved in similar processes, c_AMPs were generally depleted from conserved genomic contexts involving known systems of antibiotic synthesis and resistance, even when compared to small protein families (Fig. 4). Instead, it was found that c_AMPs are encoded in conserved genomic contexts with ribosomal genes (23.6%) at a higher frequency than other gene families (4.75%, Fig. 4A).

[0075] Most of the c_AMPs (2,201 out of the 2,642) in a conserved context with ribosomal subunits were homologous to ribosomal proteins (Fig. 4D), congruent with the observation that, in some species, ribosomal proteins have antimicrobial properties³⁹. Seventy-seven c_AMPs homologous to ribosomal proteins were also homologous to a ribosomal gene in their immediate vicinity (up to 1 gene up/downstream). This phenomenon is not exclusive to ribosomal proteins: 1,951 c AMPs can be annotated to the same KEGG Orthologous Group (KO) as some of their immediate neighbors and may have originated from gene duplication events. This shared annotation was interpreted, in this context, as evidence for a common evolutionary origin and not as a functional prediction for the c AMPs. These duplications may have arisen by recombination of flanking homologous sequences, which can happen during cell division^{60 62}. Interestingly, 1,635 (83.8%) of these c_AMPs were located upstream of the neighbor with the same KO annotation. Different permeases and transposases are the most common KOs assigned to c_AMPs and their neighbors (400 and 125 c_AMPs, respectively).

[0076] Most c AMP s are members of the accessory pangenome . It was observed that only a small portion (5.9%, Pp_ermutation = 4.8- 10'³, Nspecies = 416) of c_AMP families present in ProGenomes2⁴³ are contained in >95% of genomes from the same species (Fig. 5), here referred to as “core”⁶³. This is consistent with previous work, in which AMP production was observed to be strain-specific⁶⁴. In contrast, a high proportion (circa 68.8%) of full-length protein families are core in ProGenomes2⁴³ species. There is a 1.9-fold greater chance (Ppisher = 2.2- IO'⁹²) that a pair of genomes from the same species share at least one c_AMP when they belong to the same strain (99.5% < ANI < 99.99%).

[0077] One example of this strain-specific behavior is AMP 10.018 194, the only c_AMP found in Mycoplasma pneumoniae genomes. M. pneumoniae strains are traditionally classified into two groups based on their Pl adhesin gene⁶⁵. Of the 76 M. pneumoniae genomes present in our study, 29 were classified as type-1, 29 as type-2, and the remaining 18 were undetermined in this classification system⁶⁶ (see Methods - Determination of accessory AMPs). Twenty-six of the 29 type-2 genomes contain AMP10.018_194, as did 2 undetermined type genomes, but none of the type-1 genomes contain this AMP.

[0078] More transmissible species have lower c_AMP density. The taxonomic composition of AMP Sphere was investigated by annotating contigs with the Genome Taxonomy Database (GTDB) taxonomy⁶⁷⁶⁸ (see Methods - c_AMP density in microbial species from different habitats), which resulted in 570, 187 c_AMPs being annotated to a genus or species. The genera contributing the most c_AMPs to AMPSphere were Prevotella (18,593 c_AMPs), Bradyrhizobium (11,846 c_AMPs), Pelagibacter (6,675 c_AMPs), aecalibacterium (5,917 c_AMPs), and CAG-110 (5,254 c_AMPs) see Fig. 5). This distribution reflects the fact that these genera are among those that contribute the most assembled sequences in our dataset (all occupying percentiles above 99.75% among the assembled genera). Therefore, the c_AMP density (P^MP) ^was calculated by determining the number of c_AMP genes per megabase pairs of assembled sequence. To avoid bias due to the unequal sampling of habitats, all the sequences predicted by Macrel⁴² was included in each sample, including singleton sequences that were subsequently removed and are not part of AMPSphere.

[0079] To further explore the importance of AMP production in ecological processes, the role of AMPs in the mother-to-child transmissibility of bacterial species in a recently published paper⁶⁹ was investigated by correlating the p_AMP for each bacterial species to the published measures of microbial transmission. Human gut bacteria showed increased transmissibility at lower AMP densities (Rspeannan -0.42, Pnoim-sidak 3.4- 1 O'², Nspecies = 43). Similarly, in human oral microbiome bacterial species, transmissibility from mother to offspring was consistently inversely correlated with their p_AMP for the first year (Rspearman = - 0.55, Puoim-sidak = 1.4- 10’³, Nspecies = 41). This suggests that human gut bacteria and oral microbiome bacterial species show increased transmissibility at lower p_AMp- Moreover, it highlights the potential influence of p_AMp on the transmissibility of gut and oral microbiota, suggesting a link between AMPs and the transmission success rates of microbial species.

[0080] Physicochemical features and secondary structure of AMPs. To investigate the properties and structure of the synthesized peptides, a comparison was made of their amino acid composition to AMPs from available databases of experimentally-verified sequences (DRAMP⁴⁶ version 3.0, Database of Antimicrobial Activity and Structure of Peptides - DBAASP⁷⁰, and Antimicrobial Peptides Database - APD⁷¹ version 3). Overall, the composition was similar, as was expected, given that Macrel’s ML model was trained using known AMPs⁴². Notably, AMPSphere sequences displayed a slightly higher abundance of aliphatic amino acid residues, specifically alanine and valine. However, these AMPSphere sequences consistently differed (Fig. 6A) from EPs^4,10,33. The resemblances in amino acid composition between the identified c_AMPs and known AMPs suggested similar physicochemical characteristics and secondary structures, both of which are recognized for their influence on antimicrobial activity¹⁶. The c AMPs exhibited comparable hydrophobicity, net charge, and amphiphilicity to AMPs sourced from databases (Fig. 8). Furthermore, they displayed a slight propensity for disordered conformations (Fig. 6B) and had a lower net positive charge compared to other EPs (Fig. 6A).

[0081] To evaluate the structural and antimicrobial properties of c AMPs from AMPSphere, AMPSphere was first filtered for peptides that were predicted as suitable for in vitro assays, namely solubility in aqueous solution and ease of chemical synthesis. A set of high-quality AMPs with 50 peptide sequences was selected based on prevalence and taxonomic diversity (see Methods - Selection of peptides for synthesis and activity testing). Additionally, to provide an unbiased evaluation of the novel peptides described here, excluded first were any peptides with a homolog in one of the published databases and then randomly selected 50 additional peptides from the AMPSphere, including 25 peptides with AMP probability of at least 0.6 (as reported by Macrel⁴²) and 25 peptides with lower probabilities (0.5-0.6).

[0082] Subsequently, experimental assessments of the secondary structure of the active c AMPs were conducted using circular dichroism (Fig. 6B and 11). Similar to AMPs documented in databases, peptides derived from AMPSphere exhibited different propensities for adopting not only a-helical structures, but also some of them were unstructured or adopted P-antiparallel conformations in all media analyzed. Notably, they also displayed an unusually high content of P-antiparallel structure in both water and methanol/water mixtures (Fig. 6B), despite their amino acid composition similarities to AMPs and EPs. These findings were attributed to the slightly elevated occurrence of alanine and valine residues, which are known to favor P-like structures with a preference for P-antiparallel conformation⁷².

[0083] Methods.

[0084] Selection of metagenomes and high-quality microbial genomes. Selection of metagenomes and genomes to compose the AMPSphere was similar to that adopted by Coelho et al.^53,130. Public metagenomes available on 1 January 2020 produced with Illumina instruments (except for MiSeq, to ensure the consistency and reliability of the meta-analysis findings), with at least 2 million reads and, on average, 75 bp long, were downloaded from the European Nucleotide Archive (ENA). These samples met two criteria: (1) they were tagged with taxonomy ID 408169 (for metagenome) or were a descendent of it in the taxonomic tree; and/or (2) they came from experiments with the library source listed as “METAGENOMIC”. Samples were grouped by project and all projects with at least 20 samples were included for analysis. Additionally, metagenomes deposited by the Integrated Microbial Genomes System (IMG) missing from ENA were also included. Metadata was manually curated from each sample’s describing literature and Biosamples database¹²⁷. For habitat classification groups were created based on the similarity of habitat conditions, such as air, anthropogenic, aquatic, host-associated, ph:alkaline, sediment, terrestrial, and others. The sample origins and information related to host species were obtained using the NCBI taxonomic identification number. High-quality microbial genomes were selected from ProGenomes2 database⁴³.

[0085] Reads trimming and assembly. Reads were processed using NGLess⁹⁶, trimming positions with quality lower than 25 and discarding reads shorter than 60 bp post- trimming. Metagenomes obtained from a host-associated microbiome passed through a filtering of reads mapping to the host genome when available. Reads totaling more than 14.7 trillion base pairs of sequenced DNA were assembled with MEGAHIT 1.2.9¹¹² and the taxonomy of the 16,969,685,977 contigs generated was inferred as previously described¹³¹, using MMSeqs2⁹⁹ to map the sequences against the GTDB release 95^67,68. Mapped taxonomy lineages were then manually curated to conform to the International Code of Nomenclature of Prokaryotes^132,133.

[0086] smORF and AMP prediction. Analogously to Sberro et al.³⁸, a modified version of Prodigal³⁴ was used to predict smORFs (33 to 303 bp) from contigs. The 4,599,187,424 redundant smORFs, most of which (99.25%) originated in metagenomes, were then de-duplicated to optimize the computational resource usage, yielding 2,724,621,233 non- redundant smORFs. Macrel⁴² was run on the de-duplicated smORFs to predict c AMPs. Singleton sequences (those appearing in a single sample or genome) were eliminated, except when they had a significant match (amino acid identity > 75% and E-value < 10'⁵) to a sequence from the Data Repository of Antimicrobial Peptides (DRAMP)⁴⁶ version 3.0 using the ‘easy-search’ method from MMSeqs2⁹⁹. In total, AMPSphere encompassed 863,498 non- redundant predicted c_AMPs encoded by 5,518,294 redundant genes. AMP densities were estimated as the number of AMPs per assembled base pairs in a sample or a species.

[0087] AMP genes originating from ProGenomes2⁴³ had the taxonomy of the original genome assigned to them, whereas AMP genes from metagenomes were assigned the taxonomy predicted for the contig where they were found. Insights about potential structural conformations were obtained using the function secondary structure fraction from the ProtParam module implemented in the SeqUtils in Biopython¹⁰⁷. This function calculates the fraction of amino acids tend to assume conformations of helix [VIYFWL], turn [NPGS], and sheet [EMAL],

[0088] Clustering of AMP families. Clustering peptides by sequence identity is only possible at high identities as short low-/medium-identity matches are possible by chance. Therefore, aiming to recover matches where basic features are preserved even if individual amino acids are not identical^134,133, a reduced amino acids alphabet of 8 letters was used ⁵⁶ - [LVIMC], [AG], [ST], [FYW], [EDNQ], [KR], [P], [H], c AMPs were hierarchically clustered after alphabet reduction using three sequential identity cutoffs (100%, 85%, and 75%) with CD-Hit⁹⁸. A cluster was considered an AMP family when it consisted of at least 8 sequences³⁸. Representative sequences of peptide clusters were selected according to their length (taking the longest) with ties being broken by their alphabetical order.

[0089] To validate this clustering procedure, a sample of 3,000 sequences randomly sampled from AMPSphere was used, excluding cluster representatives. These sequences were aligned against the representative sequence of their cluster using the Smith -Waterman algorithm¹³⁶ with the BLOSUM 62 cost matrix, and gap open and extension penalties of -10 and -0.5, respectively. The alignment score was then converted to an E-value according to the model by Karlin and Altschul¹³⁷, which uses the values of K (0.132539) and (0.313667) constants adjusted to search for a short input sequence as implemented in the BLAST algorithm^120,138. Alignments were considered significant if their E-value was less than 10’⁵. We found that more than 95.3% of alignments produced in the first two levels (100% and >85% of identity) were significant, along with 77.1% of those from the third level (>75% of identity) - see Fig. 10.

[0090] Quality control of c AMPs. The c AMPs in AMPSphere were submitted to another six AMP prediction systems (AMPScanner v2⁴⁹, ampir⁴⁰ - with the model for mature peptides, amPEPpy⁵⁰, APIN’ ¹ - with their proposed model, AI4AMP⁵², and AMPLify⁵³).

[0091] The genes of c_AMPs were subjected to five different quality tests to reduce the likelihood that the observed peptides were artifacts or fragments of larger proteins. Initially, the peptides were searched against AntiFam v.7.0¹²³ using HMMSearch¹⁰⁹, which was designed to identify commonly recurring spuriously predicted ORFs, with the option cut_ga”. Fewer than 0.05% of c_AMPs had any significant hits.

[0092] For each smORF, a search as was performed for an in-frame stop codon upstream of its start codon. When no stop codon is found, we cannot rule out the possibility that the smORF is part of a larger gene which we cannot observe due to fragmented assembly. Most (68.4%) of the c_AMPs are encoded by at least one gene that is not terminally placed. However, the fact that a c_AMP is terminal does not imply that the given c_AMP is an artifact since the AMP genes are short enough to be recovered even in short contigs. For example, 72.9% (4,622/6,339) of homologs to DRAMP⁴⁶ version 3.0 were found as terminal c_AMPs in AMPSphere.

[0093] The RNAcode⁸⁴ program predicts protein-coding regions based on evolutionary signatures typical for protein genes. This analysis depends on a set of homologous and non-identical genes. Therefore, AMP clusters containing at least three gene variants were aligned. Given that an extensive portion of the AMPSphere candidates (53%; 459,910 out of 863,498) is not part of such a cluster, they could not be tested. Of the tested c_AMPs, 53% (215,421 out of 403,588) were considered genes with evolutionary traits of protein-coding sequences.

[0094] The inventors looked for evidence of transcription and/or translation using 221 publicly available metatranscriptomes, comprising human gut (142), peat (48), plant (13), and symbionts (17); and 109 publicly available metaproteomes from PRIDE¹²⁹ database comprising from 37 habitats. Using bwa v.0.7.17¹¹³, reads from the metatranscriptomes were mapped against non-redundant AMP genes, and, using NGLess⁹⁶, genes were selected with at least one read mapped across a minimum of two samples to increase our confidence. This approach is similar to that adopted when predicting AMPs⁴². Using regular expressions implemented in Python 3.8¹⁰⁰, k-mers of all AMPSphere peptides (with length equal to at least half the length of the sequence) were compared to peptide sequences in metaproteomics data. A perfect match between a k-mer and a metaproteomic peptide was considered additional evidence that this c_AMP is likely to be translated, as described by Ma et al.⁶. Briefly, the number of c_AMP peptides mapped against the set of metaproteomic samples was counted and those c_AMP peptides with at least one match covering more than 50% of the peptide were marked as detected. c AMPs with experimental evidence in metatranscriptomes and/or metaproteomes accounted for circa 20% of the AMPSphere.

[0095] The mapping of c_AMPs was performed without considering genomic context, which may have led to an overestimation of candidates being identified as potentially transcribed. For example, if they are homologous to longer proteins the presence of the longer gene may lead to a false positive detection of the shorter c AMP. We investigated this using Fisher’s Exact Test to compare the percentage of AMP homologs to the GMGCvl⁵³ database with experimental evidence of translation (3.4% - 2,073 out of 61,020 peptides, Odds-Ratio = 4.3, Ppisher's exact IO’³⁰⁰) and/or transcription (22.8% - 13,901 out of 61,020 peptides, Odds- Ratio = 1.2, Ppisher’s exact = 6.7’ 10‘¹⁰⁸). The results suggest that our approach tends to slightly overestimate the potential transcription and translation of candidates with canonical -length homologs.

[0096] Given that only a small number of transcriptomic or proteomics dataset were available and the afore-mentioned limitations in interpreting the mappings, we considered AMPs passing all quality-control tests to be high-quality, regardless of evidence of translation or transcription. We further separated those with experimental evidence of translation/transcription (17,115 c_AMPs, circa 2% of AMPSphere) and those without it (63,098 c_AMPs, circa 7%). For c_AMP families, we considered high-quality those where > 75% of its c_AMPs pass all quality control tests or those with at least one c_AMP possessing experimental evidence of translation/transcription.

[0097] Sample -based c AMPs accumulation curves. To determine the saturation of c_AMP discovery, for each habitat or group of habitats, sample-based accumulation curves were computed by randomly sampling metagenomes in steps of 10 metagenomes. This procedure was repeated 32 times, and the average was taken.

[0098] Multi-habitat and rare c AMPs. First were counted c_AMPs present in >2 habitats (“multi -habitat AMPs”). To then test the significance of this value, we opted for a similar approach to that described in Coelho et al.⁵⁵: habitat labels for each sample were shuffled 100 times and the number of resulting multi -habitat c AMPs was counted. Shuffling labels resulted in 676,489.7 ± 4,281 .8 multi-habitat c_AMPs by chance for high-level habitat groups, and in 685,477.17 ± 4,369.6 multi-habitat c_AMPs by chance when looking at the habitats individually inside the high-level groups. The Shapiro-Wilks test was used to check that the resulting data distribution is normal (P = 0.49, for specific habitats; P = 0.1 for high- level habitats). In the original (non-shuffled data), high-level habitat groups presented 93,280 multi-habitat c_AMPs (136.21 standard deviations below shuffled value), while specific habitats presented 173,955 multi-habitat c_AMPs (117.1 standard deviations below shuffled value).

[0099] To determine the rarity of c AMPs, the protocol previously established by Coelho et al.⁵⁵ was adapted in which the non-redundant genes in AMPSphere were mapped against the reads of metagenome samples using NGLess⁹⁶. Only uniquely mapped reads were considered. From the mapping, the c AMPs detected per sample and the number of detections per c_AMP were computed, considering “rare” c_AMPs as those detected less than the average of the entire AMPSphere (682 detections or 1% of all samples as previously described for species¹³⁹). This approach was adopted to overcome the high computational costs of a competitive mapping procedure. It was expected that this approach overestimates how prevalent c_AMPs are, and because of that, it is a robust way to estimate the rarity of c AMPs.

[0100] As the high-quality designation requires at least 3 gene variants for the RNAcode test to be performed, the rarest genes will not be high-quality. However, for robustness, this effect was quantified by computing the mean and median number of detections in only the high-quality c_AMPs and only non-terminal c_AMPs (a test which does not require a minimum number of genes). The mean number of detections is 682 for the full collection, 789 for high-quality c AMPs, and 679 for non-terminal ones.

[0101] Significance of the overlapping c AMPs across different habitats. Like was done when testing the significance of the number of multi-habitat c AMPs observed, the number of overlapping c AMPs was computed for each pair of habitats. The sample labels were shuffled 1,000 times, counting the number of randomly overlapping c_AMPs for each pair of habitats. Then, the probability was estimated of observing the overlap by Chebyshev's inequality, which does not rely on any assumption regarding the distribution of the data as we observed, using the Shapiro-Wilk's test, that the shuffled counts do not follow a normal

1 distribution. Chebyshev's inequality is p < — , where Z stands for the Z-score computed from the average and standard deviations estimated by the shuffling procedure. The p-values were adjusted using Holm-Sidak implemented in multipletests from the statsmodels package¹¹⁴, and those below 0.05 were considered significant.

[0102] c_AMP density in microbial species from different habitats. The c_AMP density was defined as P MP ^{= CA PS}, where n_CAMPs is the number of c_AMP redundant genes and L is the assembled base pairs. It was assumed, as an approximation, that in a large segment assembled, the start positions of AMP genes are independent and uniformly random. Then, the standard sample proportion error was calculated with the formula: ST Derr = The standard sample proportion error was used to calculate the margin of error at a

95% confidence interval (Z = 1.96, a = 0.05).

[0103] To gain insights about the contributions of different phyla, species, and genera to the AMPSphere, the c_AMP density was calculated for these taxonomy levels using the c_AMPs included within AMPSphere, summing all assembled base pairs for contigs assigned to each taxonomy level in the samples used in AMPSphere. The PAMP of genera, phyla and species within a margin of error superior to 10% of the calculated value were eliminated along with outliers according to Tukey’s fences (k = 1.5). We estimated species’ presence and abundance in each sample using mOTUs2¹¹⁵. None of the genera with the highest PAMP (Algorimicrobium, TMED78, SFJ001, STGJ01, and C AG-462) were highly prevalent microbes.

[0104] c AMPs and bacterial species transmissibility. The species taxonomy and transmissibility indices calculated by Valles-Colomer et al.⁶⁹ was used to demonstrate the effect of AMPs on the transmission of bacterial species from mother to children. Only those species overlapping AMPSphere and the datasets from Valles-Colomer et al.⁶⁹ were used for this analysis, and their AMP densities were calculated as described in the previous section (c_AMP density in microbial species from different habitats), using all the predicted c_AMPs from metagenomes and genomes we obtained, also including those not in AMPSphere, to avoid the bias of sampling. The AMP density and the coefficient of transmissibility were correlated using Spearman's method implemented in the scipy package¹⁰⁴: following children's microbiome after 1, 3, and up to 18 years, as well as, cohabitation and intradatasets. The p-values of correlations were corrected using Holm-Sidak implemented in the multipletests function from the statsmodels package¹¹⁴.

[0105] Determination of accessory AMPs. To uncover the prevalence of c AMPs through the microbial pangenomes, core, shell, and accessory c_AMP clusters were determined using the subset of c AMPs obtained from ProGenomes2⁴³ because of their high- confidence assigned taxonomies and genomically-defined species (specl¹⁴⁰). To increase confidence in our measures, only species containing at least 10 genomes were used in this analysis. c_AMPs and AMP families present in fewer than 50% of the genomes from a microbial species were classified as accessory. c_AMPs and families present in 50% - 95% of the genomes in the cluster were classified as shell¹⁴¹, and those present in >95% of the genomes were classified as core genes⁶³.

[0106] To determine the propensity of AMPs being shared between genomes belonging to the same strain, strains within species were defined first. For this, FastANI v.1.33¹¹¹ was used to cluster genomes from the same species in ProGenomes2⁴³. Genome groups with ANI > 99.99% were considered clonal complexes and only a single representative of each clonal complex was kept for further analyses. Species that had fewer than 10 genomes after this step were not considered further in this analysis. Next, strains (99.5% < ANI < 99.99%) were inferred as in Rodriguez et al.¹⁴². The pairs of genomes were counted from the same species sharing AMPs, stratified by whether the pair originates from the same strain or not, and tested the results with Fisher’s Exact Test implemented in the scipy package¹⁰⁴.

[0107] To determine the proportions of accessory, shell and core full-length proteins in the microbial pangenomes, also extracted were the predicted full-length proteins from the ENA database for each genome and hierarchically clustered them after alphabet reduction in a similar fashion to that described in the topic “AMP families'". Full-length protein clusters with >8 sequences for each species were kept. The prevalence of full-length protein families within a species was computed as above and the number of core families was compared to the number of c_AMP core families using the probability, calculated as number of species with proportion of core full-length protein families less or equal to that observed for c AMPs divided by the total of assessed species.

[0108] To determine the genotype of Mycoplasma pneumoniae genomes in ProGenomes2⁴³, extracted were the gene coding for Pl adhesin⁶⁵ by mapping the reference gene sequence NZ_LR214945.1 :c568695-567307 against each genome with bwa v.0.7.17^113, and later extracted the sequences using with SAMtools¹¹⁶ and BEDtools¹¹⁷. The extracted gene sequences were aligned using Clustal Omega¹¹⁸, and a phylogenetic tree was built using the aligned nucleotide sequences and FastTree 2¹¹⁰ with the restricted time-reversible substitution model and a bootstrapping procedure with 1,000 pseudo-replicates to determine node support. The tree was used to segregate and classify genomes taking the strain type of reference genomes from Diaz et al.⁶⁶. [0109] Annotation of AMP s using different datasets. To detect homologs to previously published proteins, AMPSphere candidates were aligned against several databases: (i) the small protein sets in SmProt 2⁵⁴, (ii) the bioactive peptides database starPepDB 45k⁹³, (iii) the small proteins from the global data-driven census of Salmonella⁹², (iv) the global microbial gene catalog GMGCvl ^?5, (v) and the AMP database DRAMP⁴⁶ version 3.0. To strictly avoid any artifacts of assembly for the analysis, only c_AMPs which passed the terminal placement test (i.e., for which there was strong evidence that the ORF is indeed complete) were searched against the GMGCvl⁵⁵. The AMPs were annotated using MMseqs2⁹⁹ with the ‘easy-search’ method, retaining hits with an E-value up to 10'⁵. As Macrel⁴² removes the starting methionine from the peptides it outputs, hits starting at the second amino acid were treated as if they matched the first one.

[0110] The hypergeometric test implemented in the scipy package¹⁰⁴ was used to model the association between c AMPs and the background distribution of ortholog groups from GMGCvl⁵⁵. The number of genes that were redundant in GMGCvl ⁵⁵ for each ortholog group was computed along with the counts for ortholog groups in the top hits to AMPSphere. The enrichment was given as the proportion of hits present in a given ortholog group divided by the proportion of that ortholog group among the redundant sequences in GMGCvl⁵⁵, and results were considered significant if p < 0.05 after correction with the Holm-Sidak method implemented in multipletests from the statsmodels package¹¹⁴. When using a robust approach that filters the ortholog groups by the number of c_AMP hits and GMGCvl⁵⁵ hits associated with them, using a minimum of 10, 20, or even 100 proteins, the results were kept similar to those obtained with all data showing that the extension of the ortholog groups in AMPSphere did not affect the enrichment analysis.

[OHl] To check for genomic entities generated after gene truncation, a screen was performed for c_AMP homologs using the default settings for Blastn¹²⁰ against the NCBI database¹²⁴, keeping only significant hits with a maximum E-value of 10‘⁵. As a case study, the AMP10.271_016 was selected, which was predicted to be produced by Prevotella jejuni, which shares the start codon with the gene coding for a NAD(P)-dependent dehydrogenase (WP_089365220.1). To verify the gene disposition and putative mutations leading to the AMP creation, we used Biopython¹⁰⁷ to codon-align the fragments from metagenomic contigs assembled from samples SAMN09837386, SAMN09837387, and SAMN09837388, and genomic fragments of different strains of Prevotella jejuni CD3 33 (CP023864.1 :504836- 504949), F0106 (CP072366.E781389-781502), F0697 (CP072364.1 : 1466323-1466436), and from Prevotella melaninogenica strains FDAARGOS_760 (CP054010.1 : 157726-157839), FDAARGOS 306 (CP022041.2:943522-943635), FDAARGOS 1566

(CP085943.1 :1102942- 1103055), and ATCC 25845 (CP002123.1 :409656-409769) and compared the segments coding for the AMP and the original full-length protein.

[0112] Genomic context conservation analysis. To gain insights into the gene synteny involving AMP genes, the 863,498 AMP sequences were mapped against a collection of 169,632 reference genomes, metagenome-assembled genomes (MAGs) and single amplified genomes (SAGs) curated elsewhere⁵⁸ with DIAMOND¹¹⁹ in “blastp” mode, as previously reported⁵⁸. Hits with identity > 50% (amino acid) and query and target coverage > 90% were considered significant. The target coverage threshold avoids hits to larger homologs whose function may be unrelated. This yielded 107,308 AMPs with homologs in at least one genome. We built gene families from the hits of each AMP detected in the prokaryotic genomes and calculated a conservation score based on the functional annotation of the neighboring genes in a window of three genes up and downstream. The vertical conservation score at each position within the window of each c_AMP was calculated as the number of genes with a given functional annotation (ortholog group, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, KEGG orthology, KEGG module¹²⁶, PFAM 33.1¹²²’¹⁴³, and CARD¹²⁵; details of annotation and annotated database described previously⁵⁸), divided by the number of genes in the family. AMPs with more than two hits and a vertical conservation score > 0.9 with any functional term were considered to have conserved genomic contexts. Figure 4 shows genomic context conservation of different KEGG pathways.

[0113] For testing whether the fraction of AMPs with conserved genomic neighbors is similar to that of other gene families within the 169,632 genomes curated by del Rio et al.⁵⁸, genomic context conservation on 3,899,674 gene families were calculated de novo with MMSeqs2⁹⁹ (using a minimal amino acid identity of 30%, coverage of the shorter sequence of at least 50%, and maximum E-value of 10'³). The c_AMPs were also annotated using EggNOG-mapper v2¹⁰⁸. Their KO annotations were compared to that of the immediate neighbors (+/- 1 positions) to identify neighborhoods with the same function. It was possible to annotate 56.1% (60,173 out of 107,308) of c_AMPs with hits to the genomes tested using the EggNOG5 database⁵⁷. Of these, 18.1% were assigned to translation-related functions (class J), 14.4% belong to proteins of unknown function (S), 9% were assigned to replication, recombination, and repair (L).

[0114] AMPSphere web resource. AMPSphere is found at the address

The implementation is based on Python¹⁰⁰ and Vue Javascript. The database was built with sqlite, and SQLalchemy was used to map the database to Python objects. Internal and external APIs were built using FastAPI and Gunicom to serve them. On the front end, Vue 3 was used as the backbone and Quasar built the layout. Plotly was used to generate interactive visualization plots, and Axios to render content seamlessly. LogoJS

wgttglabd ^ PiO ^{was use}^ to generate sequence logos for AMP families; while the helical wheel app was used to

generate AMP helical wheels.

Example 2 - Synthesis and Efficacy Testing of Candidate AMPs

[0115] One-hundred synthesized peptides were tested against 11 clinically relevant pathogenic strains, encompassing Acinetobacter baumannii, Escherichia coli (including one colistin-resistant strain), Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus (including one methicillin-resistant strain), vancomycin-resistant Enterococcus faecalis, and vancomycin-resistant Enterococcus faecium. The initial screening revealed that 63 AMPs (out of 100 synthesized) completely eradicated the growth of at least one of the pathogens tested (Fig. 6C). Remarkably, in some cases, the AMPs were active at concentrations as low as 1 pmol L'¹, close to the peptide antibiotic polymyxin B and the antibiotic levofloxacin, used as positive controls in all experiments (Fig. 11 A). The Gramnegative bacteria A. baumannii and E. coli, as well as the Gram -positive strains vancomycin- resistant E. faecalis and E. faecium, displayed higher susceptibility to the AMPs, with 39, 24, 21 and 26 peptide hits, respectively. However, none of the tested AMPs affected methicillin- resistant S. aureus (MRSA) (Fig. 6C). Also synthesized and tested for antimicrobial activity were the scrambled versions of five of the most active peptides from the high-quality group (i.e., actinomycin- 1, enterococcin-1, lachnosporin-1, proteobacticin-1, and synechocucin-1). All scrambled versions were inactive except for lachnospirin-l_scrambled, which presented modest activity against baumannii at 32 pmol L'¹ (16-times higher concentration compared to its parent peptide lachnospirin-1) (Fig. 12A). These results underscore the importance of the specific sequence of these peptides to exert their antimicrobial activity. To further explore the influence of sequence on structure, the secondary structure tendency of the scrambled peptides was assessed using circular dichroism. A decrease in helical fraction was detected for sequences with higher helical content (enterococcin-1, lachnospirin-1, and synechocucin- 1), while the predominately random coiled sequences actinomycin- 1 and proteob actin- 1, as well as their scrambled counterparts, showed similar secondary structural sequences in all media analyzed (Fig. 12B-E). These results suggest a lack of correlation between secondary structure and antimicrobial activity of the AMPs derived from AMPSphere.

[0116] The growth of human gut commensals is impaired by c AMPs. The AMPs were screened against eight of the most relevant members of the human gut microbiota associated with human health^{73 77} Tested were commensal bacteria belonging to four phyla (Verrucomicrobiota, Bacteroidota, Actinomycetota, and Bacillota), i.e., Akkermansia muciniphila, Bacteroides fragilis, Bacteroides thetaiotaomicron , Bacteroides unifornris, Phocaeicola vulgatus (formerly Bacteroides vulgatus), Collinsella aerofaciens, Clostridium scindens, and Parabacteroides distasonis.

[0117] While it is commonly observed that known natural AMPs do not target microbiome strains⁷⁸, the present study found that 58 of the synthesized AMPs (58%) demonstrated inhibitory effects on at least one commensal strain at low concentrations (8-16 pmol L'¹). Although this concentration range was higher than that required to inhibit pathogens (1-4 pmol L^-1), it still falls within the highly active range of AMPs based on previous studies^{79 81} (Fig. 6C). Interestingly, all the analyzed gut microbiome strains were susceptible to at least four c AMPs, with strains of A. muciniphila, B. uniformis, P vulgatus, C. aerofaciens, C. scindens, and P distasonis exhibiting the highest susceptibility. In total, 79 AMPs (out of 100 synthesized peptides) demonstrated antimicrobial activity against pathogens and/or commensals. The AMPs were also screened against gut commensals five scrambled sequences of five of the highly active peptides from the high-quality group. Similarly to the results obtained against pathogenic strains (Fig. 12), only lachnospirin- l_scrambled was modestly active against C. scindens at 64 pmol L'¹, as lachnospirin-1 (Fig. 12A).

[0118] Permeabilization and depolarization of the bacterial membrane by c AMPs from AMPSphere. To gain insights into the mechanism of action responsible for the antimicrobial activity observed in the peptides derived from AMPSphere (Fig. 6C), experiments were conducted to assess their ability to permeabilize and depolarize the outer and cytoplasmic membranes of bacteria at their Minimum Inhibitory Concentrations (MICs). Specifically, the effects of all 39 peptides that showed activity against A. baumannii (Figs. 6D-E and Fig. 13 A, 13D) and 6 peptides with antimicrobial activity on P aeruginosa (Figs. 13B-C,E) were investigated. For comparison and as a control, polymyxin B, a peptide antibiotic known for its membrane permeabilization and depolarization properties⁴, was used.

[0119] To investigate the potential permeabilization of the outer membranes of Gramnegative bacteria by the selected AMPs, 1 -(N-phenylamino)naphthalene (NPN) uptake assays were performed. NPN is a lipophilic fluorophore that exhibits increased fluorescence in the presence of lipids found within bacterial outer membranes. The uptake of NPN indicates membrane permeabilization and damage. Among the 39 peptides evaluated for activity against A baumannii, 10 peptides caused significant permeabilization of the outer membrane, resulting in fluorescence levels at least 50% higher than that of polymyxin B (Fig. 6D) after 45 min of exposure. In the case of P. aeruginosa cells, four out of the six peptides tested showed higher permeabilization than polymyxin B (Fig. 13 A).

[0120] To evaluate the potential membrane depolarization effect of the selected AMPs from AMPSphere, the fluorescent dye 3,3 '-dipropylthiadi carbocyanine iodide [DiSCa-(5)] was utilized, v Among the peptides tested against baumannii, bogicin-1 (AMP10.364_543), ampspherin-2 (AMP10.615_023), and marinobacticin-1 (AMP10.321_460) exhibited greater cytoplasmic membrane depolarization than polymyxin B, and among the ones tested against P aeruginosa, cagicin-2 (AMP10.014_861) exhibited greater cytoplasmic membrane depolarization than polymyxin B (Fig. 6E). vlnterestingly, all the tested AMPSphere peptides displayed a characteristic crescent-shaped depolarization pattern compared to polymyxin B, with lower levels of depolarization observed during the first 20 min of exposure, followed by an increase in depolarization over time (Fig. 6E; Fig. 13B). Taken together, these results indicate that the kinetics of cytoplasmic membrane depolarization are slower compared to the kinetics of outer membrane permeabilization, which occurs rapidly upon interaction with the bacterial cells.

[0121] These findings indicate that the tested AMPs from AMPSphere primarily exert their effects by permeabilizing the outer membrane rather than depolarizing the cytoplasmic membrane, revealing a similar mechanism of action to that observed for classical AMPs and EPs from the human proteome⁴

[0122] AMPs exhibit anti-infective efficacy in a mouse model. Next, tested was the anti-infective efficacy of AMPSphere-derived peptides in a skin abscess murine infection model (Fig. 7A). Mice were subjected to infection with A. baumannii, a dangerous Gramnegative pathogen known for causing severe infections in various body sites including the bloodstream, lungs, urinary tract, and wounds⁸². Ten lead AMPs from different sources displayed potent in vitro activity against A. baumannii'. synechocucin-1 (AMP10.000_211, 8 prnol L^-1) from Synechococcus sp. (coral associated, marine microbiome), proteobacticin-1 (AMP10.048_551, 16 prnol L'¹) from Pseudomonadota (plant and soil microbiome), actynomycin-1 (AMP10.199_072, 64 prnol L'¹) from Actinomyces (human mouth and saliva microbiome), lachnospirin-1 (AMP10.015_742, 2 prnol L'¹) from Lachnospira sp. (human gut microbiome), enterococcin-1 (AMP10.051_911, 1 prnol L’¹) from Enterococcus faecalis (human gut microbiome), alphaprotecin- 1 (AMP10.316 798, 1 pmol L'¹) from Alphaproteobacteria (aquatic microbiome), oscillospirin (AMP10.771_988, 8 pmol L ¹) from Oscillospiraceae (pig gut microbiome), ampspherin-4 (AMP10.466_287, 8 pmol L ¹) from an unknown source, methylocellin-1 (AMP10.446_571, 2 pmol L'¹) from Methylocella sp. (soil microbiome), and reyranin-1 (AMP10.337_875, 16 pmol L'¹) from Reyranella (plant and soil microbiome). The skin abscess infection was established with a bacterial load of 20 pL of A. baumannii cells at 10⁶ CFU mL'¹ onto the wounded area of the dorsal epidermis (Fig. 7A). A single dose of each peptide, at their respective MIC value obtained in vitro (Fig. 6C; Fig. 11 A), was administered to the infected area. Two days post-infection, synechocucin-1, actynomycin-1, and oscillosporin-1 presented bacteriostatic activity, inhibiting the proliferation of baumannii cells, whereas lachnospirin-1, enterococcin-1, ampspherin-4, and reyranin-1 presented bactericidal activity close to that of the antibiotic polymyxin B (at 5 pmol L’¹), reducing the colony-forming units (CFU) counts by to 3-4 orders of magnitude (Fig. 7B). Four days post-infection, synechocucin-1, lachnospirin-1, enterococcin- 1 , and ampspherin-4 presented a bacteriostatic effect close to that of the antibiotic polymyxin B, reducing the CFU counts by 2-3 orders of magnitude compared to the untreated control (Fig. 13C). These results highlight the anti -infective potential of the tested peptides from AMPSphere as they were administered at a single time immediately after the establishment of the abscess. Mouse weight was monitored as a proxy for toxicity and no significant changes were observed (Fig. 7C and Fig. 13D), suggesting that the peptides tested were not toxic.

[0123] Conclusions. Here, ML was to identify nearly a million novel candidate AMPs in the global microbiome. Building on previous studies that focused specifically on the human gut microbiome⁶'³⁸'⁸³, AMPs were catalogued from the global microbiome across 63,410 publicly available metagenomes, as well as 87,920 high-quality microbial genomes from the ProGenomes2 database⁴². This led to the creation of AMPSphere, an open-access and publicly available resource encompassing 863,498 non-redundant peptides and 6,499 high-quality AMP families from 72 different habitats, including marine and soil environments and the human gut. Most of the c_AMPs (91.5%) were previously unknown, lacking detectable homologs in other databases, and about one in five had evidence of translation and/or transcription as they could be detected in independent publicly available sets of metatranscriptomes or metaproteomes.

[0124] A set of tests was designed to capture higher-quality predictions, but many peptides failed these tests despite evidence that they were active, including our own in vitro data and existence of validated homologs in external databases. Low prevalence peptides will be less likely to pass the tests (RNAcode⁸⁴ requires multiple variants), which is independent of their activity and influenced by sampling biases.

[0125] Focusing on candidate AMPs that are directly encoded in the genome enabled in vitro and in vivo testing using chemical synthesis without post -translational modifications, but there are other processes that generate active peptides, such as encrypted peptides (EPs)⁴, which were used as a comparison point. Notably, the amino acid composition and physicochemical characteristics of the validated AMPs from AMPSphere differed from those of recently identified in EPs⁴. Two evolutionary mechanisms by which AMPs may be generated were explored. First, mutations in genes encoding longer proteins could generate gene fragments via truncation. Among the enriched ortholog groups of proteins from GMGCvl⁵⁵homologous to c_AMPs, we observed that a majority of groups had unknown function (53.8%), similar to what was reported by Sberro et al.³⁸ for small proteins from the human gut microbiome. The second mechanism is that a small protein gene undergoes a duplication followed by mutation, which we observed in the case of ribosomal proteins. Ribosomal proteins can harbor antimicrobial activity⁶³, possibly due to their amyloidogenic properties⁸⁵. Other origins of AMPs still may be the horizontal gene transfer⁸⁶ or the ancestral non-coding sequences⁸⁷.

[0126] Nonetheless, the majority of identified AMPs did not have a detectable homolog in other databases, highlighting their novelty. The lack of observed homology may be due to limitations in our ability to robustly detect these homology relationships in small sequences, but there is also the possibility that small proteins, such as AMPs, may be more likely to be generated de novo and may have repeatedly evolved in various taxa⁸⁸. This may also be an explanation for the large fraction of c AMPs in the AMPSphere that do not cluster with any other sequences.

[0127] It was observed that c_AMPs from AMPSphere were habitat-specific and mostly accessory members of microbial pangenomes. Furthermore, four out of the five genera with the most c_AMPs present in AMPSphere share a host-associated lifestyle, and three of these (Prevotella, Faecalibacterium , and CAG-llff) are common in animal hosts^{89 91} (Fig. 5).

[0128] Valles-Colomer et al.⁶⁹, who recently analyzed a large collection of human- associated metagenomes, provide a species-specific index of transmissibility for the several transmission scenarios they study (e.g., mother to infant). Hypothesizing that AMP production may be related to transmission, the species-specific p_AMP calculated in AMPSphere was correlated with transmission scores. In both the human gut and oral microbiomes, species with higher p_AMP are less transmissible, possibly because AMPs confer protection against strain replacement. Taken together, these results validate the applicability of AMPSphere in the study of microbial ecology as they suggest a role for AMPs in determining the transmissibility and colonization ability of microbes. [0129] Finally, the present inventors experimentally validated predictions made by the inventive ML model⁴² and found that 79 (out of 100) synthesized AMPs displayed antimicrobial activity against either pathogens or commensals. Nonetheless, notably, four peptides (cagicin-1, cagicin-4, and enterococcin-1 against baumannii, and cagicin-1 and lachnospirin-1 against vancomycin-resistant E.faecium) presented MIC values as low as 1 pmol L'¹, comparable to the MICs of some of the most potent peptides previously described in the literature^80,81.

[0130] It was herein demonstrated that the tested AMPs from AMPSphere tended to target clinically relevant Gram-negative pathogens and showed activity against vancomycin- resistant E.faecium. Although conventional AMPs do not target bacteria from the human gut microbiome⁷⁸, tested AMPs from AMPSphere showed efficacy against commensal bacteria, suggesting potential ecological implications of peptides as protective agents for their producing organisms and to reconfigure microbiome communities.

[0131] When assessing their activity in vivo, three peptides exhibited anti -infective efficacy in a murine infection model, with lachnospirin-1 and enterococcin-1 being the most potent, resulting in a reduction of bacterial load by up to four orders of magnitude. The active peptides included those derived from both human-associated and environmental microbiota, validating the present approach of investigating the global microbiome. Overall, the present findings unveil a wide array of novel AMP sequences, highlighting the potential of machine learning in the discovery of much-needed antimicrobials.

[0132] Methods

[0133] Selection of peptides for synthesis and activity testing. Two groups of peptides were selected: (i) 50 peptides that were selected as being particularly likely to be active and that were otherwise interesting (as described below), (ii) 50 peptides selected randomly after applying technical exclusions.

[0134] For the first group, only high-quality (see topic “Quality control of c AMPs”) c_AMPs were considered for synthesis. They were further filtered according to six criteria for solubility¹⁴⁴ and three criteria for synthesis, as in PepFun¹⁴⁵. The solubility was estimated using the criteria implemented in PepFun¹⁴⁵, observing that 67.4% (581,749 peptides) passed at least half of the solubility criteria evaluated. The subset that is homologous to peptides in DRAMP⁴⁶ version 3.0 had a slightly lower rate, 44.3% passed half the tests. We then assessed the peptides regarding their ease of synthesis, however, only 21.2% from AMPSphere passed at least 2 out of the 3 criteria established for chemical synthesis.

[0135] A peptide approved for at least six of the above-mentioned criteria was then filtered by predicting AMP activity with six methods in addition to Macrel⁴²: AMPScanner v2⁴⁹, the mature peptides model in ampir⁴⁰, amPEPpy⁵⁰, APIN⁵¹ - with their proposed model, AI4AMP⁵², and AMPLify³³. Peptides predicted to be AMPs by all methods were filtered by length, discarding sequences longer than 40 amino acid residues, for which conventional solid-phase peptide synthesis using Fmoc strategy has lower yields and many recoupling reactions^{146 l4S}. Only one peptide was kept from each family or cluster, namely the one with the highest number of observed smORFs. After this process, we obtained 364 candidate AMPs, belonging to 166 families and 198 clusters with < 8 c AMPs. Of these, 30 candidates were homologous to sequences from the databases used in annotation (e.g., SmProt 2³⁴). To compose the list of 50 high-likelihood candidates: (i) we selected 34 of the most prevalent peptides; (ii) we randomly selected 14 c_AMPs (30% of our set) with homologs to the GMGCvl⁵⁵ and one that matched SmProt 2⁵⁴; and (iii) we included one peptide that was found in the MAGs binned from stool samples used to investigate fecal transplantations¹⁴⁹. We also included scrambled sequences made using five of the most active peptide sequences to verify the potency of randomly generated sequences.

[0136] To build the group of randomly selected peptides, first selected were c AMPs that are not homologous to any other databases tested and that passed the abovementioned synthesis criteria (total of 768,061 out of 863,498 peptides). This group was further divided into subgroups: (i) those with Macrel -assigned probability >0.6 (271,555 c_AMPs) and (ii) those in the range 0.5-0.6 (496,506 c_AMPs; note that all c_AMPs in AMPSphere have a Macrel -assigned probability >0.5). Twenty-five peptides were randomly selected from each group.

[0137] Minimal inhibitory concentration (MIC) determination. The 100 AMPs were tested for antimicrobial activity using the broth microdilution method¹⁵⁰. MIC values were considered as the concentration of the peptides that killed 100% of cells after 24 h of incubation at 37°C. First, peptides diluted in water were added to untreated flat -bottom polystyrene microtiter 96-well plates in two-fold dilutions ranging from 64 to 1 pmol L’¹, and then peptides were exposed to an inoculum of 2- 10⁶ cells in LB or BHI broth, for pathogens and gut commensals, respectively. After the incubation time, the absorbance of each well representing each of the conditions was analyzed using a spectrophotometer at 600 nm. The assays were conducted in three biological replicates to ensure statistical reliability.

[0138] Circular dichroism assays. Circular dichroism experiments were conducted using a J1500 circular dichroism spectropolarimeter (Jasco). The experiments were carried out at a temperature of 25°C. Circular dichroism spectra were obtained by averaging three accumulations using a quartz cuvette with an optical path length of 1.0 mm. The spectra were recorded in the wavelength range from 260 to 190 nm at a scanning rate of 50 nm min’¹ with a bandwidth of 0.5 nm. The peptides were tested at a concentration of 50 pmol L’¹. Measurements were performed in water, a mixture of water and trifluoroethanol (TFE) in a ratio of 3 :2, and a mixture of water and methanol in a ratio of 1 : 1. Baseline measurements were recorded prior to each measurement. To minimize background effects, a Fourier transform filter was applied. The helical fraction values were calculated using the single spectra analysis tool available on the BeStSel server⁹⁵.

[0139] Outer membrane permeabilization assays. Membrane permeability was analyzed using the l-(N-phenylamino)naphthalene (NPN) uptake assay. NPN demonstrates weak fluorescence in an extracellular environment but displays strong fluorescence when in contact with lipids from the bacterial outer membrane. Thus, NPN will show increased fluorescence when the integrity of the outer membrane is compromised. A. baumannii ATCC 19606 and P. aeruginosa PA01 were cultured until cell numbers reached an ODeoo of 0.4, followed by centrifugation (10,000 rpm at 4°C for 3 min), washing, and resuspension in buffer (5 mmol L'¹ HEPES, 5 mmol L'¹ glucose, pH 7.4). Subsequently, 4 pL of NPN solution (working concentration of 0.5 mmol L'¹) was added to 100 pL of bacterial solution in a white flat bottom 96-well plate. The fluorescence was monitored at Ax = 350 nm and X_em = 420 nm. The peptide solutions in water (100 pL solution at their MIC values) were introduced into each well, and fluorescence was monitored as a function of time until no further increase in fluorescence was observed (30 min). The relative fluorescence was calculated using a nonlinear fit. The positive control (antibiotic polymyxin B) was used as baseline. The following equation was applied to reflect % of difference between the baseline (polymyxin B) and the sample:

100 x

Relativefluorescence =

CCTlCCp_Oly_my_Xing

[0140] Cytoplasmic membrane depolarization assays. The ability of the peptides to depolarize the cytoplasmic membrane was assessed by measuring the fluorescence of the membrane potential-sensitive dye 3,3’-dipropylthiadicarbocyanine iodide [DiSC₃-(5)]. This potentiometric fluorophore fluoresces upon release from the interior of the cytoplasmic membrane in response to an imbalance of its transmembrane potential. A. baumannii ATCC 19606 and P. aeruginosa PA01 cells were grown with agitation at 37°C until they reached mid-log phase (ODeoo = 0.5). The cells were then centrifuged and washed twice with washing buffer (20 mmol L’¹ glucose, 5 mmol L’¹ HEPES, pH 7.2) and re-suspended to an ODeoo of 0.05 in 20 mmol L’¹ glucose, 5 mmol L’¹ HEPES, 0.1 mol L’¹ KC1, pH 7.2. An aliquot of 100 pL of bacterial cells was added to a black flat bottom 96-well plate and incubated with 20 nmol L’¹ of DiSC₃-(5) for 15 min until the fluorescence stabilized, indicating the incorporation of the dye into the cytoplasmic membrane. The membrane depolarization was monitored by observing the change in the fluorescence emission intensity of the dye ( _ex = 622 nm, A_m = 670 nm), after the addition of the peptides (100 pL solution at their MIC values). The relative fluorescence was calculated using a non-linear fit. The positive control (antibiotic polymyxin B) was used as baseline. The percentage of difference between the baseline (polymyxin B) and the sample was estimated using the same mathematical approach as in the ''Outer membrane permeabilization assays ' .

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Claims

What is claimed:

1. A method for forming a database-assisted platform providing one or more functional and/or physicochemical features of respective metagenomic -derived candidate antimicrobial peptides (AMPs), comprising: selecting one or more genomes or metagenomes for inclusion in the platform; using an NGS assembler to assemble reads in order to identify contigs from the genomes or metagenomes; from the identified contigs, predicting small open reading frames (smORFs); removing duplicate smORFs to yield non-redundant smORFs; and, predicting candidate AMPs from the non-redundant smORFs.

2. The method according to claim 1, wherein selection of the one or more genomes or metagenomes is according to criteria (i) whereby the genome or metagenome is tagged with taxonomy ID 408169 (for metagenome) or is a descendent of it in a taxonomic tree, (ii) whereby experiments with the genome or metagenome are listed as “METAGENOMIC”, or both (i) and (ii).

3. The method according to claim 1, wherein metadata is curated from the one or more genomes or metagenomes to create groups based on similarity of habitat conditions.

4. The method according to claim 3, wherein the habitat conditions include one or more of air, anthropogenic, aquatic, host-associated, alkaline pH, sediment, or terrestrial.

5. The method according to any preceding claim, wherein the selection of the one or more genomes or metagenomes comprises assessing sample origin or other information relating to host species using an NCBI taxonomic identification number.

6. The method according to any preceding claim, further comprising processing the assembled reads by trimming positions with a quality lower than a desired number, such as about 25, and discarding reads shorter than a specified number of base pairs, such as about 50-100, preferably about 60 base pairs, post trimming.

7. The method according to any preceding claim, wherein the NGS assembler is optimized for metagenomes.

8. The method according to any preceding claim, wherein the smORFs are predicted from the identified contigs using prokaryotic gene recognition and translation initiation site identification.

9. The method according to any preceding claim, wherein the candidate AMPs are predicted from the non-redundant smORFs using metagenomic AMP classification and retrieval.

10. The method according to claim 9, wherein the metagenomic AMP classification and retrieval is Macrel.

11. The method according to any preceding claim, further comprising removing singleton sequences from the candidate AMPs.

12. The method according to claim 11 , wherein the singleton sequences are not removed if they match a sequence from a data repository of antimicrobial peptides.

13. The method according to any preceding claim, wherein candidate AMPs originating from a genomic database is assigned a taxonomy from the original genome.

14. The method according to any preceding claim, wherein candidate AMPs originating from a metagenome were assigned a taxonomy predicted for the contig in which the candidate AMP was found.

15. The method according to any preceding claim, further comprising identifying potential structural configuration of a candidate AMP by using a secondary structure function from a calculation of a fraction of amino acids in the candidate AMP that tend to assume conformations of helix, turn, or sheet.

16. The method according to any preceding claim, further comprising hierarchically clustering the candidate AMPs using a reduced amino acid alphabet and an identity cutoff of a preselected percentage.

17. The method according to claim 16, wherein the reduced amino acid alphabet includes 6-10 amino acids, preferably 8 amino acids.

18. The method according to claim 16 or claim 17, wherein the identity cutoff is 75%, 85%, or 100%, preferably 75%.

19. A method of treating a microbial infection in a subject comprising administering to the subject a therapeutically effective amount of a candidate AMP that has been identified according to the method of any one of claims 1-18.

20. The method according to claim 19, wherein the candidate AMP is any one of SEQ ID NOS: 1-100.

21. A method comprising contacting a biofilm with an effective amount of a candidate AMP that has been identified according to the method of any one of claims 1 -18.

22. The method according to claim 21, wherein the candidate AMP is any one of SEQ ID NOS: 1-100.

23. A composition comprising a candidate AMP that has been identified according to the method of any one of claims 1-18 and a pharmaceutically acceptable carrier, diluent, or excipient.

24. The method according to claim 23, wherein the candidate AMP is any one of SEQ ID

NOS: 1-100.