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WO2025137775A1 - Procédé de génération et de criblage de bibliothèques d'aptamères peptidiques synthétiques - Google Patents

Procédé de génération et de criblage de bibliothèques d'aptamères peptidiques synthétiques Download PDF

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WO2025137775A1
WO2025137775A1 PCT/CA2024/051738 CA2024051738W WO2025137775A1 WO 2025137775 A1 WO2025137775 A1 WO 2025137775A1 CA 2024051738 W CA2024051738 W CA 2024051738W WO 2025137775 A1 WO2025137775 A1 WO 2025137775A1
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peptide
amino acid
synthetic peptide
amino acids
candidate
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John G. Marshall
Peter BOWDEN
Jaimie DUFRESNE
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    • C—CHEMISTRY; METALLURGY
    • C40—COMBINATORIAL TECHNOLOGY
    • C40B—COMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES
    • C40B40/00—Libraries per se, e.g. arrays, mixtures
    • C40B40/04—Libraries containing only organic compounds
    • C40B40/10—Libraries containing peptides or polypeptides, or derivatives thereof
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C12N15/113—Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing
    • C12N15/1138—Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing against receptors or cell surface proteins
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C12N15/115—Aptamers, i.e. nucleic acids binding a target molecule specifically and with high affinity without hybridising therewith ; Nucleic acids binding to non-nucleic acids, e.g. aptamers
    • C—CHEMISTRY; METALLURGY
    • C40—COMBINATORIAL TECHNOLOGY
    • C40B—COMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES
    • C40B30/00—Methods of screening libraries
    • C40B30/04—Methods of screening libraries by measuring the ability to specifically bind a target molecule, e.g. antibody-antigen binding, receptor-ligand binding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/20—Protein or domain folding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/30—Drug targeting using structural data; Docking or binding prediction
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2310/00—Structure or type of the nucleic acid
    • C12N2310/10—Type of nucleic acid
    • C12N2310/16—Aptamers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2330/00—Production
    • C12N2330/30—Production chemically synthesised
    • C12N2330/31—Libraries, arrays
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6845—Methods of identifying protein-protein interactions in protein mixtures
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients

Definitions

  • a method of identifying a synthetic peptide aptamer, from a library comprising a plurality of synthetic peptide aptamers, that binds a target comprising: providing the peptide aptamer library comprising the plurality of synthetic peptide aptamers, the plurality of synthetic peptide aptamers being generated from at least one synthetic peptide, the at least one synthetic peptide comprises two or more domains separated by a protease cleavage site, wherein at least one domain comprises at least one amino acid this is randomly selected from a subset of the 20 common amino acids; contacting the peptide aptamer library with the target; removing unbound synthetic peptide aptamers after contacting the peptide aptamer library with the target; and analyzing the bound synthetic peptide aptamers, comprising: generating a MS/MS query spectrum of the bound peptide aptamer; receiving one or more parameters of the query spectrum;
  • At least one domain comprises at least one random amino acid.
  • the at least one random amino acid is randomly selected from a subset of amino acids.
  • the subset of amino acids comprises a subset of fewer than 5 amino acids.
  • the cleavage site comprises a protease cleavage site.
  • the at least one synthetic peptide further comprises at least one predetermined amino acid at a predetermined position within at least one domain.
  • Figure 1 is an embodiment of the method to create new peptide aptamer drugs.
  • the general scheme shows creating new punctuated peptide aptamer drugs with fixed amino acids involved in ligand-receptor binding and cleavage sites where each cleave product may contain a pool of >2 amino acids.
  • the synthetic peptide comprises two domains. In some embodiments, the synthetic peptide comprises three domains. In some embodiments, the synthetic peptide comprises four domains. In some embodiments, the synthetic peptide comprises five domains. In some embodiments, the synthetic peptide comprises six domains. In some embodiments, the synthetic peptide comprises seven domains. In some embodiments, the synthetic peptide comprises eight domains. In some embodiments, the synthetic peptide comprises nine domains. In some embodiments, the synthetic peptide comprises ten domains. In some embodiments, the synthetic peptide comprises more than ten domains.
  • the cleavage site can be a chemical cleavage site or a protease cleavage site, such as a trypsin cleavage site or a chymotrypsin cleavage site.
  • the cleavage site comprises a protease cleavage site. In one embodiment, the cleavage site comprises a trypsin cleavage site. In one embodiment, the cleavage site comprises a chymotrypsin cleavage site.
  • the at least one cleavage site comprises an arginine. In some embodiments, the at least one cleavage site comprises a lysine. In some embodiments, the at least one cleavage site comprises a tryptophan. In some embodiments, the at least one cleavage site comprises a tyrosine. In some embodiments, the at least one cleavage site comprises a phenylalanine.
  • the cleavage site comprises a chemical cleavage site.
  • the subset of amino acids comprises a subset of fewer than 20 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 19 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 18 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 17 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 16 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 15 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 14 amino acids.
  • the subset of amino acids comprises a subset of fewer than 13 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 12 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 11 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 10 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 9 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 8 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 7 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 6 amino acids.
  • the subset of amino acids comprises a subset of fewer than 5 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 4 amino acids. In some embodiments, the subset of amino acids comprises a subset of fewer than 3 amino acids. In some embodiments, the subset of amino acids comprises a subset of 2 amino acids.
  • the subset of amino acids comprises a subset of between 2 and 19 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 18 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 17 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 16 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 15 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 14 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 13 amino acids.
  • the subset of amino acids comprises a subset of between 2 and 12 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 11 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 10 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 9 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 8 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 7 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 6 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 5 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 and 4 amino acids. In some embodiments, the subset of amino acids comprises a subset of between 2 or 3 amino acids.
  • the subset of amino acids comprises a subset of the
  • the term “20 common amino acids” refers to the amino acids alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine.
  • the subset of amino acids is not limited to a subset of the 20 common amino acids. Subsets of other amino acid sets can also be used.
  • An amino acid set can comprise one or more common amino acids, one or more uncommon amino acids, one or more unnatural amino acids, or any combination thereof.
  • At least one of the domains comprises a random sequence.
  • the random sequence is a random sequence of 20 amino acids. In some embodiments, the random sequence is a random sequence of 19 amino acids. In some embodiments, the random sequence is a random sequence of 18 amino acids. In some embodiments, the random sequence is a random sequence of 17 amino acids. In some embodiments, the random sequence is a random sequence of 16 amino acids. In some embodiments, the random sequence is a random sequence of 15 amino acids. In some embodiments, the random sequence is a random sequence of 14 amino acids. In some embodiments, the random sequence is a random sequence of 13 amino acids. In some embodiments, the random sequence is a random sequence of 12 amino acids. In some embodiments, the random sequence is a random sequence of 11 amino acids.
  • the random sequence is a random sequence of 10 amino acids. In some embodiments, the random sequence is a random sequence of 9 amino acids. In some embodiments, the random sequence is a random sequence of 8 amino acids. In some embodiments, the random sequence is a random sequence of 7 amino acids. In some embodiments, the random sequence is a random sequence of 6 amino acids. In some embodiments, the random sequence is a random sequence of 5 amino acids. In some embodiments, the random sequence is a random sequence of 4 amino acids. In some embodiments, the random sequence is a random sequence of 3 amino acids. In some embodiments, the random sequence is a random sequence of 2 amino acids.
  • the synthetic peptide can further comprise one or more invariant amino acid residues.
  • the one or more invariant amino acid residues can have acidic, basic or hydrophobic characters.
  • the peptide further comprises at least one predetermined amino acid residue at a predetermined position within at least one domain.
  • the at least one predetermined amino acid residue comprises a hydrophobic amino acid.
  • the at least one predetermined amino acid residue comprises tryptophan, histidine, phenylalanine, methionine, tyrosine, cysteine, or lysine.
  • the peptide can further comprise different random pools of amino acids between two cleavage sites, between two invariant amino acid residues, or between a cleavage site and an invariant amino acid residue, wherein each random pool comprises a pool of a subset of the 20 common amino acids.
  • at least one domain comprises at least one random amino acid.
  • a peptide aptamer library can be designed with tryptic and chymotryptic sites spaced to create several subdomains and in each domain an defined subset of the 20 common amino acids can be randomly ordered alongside invariant amino acids with acidic, basic or hydrophobic characters.
  • An aptamer of all 20 common amino acids randomly ordered would create a large computational problem.
  • randomizing all 20 common amino acids would also create a chemical problem where there were too many peptide species in the library for anyone to reach a concentration in the atto molar or femtomolar or picomolar or nanomolar range for binding assays.
  • the whole aptamer may contain ⁇ 20 random amino acids or modifications thereof, while the cleavage products are each short enough and simple enough to be identified by tandem mass spectrometry.
  • the term “contacting the peptide aptamer library with the target” means allowing the peptide aptamer library and the target to interact by any means.
  • the target can be immobilized on a surface, and the peptide aptamer library can be allowed to come into contact with the surface.
  • Contacting the peptide aptamer library with the target can cause one or more synthetic peptide aptamers to bind the target.
  • synthetic peptide aptamers that are not bound to the target after contacting the peptide aptamer library with the target are removed.
  • the bound synthetic peptide aptamers can be released from the target for analysis.
  • the methods disclosed herein can further involve: generating the in silico peptide library to match the characteristics of the physical library used in the experiment and reducing the dimension for each MS/MS or MSn spectra searched using the observed amino acids from the MS/MS or MSn spectra; fitting the observed MS/MS or MSn spectra from the physical peptide that bound the target to the possible peptide in the in silico library using de novo, or goodness of fit, or regression or linear models or correlation or heuristic algorithms to determine the amino acid sequence and the molecular composition of matter of the physical peptide that bound the target.
  • the method can comprise generating a MS/MS query spectrum of the bound synthetic peptide aptamer.
  • the query spectrum can comprise one or more parameters.
  • the one or more parameters can be received from user input.
  • the one or more parameters can be one or more of an amino acid or an amino acid group.
  • a position corresponding to each of the amino acid or the amino acid group can be included.
  • the one or more parameters can be derived from the query spectrum by a processor.
  • the selection of candidate peptide sequences from memory can be based on the one or more parameters received that can be used as constraints to narrow the search space of candidate peptide sequences.
  • the one or more parameters can include a particular amino acid (e.g., lysine or arginine) for the candidate peptide sequence.
  • the one or more parameters can also include a particular position (e.g., last position) for the particular amino acid.
  • Peptide sequences can be identified from the plurality of peptide sequences stored in memory having the particular amino acid in the particular position and then the candidate peptide sequences can be selected from amongst the peptide sequences having the particular amino acid in the particular position.
  • candidate peptide sequences can be randomly generated to include known amino acids at known or unknown positions. Furthermore, the randomly generated candidate peptide sequences can be tailored to what is known about a particular binding target.
  • the pre-determined spectra intensity can be based on a candidate peptide sequence, such as a percentage of the spectra intensity of a candidate peptide sequence.
  • the percentage of the spectra intensity can be specified by the user.
  • the maximum spectra lines filtering can be applied dynamically.
  • the max spectra relates to the intensity values. Assuming a max spectra value of 50 the engine would only examine the 50 most intense spectra lines. This significantly reduces the computation time as a single MS2 spectra can contain thousands of spectra lines.
  • Precursor mass testing involves comparing a precursor mass of a query sample to the mass of a candidate peptide sequence. Query samples having a precursor mass matching the mass of any candidate peptide can be further processed. Query samples having a precursor mass that does not match any candidate peptide can be discarded from further consideration, and the next sample is assessed.
  • Precursor mass testing can involve determining a precursor mass for a query sample. If the precursor mass is substantially equal to a mass of a candidate peptide sequence of the one or more candidate peptide sequences, that query sample can be selected for comparison with the one or more candidate peptide sequences. The precursor mass can be considered to be substantially equal to a mass of a candidate peptide sequence within a pre-determined error range of the mass of the candidate peptide sequence. In some embodiments, the pre-determined error range can be specified by the user. [00107] In some embodiments, a precursor mass for a query sample can be determined at different charge states. For example, precursor masses at charge states of 1 , 2, and 3 can be determined.
  • Base peak testing involves comparing a mass of a theoretical ion of the query sample to the mass of a base peak.
  • Query samples having theoretical ions having a mass that matches the mass of the base peak can be further processed.
  • Query samples having theoretical ions having a mass that does not match the base peak can be discarded from further consideration, and the next sample is assessed.
  • a mass of a theoretical ion of the query sample is determined. If the mass of the theoretical ion is substantially equal to a mass of a base peak, that query sample can be selected for comparison with one or more candidate peptide sequences.
  • the precursor mass can be added to the theoretical spectra.
  • the likelihood of identifying the first amino acid in the peptide sequence can be increased and the Amino Acid Match Ratio (AAMR) score can be improved.
  • a theoretical ion can be added to the theoretical spectra, that is the low end of the spectra can be added to.
  • the theoretical ion can represent a terminal mass. Addition of the theoretical ion can improve the peptide match mass ratio score.
  • a theoretical peptide’s MH value at charge state 1 , or 2 or 3 could be added to the theoretical spectra. Addition of the theoretical peptide’s MH value at charge state 1 , or 2 or 3 can apply to the peptide match score. Once a random peptide is generated, both the theoretical spectra and MH value can be calculated. The MH mass can be compared to the precursor mass so as to calculate the peptide's charge. The mass spectrometer however, regardless of the precursor mass may well be reporting the spectra for a peptide at each of the three charge states within a single MS2 scan.
  • the real 1 + spectra representing the unknown peptide can generally provide the most usable spectra.
  • the 1 + spectra can be used as the primary identification signal followed by examination of the 2+ and 3+ spectra for additional evidence of a good identification.
  • the MH of a particular peptide can be injected into the observed spectra. Addition of the MH of a particular peptide can be used to calculate the peptide match score.
  • the difference in theoretical mass and the observed peptide mass can be calculated using the estimated charge of a theoretical peptide, the known theoretical mass of the same theoretical peptide, and the known observed precursor mass.
  • the delta mass can be used to identify a modification which is an extra chemical element on the peptide. This is also known as a modification mass. For example, phosphorylation is a commonly observed post translational modification and has a known mass shift of 79.99 Da; if the delta mass is about this value, then it can be determined that the peptide is phosphorylated.
  • application of signal processing filters can vary. For example, in some embodiments, only minimum spectra counting can be used. In other embodiments, minimum spectra counting, maximum spectra lines filtering, sequential ion (e.g., b and y ions) counting, and precursor testing can be used. Other combinations are possible. Furthermore, the signal processing filters can also depend on whether a candidate peptide sequence is selected from memory or randomly generated.
  • Candidate peptide sequences can be selected from memory.
  • one theoretical peptide or candidate peptide sequence at a time is compared to all available scans by tests and scoring algorithms until the peptide list is exhausted.
  • each theoretical peptide and spectrum pair must pass a precursor mass test and base peak test before they are scored by at least one of chi square, multiple/linear or nested regression, amino acid match ratio, and ion intensity match ratio. A user defined combination of these scores can be used to identify the best match for each scan.
  • one query sample can be compared to each candidate peptide sequence before another query sample is compared to each candidate peptide sequences.
  • Candidate peptide sequences can be randomly generated.
  • a “first in first out” (FIFO) queue of candidate peptide sequences can be used. The queue grows as candidate peptide sequences are generated and shrinks each time a candidate peptide sequence is processed.
  • FIFO first in first out
  • Constraints can be used to narrow the possible combinations for randomly generated candidate peptide sequences. Constraints can be specified by the user, by one or more parameters. Constraints can also be determined from the library search method or from amino acid matching. As set out above, constraints can relate to a pre-determined length, a minimum number of amino acids from different amino acid groups, a particular amino acid at a particular position, and/or a precursor mass.
  • a constraint relating to the precursor mass is used. All query samples having a precursor mass within a tolerance window can be retrieved and a list of the base peak values created from these scans.
  • a candidate peptide sequence can be randomly generated based on a pre-determined length and wild cards, and then the MH value of that candidate peptide sequence can be generated at each of 3 charge states.
  • the precursor test to the candidate peptide sequence can be run and if passed, theoretical spectra can be generated and the base peak test can be run. If the base peak test is passed, the more computationally intensive AAMR and I IM R tests are run. If these last two scores are above minimum thresholds, then the candidate peptide sequence is considered a match and is stored.
  • the precursor and base peak tests can be repeated again later in the workflow due to technical reasons.
  • the precursor hint can be a list of m/z values so one candidate peptide sequence might not match at the precursor at the precursor level for a particular scan, and similarly it may not pass the base peak test.
  • a likelihood indicator for each candidate peptide sequence can be determined based on a comparison with the at least one query sample.
  • determining a likelihood indicator for each candidate peptide sequence can involve applying scoring techniques, filtering techniques or a combination thereof.
  • scoring techniques can first be applied.
  • the scoring techniques can be analogous to the signal processing filters, including spectra line filtering.
  • the likelihood indicator can alternatively or additionally be determined based on additional filtering techniques including one or more of a chi-square score, a regression score, and a cross correlation score. Other scoring functions can be used to generate a likelihood indicator.
  • a chi-square score can be used to rank candidate peptide sequence matches.
  • a candidate peptide sequence can be selected as a proposed peptide sequence based on differences between an observed ion count of the candidate peptide sequence and the predicted theoretical ion count.
  • the chi-square score can be calculated by summing the number of expected theoretical ions (e.g., b and y ions) for the expected peptide that fall within the M/z range of the mass spectrometer; summing the number of observed ions that match the expected M/z values, within the mass resolution limit of the mass spectrometer and applying Equation (2).
  • expected theoretical ions e.g., b and y ions
  • the best fit per spectrum can be selected by Library and Search Engine.
  • the counts may be split off into the method where the SpectralD scored the best, or had the highest likelihood indicator. In this case there is minimal overlap between the two methods except where the SpectralD scored equally highest in both methods.
  • Searchdefinition ID corresponds to the library and search settings.
  • the method disclosed herein can comprise applying a signal to noise filter.
  • Noise can be generated from various sources.
  • noise can come from a statistical control and/or a non-specific binding control.
  • a statistical control can include, for example, an electromagnetic noise control, a random MS/MS spectra control, or both.
  • a pre-immune serum may be used in binding.
  • An affinity column without an antigen attached, a affinity column with a control antigen attached, a naive affinity column that has not been pre-treated (blocked by a blocking agent), and/or a conditioned affinity column can also be a non-specific binding control.
  • an observation frequency of at least one control and an observation frequency of at least one query sample can be determined. If the observation frequency of the at least one query sample is higher than the observation frequency of the at least one control, then the at least one query sample can be further analyzed and/or the candidate spectrum can be selected as a proposed spectrum.
  • a threshold of the difference between the observation frequencies may be set to allow the at least one query sample to be further analyzed.
  • applying the signal to noise filter comprises determining an observation frequency of at least one query sample. In some embodiments, applying the signal to noise filter comprises determine an observation frequency of at least one control. In some embodiments, if the observation frequency of the at least one query sample is higher than the observation frequency of the at least one control, then the at least one query sample is further analyzed. In some embodiments, if the observation frequency of the at least one query sample is higher than the observation frequency of the at least one control, then the candidate spectrum is selected as a proposed spectrum.
  • applying the signal to noise filter comprises (i) determining an observation frequency of at least one query sample; (ii) determine an observation frequency of at least one control; (iii) if the observation frequency of the at least one query sample is higher than the observation frequency of the at least one control, then the candidate spectrum is selected as a proposed spectrum.
  • the method can comprise the use of experimental controls.
  • the use of experimental controls can comprise the use of: (1 ) naive blank columns (never used) or conditioned blank columns; (2) control affinity support resin without the specific antigen, ligand, receptor, or diagnostic or therapeutic target; (3) a support resin with a control antigen, ligand, receptor, or diagnostic or therapeutic target; (4) support resin with the specific antigen, ligand, receptor, or diagnostic or therapeutic target for use with pre-immune serum; or (5) some other controls from tissues, cells or bodily fluids.
  • the method may comprise the use of statistical controls, which can comprise the use of naturally-occurring electromagnetic noise or random MS/MS spectra or computer generated random MS/MS spectra to select potential peptide sequences with a higher probability of being true positive peptide identification from MS/MS spectra.
  • the target comprises a receptor or a fragment thereof. In one embodiment, the target comprises a ligand or a fragment thereof. In one embodiment, the target comprises an enzyme or a fragment thereof. In one embodiment, the target comprises a protein or a fragment thereof. In one embodiment, the target comprises an antibody or a fragment thereof. In one embodiment, the target comprises a variable domain or a fragment thereof. In one embodiment, the target comprises a drug.
  • the target is immobilized on microbeads, nanobeads, a 2-dimensional surface, a 3-dimensional scaffold, and/or a 3-dimensional fiber.
  • Synthetic peptides may be obtained from a random combinatorial approach.
  • Synthetic peptides may be obtained where fixed residues are comprised of hydrophobic amino acids that may contribute to binding including but not limited to W, H, F, M, Y, C or L.
  • a peptide aptamer library will be designed with tryptic and chymotryptic sites spaced to create several subdomains and in each domain a defined subset of the 20 amino acids will be randomly ordered alongside invariant amino acids with acidic, basic or hydrophobic characters. While an aptamer of all 20 amino acid randomly ordered would create a large computational problem. Moreover, randomizing all 20 amino acids would also create a chemical problem where there are too many peptide species in the library for anyone to reach a concentration in the atto molar or femtomolar or picomolar or nanomolar range for binding assays.
  • a synthetic peptide with the following structure can be made for generating a library of peptide aptamers (FIG. 3):
  • Specific high observation frequency peptide adapters can be:
  • Example 4 Using the aptamer library to identify binding agents to a target
  • the aptamer library will be incubated with the receptor, ligand, enzyme, protein, antibody, variable domain or drug to induce binding.
  • the receptor, ligand, enzyme, protein, antibody, variable domain or drug will be immobilized on microbeads or nanobeads or a flat 2 dimensional surface or 3 dimensional scaffold or fiber.
  • Bound peptides will be eluted with strong salt solutions, strong mixtures of organic solvents with water, strong acids or bases far from neutral pH.
  • the eluted peptides will be identified by mass spectrometry, or top down mass spectrometry, or electrospray ionization, or MALDI ionization or chemical ionization or electron impact ionization or LC-ESI-MS/MS.
  • the amino acid sequence will be derived from the MS/MS spectra by de novo sequencing.
  • the observed MS/MS spectra will be fitted to a predicted library of MS/MS spectra by 64 bit computation using de novo sequencing using XTANDEM, SEQUEST, or regression, or goodness of fit, or heuristic algorithms, or other algorithms.
  • Real peptides and their fragments frequently contain heavy isotopes and so a filter to look for spectrum lines where there is a presence of isotopes of hydrogen rearrangements or H loss may or hydrogen rearrangements and rarely - using isotope filtering to remove the noise.
  • Isotopes and hydrogen re-arrangements or losses may occur in precursor peptides or fragments.
  • accession numbers provided herein including for example accession numbers and/or biomarker sequences (e.g. protein and/or nucleic acid) provided in the Tables or elsewhere, are incorporated by reference in its entirely.

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Abstract

L'invention concerne des procédés de fabrication d'une bibliothèque d'aptamères peptidiques partiellement aléatoires. La bibliothèque d'aptamères peptidiques peut être conçue avec des sites tryptiques et chymotryptiques espacés pour créer des domaines. Dans chaque domaine, un sous-ensemble défini d'acides aminés est ordonné de manière aléatoire à côté d'acides aminés invariants. L'invention concerne également des procédés d'identification d'aptamères peptidiques qui se lient à une cible à l'aide de la bibliothèque d'aptamères peptidiques de l'invention.
PCT/CA2024/051738 2023-12-29 2024-12-27 Procédé de génération et de criblage de bibliothèques d'aptamères peptidiques synthétiques Pending WO2025137775A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050053970A1 (en) * 2001-11-06 2005-03-10 Benson John D. Methods and compositions for identifying peptide aptamers capable of altering a cell phenotype
WO2007090630A2 (fr) * 2006-02-07 2007-08-16 Stiftung Für Diagnostische Forschung Aptamere de peptidique pour neutraliser la liaison des anticorps specifiques d'un antigene plaquettaire et applications diagnostiques et therapeutiques qui le contiennent
WO2024000077A1 (fr) * 2022-06-30 2024-01-04 Yyz Pharmatech Inc. Systèmes et procédés d'identification de peptides

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050053970A1 (en) * 2001-11-06 2005-03-10 Benson John D. Methods and compositions for identifying peptide aptamers capable of altering a cell phenotype
WO2007090630A2 (fr) * 2006-02-07 2007-08-16 Stiftung Für Diagnostische Forschung Aptamere de peptidique pour neutraliser la liaison des anticorps specifiques d'un antigene plaquettaire et applications diagnostiques et therapeutiques qui le contiennent
WO2024000077A1 (fr) * 2022-06-30 2024-01-04 Yyz Pharmatech Inc. Systèmes et procédés d'identification de peptides

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