WO2025042561A1 - Deconvolution by visual acuity-based interpretation of mass spectrometry (ms) and tandem mass spectrometry (ms/ms) spectra - Google Patents

Abstract

In some examples, deconvolution by visual acuity-based interpretation of mass spectrometry (MS) and tandem mass spectrometry (MS/MS) spectra may include receiving, for an ion that is be identified, a plurality of expected ion spectra, where each expected ion spectrum of the plurality of expected ion spectra may include at least one expected peak profile, receiving an observed ion spectrum including at least one observed peak profile, and identifying characteristics of the expected ion spectra and the observed ion spectrum. The characteristics may be analyzed to determine ion scores, where a highest ion score may be used to identify a corresponding expected ion spectrum. An indication and/or a graphical user interface display of the expected ion spectrum corresponding to the highest ion score may be generated.

Description

20230180-03 DECONVOLUTION BY VISUAL ACUITY-BASED INTERPRETATION OF MASS SPECTROMETRY (MS) AND TANDEM MASS SPECTROMETRY (MS/MS) SPECTRA CROSS-REFERENCE TO RELATED APPLICATION(S) This application claims priority to U.S. Provisional Patent Application Serial Numbers 63/520,517, filed August 18, 2023, titled “DECONVOLUTION BY VISUAL ACUITY- BASED INTERPRETATION OF MASS SPECTROMETRY (MS) AND TANDEM MASS SPECTROMETRY (MS/MS) SPECTRA”, and 63/585,841, filed September 27, 2023, titled “DECONVOLUTION BY VISUAL ACUITY-BASED INTERPRETATION OF MASS SPECTROMETRY (MS) AND TANDEM MASS SPECTROMETRY (MS/MS) SPECTRA” both of which are incorporated herein by reference in their entirety. BACKGROUND [0001] With respect to annotation of fragment ions in a mass spectrum, a spectrum may originate, for example, from either a short peptide or intact protein such as a monoclonal antibody. Various types of deconvolution methods exist. Such deconvolution methods may be method-specific or require expert tuning when transitioning between low-noise peptide MS/MS spectra and high-complexity top-down protein mass spectra. Furthermore, various types of software tools may be utilized for averaging together scans to improve signal-to-noise ratios. BRIEF DESCRIPTION OF DRAWINGS [0002] Features of the present disclosure are illustrated by way of example and not 20230180-03 limited in the following figure(s), in which like numerals indicate like elements, in which: [0003] Figure 1 illustrates a layout of a deconvolution by visual acuity-based interpretation of mass spectrometry (MS) and tandem mass spectrometry (MS/MS) spectra apparatus, in accordance with an example of the present disclosure; [0004] Figures 2A-2C illustrate compartmentalizing aspects of visual acuity to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0005] Figure 3 illustrates definition of an ion scoring binary classifier to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0006] Figures 4A-4C illustrate application of a consistent weighting scheme to all classifier measures to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0007] Figure 5 illustrates exhaustive search of hydrogen transfer to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0008] Figure 6 illustrates annotated ions to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0009] Figures 7A and 7B illustrate setting of signal processing parameters to illustrate 20230180-03 operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0010] Figures 8A and 8B illustrate benchmarking ion scoring to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0011] Figure 9 illustrates intact NIST monoclonal antibody (NIST mAb) to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0012] Figure 10 illustrates top-down deconvolution benchmarking to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0013] Figure 11 illustrates users set preferences to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0014] Figure 12 illustrates parameter-free deconvolution to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0015] Figure 13 illustrates deconvolution and de-charging in ExDViewer to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0016] Figure 14 illustrates hybrid targeted/untargeted deconvolution to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS 20230180-03 spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0017] Figure 15 illustrates identification of ions in a MS/MS spectrum with ECD, and monitoring of ion charge distributions and efficiency to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0018] Figure 16 illustrates ion identification to illustrate operation of the deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus of Figure 1, in accordance with an example of the present disclosure; [0019] Figure 17 illustrates an example block diagram for deconvolution by visual acuity-based interpretation of MS and MS/MS spectra, in accordance with an example of the present disclosure; [0020] Figure 18 illustrates a flowchart of an example method for deconvolution by visual acuity-based interpretation of MS and MS/MS spectra, in accordance with an example of the present disclosure; and [0021] Figure 19 illustrates a further example block diagram for deconvolution by visual acuity-based interpretation of MS and MS/MS spectra, in accordance with another example of the present disclosure.

20230180-03 DETAILED DESCRIPTION [0022] For simplicity and illustrative purposes, the present disclosure is described by referring mainly to examples. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be readily apparent however, that the present disclosure may be practiced without limitation to these specific details. In other instances, some methods and structures have not been described in detail so as not to unnecessarily obscure the present disclosure. [0023] Throughout the present disclosure, the terms "a" and "an" are intended to denote at least one of a particular element. As used herein, the term "includes" means includes but not limited to, the term "including" means including but not limited to. The term "based on" means based at least in part on. [0024] Apparatuses for deconvolution by visual acuity-based interpretation of MS and MS/MS spectra, and methods for deconvolution by visual acuity-based interpretation of MS and MS/MS spectra are disclosed herein. The apparatuses and methods disclosed herein provide, for example, for averaging of profile spectra directly from native vendor file formats, deconvolution of any unannotated spectrum without expert adjustment of input parameters, identification of fragmentation ions by their exact isotopic distribution, enablement of manual curation of these identified ions, and generation of visualizations in a web-friendly application that may be readily disseminated. The apparatuses and methods disclosed herein further provide an all-in-one tool for top-down and intact MS/MS deconvolution that produces high-quality interactive visualizations shareable over a cloud environment. [0025] The apparatuses and methods disclosed herein provide, for example, greater 20230180-03 than 80% sensitivity of all discoverable ions to transform noisy centroid data into a resolved list of ions. For example, the apparatuses and methods disclosed herein provide, for example, generation of deconvolved ion lists with near-perfect accuracy (e.g., 99% accuracy) and very high sensitivity of interpretable ions (e.g., >80%). The improvement in accuracy enables new automated (e.g., without human intervention) analytical techniques to be performed with complex MS and MS/MS spectra of intact proteins and large (>2 kDa) macromolecules that were previously not possible. One obstacle to wider adoption of top-down and intact workflows is that achieving high sequence coverage of desired ions can require expert tuning of instrumentation parameters. Instrument operators may often investigate multiple instrument settings, such as collision energy, ion source conditions, and, in the case of the proprietary e- MSion ExD cell, up to six different voltage gradients to direct the flow of ions through and around an electron-emitting filament that induces desired reactions such as charge reduction and molecular fragmentation. In this regard, near-perfect accuracy of automatic ion identification may be implemented with the apparatuses and methods disclosed herein for more robust automatic tuning of these instrumentation parameters, which currently relies upon measures of peak intensity that may be susceptible to noise, which is omnipresent in aforementioned complex mass spectra as disclosed herein. Analysis software may now, in less than a second, examine a large set of putative ions, determine which ones are detectible, and report their cumulative score. This information may be utilized by the apparatuses and methods disclosed herein as feedback to control software, allowing for real-time optimization of instrumentation parameters that maximizes coverage of expected ions. 20230180-03 [0026] For the apparatuses and methods disclosed herein, the identified ion information may be incorporated into a coverage map that may represent a medium for communicating which fragment ions were found for a given sequence. Users are typically concerned with which amino acid (AA) residues and post-translational modifications (PTMs) are supported by fragment ions. For example, a biopharmaceutical company may be interested in whether their drug manufacturing platform is producing proteins/peptides with the desired AA sequence and PTMs. If unexpected AA mutations or PTMs are found, the Food and Drug Administration (FDA) may be concerned of the drug’s safety profile and suspend drug approval. If unexpected mutations or PTMs are found, the coverage map may be absent important fragment ion information because expected ions from the unmodified sequence will not be found. For some deconvolution methods, poor accuracy may mean that users cannot immediately trust these disparities in coverage, and thus may need to rely on alternative methods to produce MS/MS spectra of lower complexity, such as enzyme digestion, which can add significantly more sample preparation time and cost depending on the specific protocol. Such methods may also introduce their own PTMs that can confound analysis of which PTMs are present in the undigested sample. [0027] For the apparatuses and methods disclosed herein, in one example, certain features may be implemented by utilizing software that includes a C++ server built on OpenMS (e.g., for data processing) and a JavaScript front end (e.g., for visualization), all available in a downloadable Windows installer or a browser-friendly web application. Data may be read from various vendor formats (e.g., AGILENT vendor format) along with open- source mzML (a community standard for mass spectrometry data) and text of (mascot 20230180-03 generic format) MGF file types. When profile data is present, the data may be averaged together over multiple scans to improve signal-to-noise ratios. Noise levels and peak picking parameters may be automatically (e.g., without human intervention) adjusted based on peak density. Deconvolution may utilize, for example, an ab initio scoring method that assigns, for example, a score of 0-15 to each predicted isotopic cluster based on how well the observed data matches theoretical data, considering profile peak data, centroid mass/charge number of ions (m/z) error, hydrogen transfer, and overlap of neighboring peaks. [0028] For the apparatuses and methods disclosed herein, in one example, certain features may be implemented by utilizing software to detect and visualize sequence coverage and the associated MS or MS/MS fragmentation ions generated in top-down mass spectrometry, particularly electron-based fragmentation. In this regard, both electron capture dissociation (ECD) and electron-transfer dissociation (ETD) may generate satellite ions, such as w-type, that distinguish Leucine/Isoleucine (Leu/Ile) and aspartate/asparagine (Asp/Asn) but are often low-intensity and sometimes challenging to reliably identify in the presence of noise and neighboring peak complexity. Hydrogen transfer re-arrangements may further complicate analysis by widening expected isotopic distributions. Thus, benchmarking may be performed with a diverse set of ECD and collision-induced dissociation (CID) spectra from samples containing peptides and proteins between 1.4 - 29 kDa. [0029] For the apparatuses and methods disclosed herein, with respect to benchmarking, the generated annotations include a high degree of accuracy. In this regard, the apparatuses and methods disclosed herein provide for top-down MS/MS 20230180-03 analysis with a fully automated analysis workflow for matching experimental spectra to peptides and proteins in an efficient and expedited manner by delivering results in real- time (e.g., raw time-of-flight (TOF) data to visualization in < 30 seconds). [0030] For the apparatuses, methods, and non-transitory computer readable media disclosed herein, the elements of the apparatuses, methods, and non-transitory computer readable media disclosed herein may be any combination of hardware and programming to implement the functionalities of the respective elements. In some examples described herein, the combinations of hardware and programming may be implemented in a number of different ways. For example, the programming for the elements may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the elements may include a processing resource to execute those instructions. In these examples, a computing device implementing such elements may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separately stored and accessible by the computing device and the processing resource. In some examples, some elements may be implemented in circuitry. [0031] Figure 1 illustrates a layout of a deconvolution by visual acuity-based interpretation of MS and MS/MS spectra apparatus, (hereinafter also referred to as “apparatus 100”), in accordance with an example of the present disclosure. [0032] Referring to Figure 1, the apparatus 100 may include an ion spectra analyzer 102 that is executed by at least one hardware processor (e.g., the hardware processor 1702 of Figure 17, and/or the hardware processor 1904 of Figure 19) to receive, for an ion 104 that is be identified, a plurality of expected ion spectra 106. Each expected ion 20230180-03 spectrum 108 of the expected ion spectra 106 may include at least one expected peak profile 110. The ion spectra analyzer 102 may receive, for the ion 104 that is be identified, an observed ion spectrum 112 including at least one observed peak profile 114. The ion spectra analyzer 102 may identify, based on an analysis of the at least one expected peak profile 110 for each expected ion spectrum of the expected ion spectra 106 and the at least one observed peak profile for the observed ion spectrum 112, characteristics 116 of the expected ion spectra 106 and the observed ion spectrum 112. [0033] An ion score generator 118 that is executed by the at least one hardware processor (e.g., the hardware processor 1702 of Figure 17, and/or the hardware processor 1904 of Figure 19) may determine, based on an analysis of the identified characteristics 116 of the expected ion spectra 106 and the observed ion spectrum 112, ion scores 120. [0034] An ion score analyzer 122 that is executed by the at least one hardware processor (e.g., the hardware processor 1702 of Figure 17, and/or the hardware processor 1904 of Figure 19) may identify, from the ion scores 120, a highest ion score 124. The ion score analyzer 122 may identify, based on the highest ion score 124, an expected ion spectrum 126 corresponding to the highest ion score 124. [0035] A visual results generator 128 that is executed by the at least one hardware processor (e.g., the hardware processor 1702 of Figure 17, and/or the hardware processor 1904 of Figure 19) may generate, based on the identification of the expected ion spectrum 126 corresponding to the highest ion score 124, an indication 130 and/or a graphical user interface display 132 of the expected ion spectrum 126 corresponding to the highest ion score 124. 20230180-03 [0036] The ion score analyzer 122 may compare the highest ion score 124 to at least one threshold range 134 (e.g., 0-15 for an overall threshold range that may include intermediate threshold ranges of greater than 0 to less than 5 for “weak” match, greater than or equal to 5 and less than 11 for a “good” match, and greater than or equal to 11 and less than or equal to 15 for a “great” match). The ion score analyzer 122 may determine, based on the comparison of the highest ion score 124 to the at least one threshold range 134, a type of match 136 (e.g., “weak”, “good”, or “great” as disclosed herein) of the expected ion spectrum 126 corresponding to the highest ion score 124 to the observed ion spectrum 112. Further, the visual results generator 128 may generate, based on the determination of the type of match 136 of the expected ion spectrum 126 corresponding to the highest ion score 124 to the observed ion spectrum 112, another indication 138 or another graphical user interface display 140 of the determination of the type of match 136. [0037] According to examples disclosed herein, the identified characteristics 116 of the expected ion spectra 106 may include expected centroid mass/charge number of ions (m/z) and intensity values, expected profile signal, m/z parts per million (ppm) error, expected centroid standard deviation, and noise threshold. [0038] According to examples disclosed herein, the identified characteristics 116 of the observed ion spectrum 112 may include at least one observed centroid value, observed profile signal, and mass/charge number of ions (m/z) parts per million (ppm) error. [0039] The ion score generator 118 may determine, based on the analysis of the identified characteristics 116 of the expected ion spectra 106 and the observed ion 20230180-03 spectrum 112, the ion scores 120 by determining the ion scores 120 as a function of a chi squared p-value, a spearman correlation, a pearson p-value, and a noise probability. [0040] According to examples disclosed herein, the ion score generator 118 may determine the ion scores 120 as a function of the chi squared p-value by determining a probability of randomly observing a set of centroid mass/charge number of ions (m/z) and intensity values that match expected m/z parts per million (ppm) error and intensity standard deviation. [0041] According to examples disclosed herein, the ion score generator 118 may determine the ion scores 120 as a function of the spearman correlation by determining a rank-based correlation coefficient between a specified expected ion spectrum of the expected ion spectra and the observed ion spectrum. [0042] According to examples disclosed herein, the ion score generator 118 may determine the ion scores 120 as a function of the pearson p-value by determining a probability of randomly observing a set of centroid mass/charge number of ions (m/z) and intensity values for the observed ion spectrum that linearly correlate to expected values with a higher pearson correlation coefficient for a specified expected ion spectrum of the expected ion spectra. [0043] According to examples disclosed herein, the ion score generator 118 may determine the ion scores 120 as a function of the noise probability by determining peak density as a function of intensity rank between a specified expected ion spectrum of the expected ion spectra and the observed ion spectrum. [0044] Operation of the apparatus 100 is described in further detail with reference to 20230180-03 Figures 2A-16. [0045] Figures 2A-2C illustrate compartmentalizing aspects of visual acuity to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0046] Referring to Figures 2A-2C, with respect to compartmentalizing aspects of visual acuity, ion matches may be determined based on factors such as alignment between expected peak profiles and observed peak profiles, and confounding a signal below a subjective threshold. In this regard, the quality of peak matches may be based on subjective measures. For the apparatus 100, each of these various measures may be compartmentalized and placed into well-defined terms, and levels may be adjusted until they are aligned with a consensus of expert annotations. [0047] Figures 2A-2C illustrate a match between an expected ion and observed data from direct infusion of Carbonic Anhydrase into an liquid chromatography–mass spectrometry (LC/MS) system, such as the Agilent QTOF, equipped with an electron- based dissociation (ExD) cell. A “great”, “good”, and “weak” match are shown, respectively, at 200, 202, and 204. The crosses, some of which are indicated at 206, 208, and 210, may denote expected m/z and intensity of each isotopic peak. Width of the boxes, some of which are indicated, for example, at 212, 214, and 216, may denote m/z parts per million (ppm) error around each peak, which may be typically well defined. The height of each box, for example, at 212, 214, and 216, may correspond to estimation of peak intensity standard deviation, which is a function of expected centroid intensity and is a subjective bound that may correspond to how much deviation is typically allowed between observed and expected intensity values. The dashed line, for example, at 218, 20230180-03 220, and 222 may correspond to an estimation of noise threshold, which may represent an intensity cutoff defined as a function of m/z. Peaks below this threshold may be ignored as confounding. A confounding signal may be measured visually by examining the signal in the m/z range of boxes shown at 224, 226, and 228, which lies between peak areas in the boxes, for example, at 212, 214, and 216. An abundance of non-zero signal may degrade the quality of an ion match. [0048] Figure 3 illustrates definition of an ion scoring binary classifier to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0049] Referring to Figure 3, for an input that includes expected centroid m/z and intensity values (e.g., crosses, such as the cross at 300), observed centroid values (e.g., lines, such as the line at 302), expected profile signal (e.g., dashed line at 304), observed profile signal (e.g., at 306), m/z ppm error, expected centroid standard deviation, and noise threshold, an output may include an ion score 0 Æ 15. [0050] With respect to Figure 3, the ion score may be determined as follows: Ion Score = (1.0 - chi squared p-value) * spearman correlation * log((1.0 - pearson p-value) / noise probability)) Equation (1) For Equation (1), the chi squared p-value may represent a probability of randomly observing a set of centroid m/z and intensity values that match expected m/z ppm error and intensity standard deviation. The spearman correlation may represent a rank-based correlation coefficient between observed and expected profile signal. The pearson p- value may represent a probability of randomly observing a set of centroid m/z and intensity values that linearly correlate to expected values with a higher pearson correlation 20230180-03 coefficient. Further, with respect to noise probability, assuming independence of centroids, with respect to the probability of observing peaks with higher intensities, the noise probability may capture peak density as a function of intensity rank in the wider spectrum. [0051] Figures 4A-4C illustrate application of a consistent weighting scheme to all classifier measures to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0052] Referring to Figures 4A-4C, a “great”, “good”, and “weak” match are shown, respectively, at 400, 402, and 404. With respect to the application of consistent weighting, these measures may include greater applicability with respect to the most abundant isotopic peaks. Less abundant isotopic peaks may not be seen at all and may have greater standard deviation. A straight line may be drawn between each expected centroid peak (top of the polygon at 406). This line may define the weight of each measure as a function of m/z, which is applied to all measures defined in Figure 3. For example, a weighted chi squared p-value may be utilized where each weight is defined as the expected centroid intensity. For the plot of Figure 4A at 400, the areas in the polygon shapes may be devoid of overlapping signal, except for the last triangle on the right side in the orientation of Figure 4A, which has the least weight. For the plot of Figure 4A at 400, expected isotopic ratios match observed centroids perfectly. For the plot of Figure 4B at 402, the tallest polygons at 408, with the most weight, have sparse overlapping signal. Expected isotopic ratios may match observed centroids perfectly, but only for the most abundant peaks. For the plot of Figure 4C at 404, a limited number of the tallest polygons at 410 have acceptable overlapping signal. Expected isotopic ratios may 20230180-03 loosely match observed centroids in those areas as well. [0053] Figure 5 illustrates exhaustive search of hydrogen transfer to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0054] Referring to Figure 5, an exhaustive search of hydrogen transfer may allow each ion match to use any combination of +/-1 or +/-2 hydrogen transfers. In this regard, three possibilities may exist. For the first two possibilities, since ^+ ^+ ^ = 1, linear algebra may be utilized to determine the coefficients that minimize error between observed and expected centroid values. Applying a single hydrogen transfer event (e.g., ^^^^^{^}±^^{^}) to a peak distribution may increment or decrement each m/z value by mass(H+)/charge. With respect to expected isotopic peak distribution options, an option with the highest score may be utilized from the following options: (1) ^^ × ^^^^(0)) + ^^ × ^^^^(H)) + ^^ × ^^^^(2H)) (₂₎ ^{^} _{^ × ^^^^(0)} ⁾ ₊ ^{^} _{^ × ^^^^(-H)} ⁾ ₊ ^{^} _{^ × ^^^^(െ2^)} ⁾ (3) neutral [0055] Referring to Figure 5, for ^ = 0.33, ^ = 0.51, and ^ = 0.15, 51% H, 15% 2H, and 33% neutral species may provide the best possible score for this match as shown at 500, 502, and 504. The plot at 500 may represent ^^^^(0), plot at 502 may represent ^^^^(H), and plot at 504 may represent ^^^^(2H). [0056] Figure 6 illustrates annotated ions to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0057] Referring to Figure 6, a set of centroid(s) above noise threshold is illustrated at 600, and annotated ion(s) are illustrated at 602. In this regard, at 604, the ion spectra 20230180-03 analyzer 102 may perform a match to isotopic distribution of a known ion. At 606, the ion score generator 118 may maximize the ion score over all possible hydrogen transfers. At 608, the ion spectra analyzer 102 may perform a match to the averagine isotopic distribution (unknown). At 610, the ion score generator 118 may determine the ion score. At 612, the ion score analyzer 122 may sort all ions by decreasing score. Further, at 614, the ion score analyzer 122 may iteratively select a highest scoring ion and add it to the output if the ion does not contain a centroid that was previously assigned. [0058] Figures 7A and 7B illustrate setting of signal processing parameters to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0059] Referring to Figures 7A and 7B, in order to set signal processing parameters, data, such as Orbitrap profile data, may be cleaned with baseline and noise removal. In this regard, TOF data may include spurious and overlapping signals depending on the analyte. Short peptides, as shown at 700, may yield a relatively simple spectra that can be annotated by matching centroids. An associated TOF data for the short peptides at 700 is shown at 702. Top-down fragmentation of large macromolecules (e.g., antibodies) may require tuning of deconvolution parameters. Scoring preferences, signal-to-noise threshold, window widths, etc., may be specified based on a number of isotopic peaks needed, as well as the existence of overlapping isotopic distributions. [0060] Figures 8A and 8B illustrate benchmarking ion scoring to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0061] Referring to Figures 8A and 8B, with respect to benchmarking, in order to test sensitivity and accuracy, two ground truth data sets may be utilized, with each data set considering any ion with at least one isotopic peak found above noise threshold. Based 20230180-03 on the verification (e.g., by multiple verifiers skilled in the art) of a set of putative ions from Ubiquitin (QTOF analyzed), annotations with a high degree of accuracy with respect to an ion at a specified m/z and charge were utilized. Figure 8A shows results with respect to multiple verifiers at 800, whereas Figure 8B shows results with respect to a single verifier 802. At 90% sensitivity, for the apparatus 100, >99% of ions were correctly labeled by m/z and charge (scoring threshold = 1.21). At 85% sensitivity, for the apparatus 100, >99% of ions were correctly labeled by m/z and charge (scoring threshold = 1.15). Ions with a score near the threshold (e.g., score = 2.0) may exhibit “weak” but “acceptable” matching quality, indicating that the aforementioned ion scoring techniques are being performed accurately. [0062] Figure 9 illustrates intact NIST mAb to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0063] Referring to Figure 9, as shown at 900, the most abundant ions may be identified with a visual acuity classifier, with their profile signal being subtracted, and further iterated. For an observed ion spectrum shown at 900, results of ions identified by the apparatus 100 are shown at 902, 904, 906, 908, and 910. The associated region of interest is shown at 912. The equation for computing scores is disclosed herein with respect to Figure 5. The visual acuity classifier may operate by combining observed evidence from the raw profile signal with processed centroid peaks to test the hypothesis that alignment between observed and expected data was due to random chance. For example, the spearman correlation measures divergence between observed (e.g., wavy line at 914) and expected profile signal. The expected profile signal may be generated by first estimating instrument resolution (e.g., peak width) by fitting a Gaussian curve to the 20230180-03 profile signal around each centroid, measuring peak width, and averaging this value over all observed centroids. Then, for each isotopic distribution, a Gaussian curve may be scaled to each expected centroid m/z and intensity data point, and summed amongst all expected isotopic peaks, with zero intensity elsewhere. The remaining p-values measure divergence between observed (vertical line at 918) and expected centroid peaks (crosses at 920) as well as the amount of confounding centroid peaks within the area of isotopic peaks that are not expected. All of these calculations are weighted as disclosed herein with respect to Figure 6 such that evidence closer to the most abundant isotopes over evidence from less abundant isotopes are trusted, where the classifier may be utilized for less abundant and partially overlapping isotopic peak clusters. [0064] Figure 10 illustrates top-down deconvolution benchmarking to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0065] Referring to Figure 10, top-down deconvolution benchmarking is shown at 1000, with 88% sequence coverage shown at 1002. [0066] Figure 11 illustrates users set preferences to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0067] Referring to Figure 11, user set preferences are shown at 1100. In this regard, target information, m/z tolerance (ppm), minimum ion matching confidence, fragmentation, and iterative matching preferences be entered and modified as shown. Based on the user set preferences at 1100, the match settings 1104 based on the user set preferences at 1100 may be locked as shown at 1102. In this regard, the match settings such as mass tolerance, ion types, etc., may be automatically (e.g., without human intervention) set. 20230180-03 [0068] Figure 12 illustrates parameter-free deconvolution to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0069] Referring to Figure 12, with respect to parameter-free deconvolution as shown at 1200, in order to analyze accuracy over a larger data set, based on execution of ExDViewer (e.g., software that provides a comprehensive overview on the observed fragment ions matching to a given protein sequence) with default settings, all ions with a score of 1.5 or higher may be considered after three rounds of scoring to detect overlapping isotope distributions. With respect to, for example, over 6 peptides (e.g., at 1202, 1204, etc.) and 2 intact proteins (e.g., at 1206 and 1208, and both from direct infusion and liquid chromatographic-separation), from multiple instruments, the apparatus 100 achieved >99% accuracy of m/z and charge from resulting ion predictions. Based on evaluation of > 10,000 putative ions, the apparatus 100 reliably identifies constellations of satellite ion types with robust likelihood scores. [0070] Figure 13 illustrates deconvolution and de-charging in ExDViewer to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0071] Referring to Figure 13, with respect to deconvolution and de-charging in ExDViewer, a raw unlabeled spectrum is shown at 1300, a de-charged (deconvoluted) spectrum is shown at 1302, a labeled spectrum is shown at 1304, and a de-isotoped spectrum is shown at 1306. [0072] Figure 14 illustrates hybrid targeted/untargeted deconvolution to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0073] Referring to Figure 14, with respect to hybrid targeted/untargeted 20230180-03 deconvolution as shown at 1400, for a charge 20+ precursor, ppm tolerance is greater than 1/charge, every ppm tolerance bin may contain at least 1 centroid, and profile data may provide for reliable identification. A charge 4+ y ion may be matched to an exact isotopic distribution generated from a targeted search. An unassigned charge 20+ precursor product may be assigned to averagine isotopic distribution. [0074] Figure 15 illustrates identification of ions in a MS/MS spectrum with ECD, and monitoring of ion charge distributions and efficiency to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0075] Referring to Figure 15, with respect to identification of ions in a MS/MS spectrum with ECD, and monitoring of ion charge distributions and efficiency, at 1500, tuning may be performed for full c/z ion coverage with collision energy added (e.g., CID+ECD). At 1502, tuning may be performed for extra efficiency, and yield a different distribution of charge states and side chain losses (ECD only). [0076] Figure 16 illustrates ion identification to illustrate operation of the apparatus 100, in accordance with an example of the present disclosure. [0077] Referring to Figure 16, the apparatus 100 may include, for example, >99% accuracy, and >80% sensitivity on complex intact mAb MS1 data. In this regard, based on receipt of a spectrum, the apparatus 100 may, in a fraction of a second, accurately identify a relatively high number of ions. [0078] Figures 17-19 respectively illustrate an example block diagram 1700, a flowchart of an example method 1800, and a further example block diagram 1900 for deconvolution by visual acuity-based interpretation of MS and MS/MS spectra, according 20230180-03 to examples. The block diagram 1700, the method 1800, and the block diagram 1900 may be implemented on the apparatus 100 described above with reference to Figure 1 by way of example and not of limitation. The block diagram 1700, the method 1800, and the block diagram 1900 may be practiced in other apparatus. In addition to showing the block diagram 1700, Figure 17 shows hardware of the apparatus 100 that may execute the instructions of the block diagram 1700. The hardware may include a processor 1702, and a memory 1704 storing machine readable instructions that when executed by the processor cause the processor to perform the instructions of the block diagram 1700. The memory 1704 may represent a non-transitory computer readable medium. Figure 18 may represent an example method for deconvolution by visual acuity-based interpretation of MS and MS/MS spectra, and the steps of the method. Figure 19 may represent a non-transitory computer readable medium 1902 having stored thereon machine readable instructions to provide deconvolution by visual acuity-based interpretation of MS and MS/MS spectra according to an example. The machine readable instructions, when executed, cause a processor 1904 to perform the instructions of the block diagram 1900 also shown in Figure 19. [0079] The processor 1702 of Figure 17 and/or the processor 1904 of Figure 19 may include a single or multiple processors or other hardware processing circuit, to execute the methods, functions and other processes described herein. These methods, functions and other processes may be embodied as machine readable instructions stored on a computer readable medium, which may be non-transitory (e.g., the non-transitory computer readable medium 1902 of Figure 19), such as hardware storage devices (e.g., RAM (random access memory), ROM (read only memory), EPROM (erasable, 20230180-03 programmable ROM), EEPROM (electrically erasable, programmable ROM), hard drives, and flash memory). The memory 1704 may include a RAM, where the machine readable instructions and data for a processor may reside during runtime. [0080] Referring to Figures 1-17, and particularly to the block diagram 1700 shown in Figure 17, the memory 1704 may include instructions 1706 to receive, for an ion 104 that is be identified, a plurality of expected ion spectra 106. Each expected ion spectrum 108 of the expected ion spectra 106 may include at least one expected peak profile 110. [0081] The processor 1702 may fetch, decode, and execute the instructions 1708 to receive, for the ion 104 that is be identified, an observed ion spectrum 112 including at least one observed peak profile 114. [0082] The processor 1702 may fetch, decode, and execute the instructions 1710 to identify, based on an analysis of the at least one expected peak profile 110 for each expected ion spectrum of the expected ion spectra 106 and the at least one observed peak profile for the observed ion spectrum 112, characteristics 116 of the expected ion spectra 106 and the observed ion spectrum 112. [0083] The processor 1702 may fetch, decode, and execute the instructions 1712 to determine, based on an analysis of the identified characteristics 116 of the expected ion spectra 106 and the observed ion spectrum 112, ion scores 120. [0084] The processor 1702 may fetch, decode, and execute the instructions 1714 to identify, from the ion scores 120, a highest ion score 124. [0085] The processor 1702 may fetch, decode, and execute the instructions 1716 to identify, based on the highest ion score 124, an expected ion spectrum 126 corresponding 20230180-03 to the highest ion score 124. [0086] The processor 1702 may fetch, decode, and execute the instructions 1718 to generate, based on the identification of the expected ion spectrum 126 corresponding to the highest ion score 124, an indication 130 and/or a graphical user interface display 132 of the expected ion spectrum 126 corresponding to the highest ion score 124. [0087] Referring to Figures 1-16 and 18, and particularly Figure 18, for the method 1800, at block 1802, the method may include receiving, for an ion 104 that is be identified, an expected ion spectrum including at least one expected peak profile. [0088] At block 1804, the method 1800 may include receiving, for the ion 104 that is be identified, an observed ion spectrum 112 including at least one observed peak profile 114. [0089] At block 1806, the method 1800 may include identifying, based on an analysis of the at least one expected peak profile for the expected ion spectrum and the at least one observed peak profile for the observed ion spectrum 112, characteristics of the expected ion spectrum and the observed ion spectrum 112. [0090] At block 1808, the method 1800 may include determining, based on an analysis of the identified characteristics of the expected ion spectrum and the observed ion spectrum, an ion score. [0091] According to examples disclosed herein, the method 1800 may further include generating, at least one of an indication or a graphical user interface display of the expected ion spectrum and the ion score. [0092] According to examples disclosed herein, the method 1800 may further include 20230180-03 determining, based on comparison of the ion score to at least one threshold range, a type of match of the expected ion spectrum to the observed ion spectrum. [0093] Referring to Figures 1-16 and 19, and particularly Figure 19, for the block diagram 1900, the non-transitory computer readable medium 1902 may include instructions 1906 to receive, based on input at a graphical user interface display, user set preferences. [0094] The processor 1904 may fetch, decode, and execute the instructions 1908 to receive, for an ion 104 that is be identified, a plurality of expected ion spectra 106. Each expected ion spectrum 108 of the expected ion spectra 106 may include at least one expected peak profile 110. [0095] The processor 1904 may fetch, decode, and execute the instructions 1910 to receive, for the ion 104 that is be identified, an observed ion spectrum 112 including at least one observed peak profile 114. [0096] The processor 1904 may fetch, decode, and execute the instructions 1912 to determine, based on the received user set preferences, and an analysis of the at least one expected peak profile 110 for each expected ion spectrum 108 of the expected ion spectra 106 and the at least one observed peak profile for the observed ion spectrum 112, ion scores 120. [0097] The processor 1904 may fetch, decode, and execute the instructions 1914 to identify, from the ion scores 120, a highest ion score 124. [0098] The processor 1904 may fetch, decode, and execute the instructions 1916 to identify, based on the highest ion score 124, an expected ion spectrum 126 corresponding 20230180-03 to the highest ion score 124. [0099] What has been described and illustrated herein is an example along with some of its variations. The terms, descriptions and figures used herein are set forth by way of illustration only and are not meant as limitations. Many variations are possible within the spirit and scope of the subject matter, which is intended to be defined by the following claims -and their equivalents -in which all terms are meant in their broadest reasonable sense unless otherwise indicated.

Claims

20230180-03 What is claimed is: 1. An apparatus comprising: at least one hardware processor; an ion spectra analyzer, executed by the at least one hardware processor, to: receive, for an ion that is be identified, a plurality of expected ion spectra, each expected ion spectrum of the expected ion spectra including at least one expected peak profile; receive, for the ion that is be identified, an observed ion spectrum including at least one observed peak profile; and identify, based on an analysis of the at least one expected peak profile for each expected ion spectrum of the expected ion spectra and the at least one observed peak profile for the observed ion spectrum, characteristics of the expected ion spectra and the observed ion spectrum; an ion score generator, executed by the at least one hardware processor, to: determine, based on an analysis of the identified characteristics of the expected ion spectra and the observed ion spectrum, ion scores; an ion score analyzer, executed by the at least one hardware processor, to: identify, from the ion scores, a highest ion score; and identify, based on the highest ion score, an expected ion spectrum corresponding to the highest ion score; and 20230180-03 a visual results generator, executed by the at least one hardware processor, to: generate, based on the identification of the expected ion spectrum corresponding to the highest ion score, at least one of an indication or a graphical user interface display of the expected ion spectrum corresponding to the highest ion score. 2. The apparatus according to claim 1, wherein: the ion score analyzer is executed by the at least one hardware processor to: compare the highest ion score to at least one threshold range; and determine, based on the comparison of the highest ion score to the at least one threshold range, a type of match of the expected ion spectrum corresponding to the highest ion score to the observed ion spectrum; and the visual results generator is executed by the at least one hardware processor to: generate, based on the determination of the type of match of the expected ion spectrum corresponding to the highest ion score to the observed ion spectrum, at least one of another indication or another graphical user interface display of the determination of the type of match. 3. The apparatus according to claim 1, wherein the identified characteristics of the expected ion spectra include expected centroid mass/charge number of ions (m/z) and intensity values, expected profile signal, m/z parts per million (ppm) error, expected centroid standard deviation, and noise threshold. 20230180-03 4. The apparatus according to claim 1, wherein the identified characteristics of the observed ion spectrum include at least one observed centroid value, observed profile signal, and mass/charge number of ions (m/z) parts per million (ppm) error. 5. The apparatus according to claim 1, wherein the ion score generator is executed by the at least one hardware processor to determine, based on the analysis of the identified characteristics of the expected ion spectra and the observed ion spectrum, the ion scores by: determining the ion scores as a function of a chi squared p-value, a spearman correlation, a pearson p-value, and a noise probability. 6. The apparatus according to claim 5, wherein the ion score generator is executed by the at least one hardware processor to determine the ion scores as a function of the chi squared p-value by determining a probability of randomly observing a set of centroid mass/charge number of ions (m/z) and intensity values that match expected m/z parts per million (ppm) error and intensity standard deviation. 7. The apparatus according to claim 5, wherein the ion score generator is executed by the at least one hardware processor to determine the ion scores as a function of the spearman correlation by determining a rank-based correlation coefficient between a specified expected ion spectrum of the expected ion spectra and the observed ion 20230180-03 spectrum. 8. The apparatus according to claim 5, wherein the ion score generator is executed by the at least one hardware processor to determine the ion scores as a function of the pearson p-value by determining a probability of randomly observing a set of centroid mass/charge number of ions (m/z) and intensity values for the observed ion spectrum that linearly correlate to expected values with a higher pearson correlation coefficient for a specified expected ion spectrum of the expected ion spectra. 9. The apparatus according to claim 5, wherein the ion score generator is executed by the at least one hardware processor to determine the ion scores as a function of the noise probability by determining peak density as a function of intensity rank between a specified expected ion spectrum of the expected ion spectra and the observed ion spectrum. 10. A method for deconvolution by visual acuity-based interpretation of mass spectrometry (MS) and tandem mass spectrometry (MS/MS) spectra, the method comprising: receiving, by at least hardware processor, for an ion that is be identified, an expected ion spectrum including at least one expected peak profile; receiving, by the at least hardware processor, for the ion that is be identified, an observed ion spectrum including at least one observed peak profile; identifying, by the at least hardware processor, based on an analysis of the at least 20230180-03 one expected peak profile for the expected ion spectrum and the at least one observed peak profile for the observed ion spectrum, characteristics of the expected ion spectrum and the observed ion spectrum; and determining, by the at least hardware processor, based on an analysis of the identified characteristics of the expected ion spectrum and the observed ion spectrum, an ion score. 11. The method according to claim 10, further comprising: generating, by the at least hardware processor, at least one of an indication or a graphical user interface display of the expected ion spectrum and the ion score. 12. The method according to claim 10, further comprising: determining, by the at least hardware processor, based on comparison of the ion score to at least one threshold range, a type of match of the expected ion spectrum to the observed ion spectrum. 13. The method according to claim 10, wherein determining, by the at least hardware processor, based on the analysis of the identified characteristics of the expected ion spectrum and the observed ion spectrum, the ion score, further comprises: determining, by the at least hardware processor, the ion score as a function of a chi squared p-value, a spearman correlation, a pearson p-value, and a noise probability. 20230180-03 14. A non-transitory computer readable medium having stored thereon machine- readable instructions, the machine-readable instructions, when executed by at least one hardware processor, cause the at least one hardware processor to: receive, based on input at a graphical user interface display, user set preferences; receive, for an ion that is be identified, a plurality of expected ion spectra, each expected ion spectrum of the expected ion spectra including at least one expected peak profile; receive, for the ion that is be identified, an observed ion spectrum including at least one observed peak profile; determine, based on the received user set preferences, and an analysis of the at least one expected peak profile for each expected ion spectrum of the expected ion spectra and the at least one observed peak profile for the observed ion spectrum, ion scores; identify, from the ion scores, a highest ion score; and identify, based on the highest ion score, an expected ion spectrum corresponding to the highest ion score. 15. The non-transitory computer readable medium according to claim 14, wherein the machine-readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: 20230180-03 generate, based on the identification of the expected ion spectrum corresponding to the highest ion score, at least one of an indication or another graphical user interface display of the expected ion spectrum corresponding to the highest ion score. 16. The non-transitory computer readable medium according to claim 14, wherein the machine-readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: compare the highest ion score to at least one threshold range; and determine, based on the comparison of the highest ion score to the at least one threshold range, a type of match of the expected ion spectrum corresponding to the highest ion score to the observed ion spectrum. 17. The non-transitory computer readable medium according to claim 16, wherein the machine-readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: generate, based on the determination of the type of match of the expected ion spectrum corresponding to the highest ion score to the observed ion spectrum, at least one of an indication or another graphical user interface display of the determination of the type of match. 18. The non-transitory computer readable medium according to claim 14, wherein the machine-readable instructions, when executed by the at least one hardware processor, 20230180-03 further cause the at least one hardware processor to: identify characteristics of the expected ion spectra to include expected centroid mass/charge number of ions (m/z) and intensity values, expected profile signal, m/z parts per million (ppm) error, expected centroid standard deviation, and noise threshold. 19. The non-transitory computer readable medium according to claim 14, wherein the machine-readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: identify characteristics of the observed ion spectrum to include at least one observed centroid value, observed profile signal, and mass/charge number of ions (m/z) parts per million (ppm) error. 20. The non-transitory computer readable medium according to claim 14, wherein the machine-readable instructions to determine, based on the analysis of the at least one expected peak profile for each expected ion spectrum of the expected ion spectra and the observed ion spectrum, the ion scores, when executed by the at least one hardware processor, further cause the at least one hardware processor to: determine the ion scores as a function of a chi squared p-value, a spearman correlation, a pearson p-value, and a noise probability.