Peptide Tandem Mass Spectra Research Articles

Test-Time Training for Deep MS/MS Spectrum Prediction Improves Peptide Identification.

In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, enabling the calculation of similarity scores between the predicted and experimental spectra for peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard data sets and directly employs the model on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To tackle this problem, we developed a test-time training paradigm that adapts the pretrained model to generate experimental data-specific models, namely, PepT3. PepT3 yields a 10-40% increase in peptide identification depending on the variability in training and experimental data. Intriguingly, when applied to a patient-derived immunopeptidomic sample, PepT3 increases the identification of tumor-specific immunopeptide candidates by 60%. Two-thirds of the newly identified candidates are predicted to bind to the patient's human leukocyte antigen isoforms. To facilitate access of the model and all the results, we have archived all the intermediate files in Zenodo.org with identifier 8231084.

Spectroscape enables real-time query and visualization of a spectral archive in proteomics

In proteomics, spectral archives organize the enormous amounts of publicly available peptide tandem mass spectra by similarity, offering opportunities for error correction and novel discoveries. Here we adapt an indexing algorithm developed by Facebook for organizing online multimedia resources to tandem mass spectra and achieve practically instantaneous retrieval and clustering of approximate nearest neighbors in a large spectral archive. An interactive web-based graphical user interface enables the user to view a query spectrum in its clustered neighborhood, which facilitates contextual validation of peptide identifications and exploration of the dark proteome.

Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics.

In shotgun proteomics, false discovery rate (FDR) estimation is a necessary step to ensure the quality of accepted peptide-spectrum matches (PSMs) from a database search. Popular statistical validation tools for FDR control tend to rely on target-decoy searching to build empirical, dataset-specific models, which often leads to inaccurate FDR estimates. In this paper, we propose a new approach named common decoy distribution (CDD) to FDR estimation using the idea of a fixed empirical null score distribution derived from millions of peptide tandem mass spectra. To demonstrate the viability of CDD, its stability with respect to noise and the presence of unexpected peptide modifications was evaluated. PeptideProphet-based implementation of CDD was benchmarked against decoy-based PeptideProphet, and both methods exhibited similar accuracy of FDR estimates and retrieval of correct PSMs. The finding of this study calls for a re-evaluation of the necessity of dataset-specific target-decoy searches and illustrates the potential of Big Data approaches for statistical analysis in proteomics.

SPEQ: quality assessment of peptide tandem mass spectra with deep learning.

MotivationIn proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may not be identified due to incompleteness of the database or failure of the software. Thus, spectrum quality (SPEQ) assessment tools are helpful programs that can eliminate poor-quality spectra before the database search and highlight the high-quality spectra that are not identified in the initial search. These spectra may be valuable candidates for further analyses.ResultsWe propose SPEQ: a spectrum quality assessment tool that uses a deep neural network to classify spectra into high-quality, which are worthy candidates for interpretation, and low-quality, which lack sufficient information for identification. SPEQ was compared with a few other prediction models and demonstrated improved prediction accuracy.Availability and implementationSource code and scripts are freely available at github.com/sor8sh/SPEQ, implemented in Python.

MS_Piano: A Software Tool for Annotating Peaks in CID Tandem Mass Spectra of Peptides and N-Glycopeptides.

Annotating product ion peaks in tandem mass spectra is essential for evaluating spectral quality and validating peptide identification. This task is more complex for glycopeptides and is crucial for the confident determination of glycosylation sites in glycoproteins. MS_Piano (Mass Spectrum Peptide Annotation) software was developed for reliable annotation of peaks in collision induced dissociation (CID) tandem mass spectra of peptides or N-glycopeptides for given peptide sequences, charge states, and optional modifications. The program annotates each peak in high or low resolution spectra with possible product ion(s) and the mass difference between the measured and theoretical m/z values. Spectral quality is measured by two major parameters: the ratio between the sum of unannotated vs all peak intensities in the top 20 peaks, and the intensity of the highest unannotated peak. The product ions of peptides, glycans, and glycopeptides in spectra are labeled in different class-type colors to facilitate interpretation. MS_Piano assists validating peptide and N-glycopeptide identification from database and library searches and provides quality control and optimizes search reliability in custom developed peptide mass spectral libraries. The software is freely available in .exe and .dll formats for the Windows operating system.

Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification.

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

The Journey Is the Reward, a Taoist Proverb: John B. Fenn Award for Distinguished Contribution in Mass Spectrometry Lecture.

This account provided by John Yates describes his incredible path as a scientist, emphasizing key decisions along the way that shaped his career and led to his landmark contributions to the field of mass spectrometry. Although perhaps best known is the development of the SEQUEST algorithm for automated interpretation of tandem mass spectra of peptides, John's achievements have spanned the field of proteomics and had major impact on the ability to address and solve significant biological problems.

Assessing Protein Sequence Database Suitability Using De Novo Sequencing.

The analysis of samples from unsequenced and/or understudied species as well as samples where the proteome is derived from multiple organisms poses two key questions. The first is whether the proteomic data obtained from an unusual sample type even contains peptide tandem mass spectra. The second question is whether an appropriate protein sequence database is available for proteomic searches. We describe the use of automated de novo sequencing for evaluating both the quality of a collection of tandem mass spectra and the suitability of a given protein sequence database for searching that data. Applications of this method include the proteome analysis of closely related species, metaproteomics, and proteomics of extinct organisms.

Interactive Peptide Spectral Annotator: A Versatile Web-based Tool for Proteomic Applications

Here we present IPSA, an innovative web-based spectrum annotator that visualizes and characterizes peptide tandem mass spectra. A tool for the scientific community, IPSA can visualize peptides collected using a wide variety of experimental and instrumental configurations. Annotated spectra are customizable via a selection of interactive features and can be exported as editable scalable vector graphics to aid in the production of publication-quality figures. Single spectra can be analyzed through provided web forms, whereas data for multiple peptide spectral matches can be uploaded using the Proteomics Standards Initiative file formats mzTab, mzIdentML, and mzML. Alternatively, peptide identifications and spectral data can be provided using generic file formats. IPSA provides supports for annotating spectra collecting using negative-mode ionization and facilitates the characterization of experimental MS/MS performance through the optional export of fragment ion statistics from one to many peptide spectral matches. This resource is made freely accessible at http://interactivepeptidespectralannotator.com, whereas the source code and user guides are available at https://github.com/coongroup/IPSA for private hosting or custom implementations.

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.

In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating Prosit into database search pipelines led to more identifications at >10× lower false discovery rates. We show the general applicability of Prosit by predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone.

PTMiner: Localization and Quality Control of Protein Modifications Detected in an Open Search and Its Application to Comprehensive Post-translational Modification Characterization in Human Proteome*[S

The open (mass tolerant) search of tandem mass spectra of peptides shows great potential in the comprehensive detection of post-translational modifications (PTMs) in shotgun proteomics. However, this search strategy has not been widely used by the community, and one bottleneck of it is the lack of appropriate algorithms for automated and reliable post-processing of the coarse and error-prone search results. Here we present PTMiner, a software tool for confident filtering and localization of modifications (mass shifts) detected in an open search. After mass-shift-grouped false discovery rate (FDR) control of peptide-spectrum matches (PSMs), PTMiner uses an empirical Bayesian method to localize modifications through iterative learning of the prior probabilities of each type of modification occurring on different amino acids. The performance of PTMiner was evaluated on three data sets, including simulated data, chemically synthesized peptide library data and modified-peptide spiked-in proteome data. The results showed that PTMiner can effectively control the PSM FDR and accurately localize the modification sites. At 1% real false localization rate (FLR), PTMiner localized 93%, 84 and 83% of the modification sites in the three data sets, respectively, far higher than two open search engines we used and an extended version of the Ascore localization algorithm. We then used PTMiner to analyze a draft map of human proteome containing 25 million spectra from 30 tissues, and confidently identified over 1.7 million modified PSMs at 1% FDR and 1% FLR, which provided a system-wide view of both known and unknown PTMs in the human proteome.

Matchmaking in Proteomics.

Featured Article: Eng JK, McCormack AL, Yates JR. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 1994;5:976–89. 2 Our 1994 report established a fast and accurate method to interpret the tandem mass spectra of peptides. In the early 1960s, as mass spectrometers became important tools for the elucidation of molecular structures, particularly in the natural products area, there was an interest in applying the technology to the challenge of protein sequence analysis. This task presented many technical barriers, including how to get polar, zwitterionic molecules into the gas phase inside a mass spectrometer. Many of the early solutions to this problem, such as extensive chemical modification of peptides, were elegant but not particularly practical. It was not until new ionization techniques were discovered did it become easier to introduce peptides and proteins into the gas phase, which allowed substantial progress to be made. The invention of the tandem mass …

The Hybrid Search: A Mass Spectral Library Search Method for Discovery of Modifications in Proteomics.

We present a mass spectral library-based method to identify tandem mass spectra of peptides that contain unanticipated modifications and amino acid variants. We describe this as a "hybrid" method because it combines matching both ion m/z and mass losses. The mass loss is the difference between the mass of an ion peak and the mass of its precursor. This difference, termed DeltaMass, is used to shift the product ions in the library spectrum that contain the modification, thereby allowing library product ions that contain the unexpected modification to match the query spectrum. Clustered unidentified spectra from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Chinese hamster ovary cells were used to evaluate this method. The results demonstrate the ability of the hybrid method to identify unanticipated modifications, insertions, and deletions, which may include those due to an incomplete protein sequence database or to search settings that exclude the correct identification, in high-resolution tandem mass spectra without regard to their precursor mass. This has been made possible by indexing of the m/z value of each fragment ion and its difference in mass from its precursor ion.

Doubling down on phosphorylation as a variable peptide modification.

Some mass spectrometrists believe that searching for variable PTMs like phosphorylation of serine or threonine when using database-search algorithms to interpret peptide tandem mass spectra will increase false-positive matching. The basis for this is the premise that the algorithm compares a spectrum to both a nonphosphorylated peptide candidate and a phosphorylated candidate, which is double the number of candidates compared to a search with no possible phosphorylation. Hence, if the search space doubles, false-positive matching could increase accordingly as the algorithm considers more candidates to which false matches could be made. In this study, it is shown that the search for variable phosphoserine and phosphothreonine modifications does not always double the search space or unduly impinge upon the FDR. A breakdown of how one popular database-search algorithm deals with variable phosphorylation is presented.

Differentiating samples and experimental protocols by direct comparison of tandem mass spectra.

Peptide tandem mass spectra can be analyzed by a number of means. They can be compared against predicted spectra of peptides derived from genome sequences, compared against previously acquired and identified spectra, or - sometimes - sequenced de novo. We recently introduced another method which compares spectra between liquid chromatography/tandem mass spectrometry (LC/MS/MS) datasets to determine the shared spectral content, and demonstrated how this can be applied in a molecular phylogenetic study using sera from human and non-human primates. We will here explore if such a method have other, serendipitous uses. We used the existing compareMS2 algorithm without modification on a diverse set of experiments. First we conducted a small phylogenetic study, using (mammalian) bone samples to study old material, and human pathogens aiming to distinguish clinically important strains. Although not as straightforward as primate sera analysis, the method shows significant promise for all these applications. We also used the algorithm to compare 24 different protocols for extraction of proteins from muscle tissue. The results provided useful information in comparing protocols. Finally, we applied compareMS2 aiming for quality control of two traceable protein reference standards (troponin) used in clinical chemistry assays, by analysing the effect of storage conditions. The results illustrate a broad applicability of the metric based on shared tandem mass spectra between LC/MS/MS datasets for analysing protein digests in different types of experiments. There is no reason to assume that our instance of this method is optimal in any of these situations, as it makes limited or no use of accurate mass and chromatographic retention time. We propose that with further improvement and refinement, this type of analysis can be applied as a simple but informative first step in many pipelines for bottom-up tandem mass spectrometry data analysis in proteomics and other fields, comparing or analysing large numbers of samples or datasets.

Identification of meat products by shotgun spectral matching

A new method, based on shotgun spectral matching of peptide tandem mass spectra, was successfully applied to the identification of different food species. The method was demonstrated to work on raw as well as processed samples from 16 mammalian and 10 bird species by counting spectral matches to spectral libraries in a reference database with one spectral library per species. A phylogenetic tree could also be constructed directly from the spectra. Nearly all samples could be correctly identified at the species level, and 100% at the genus level. The method does not use any genomic information and unlike targeted methods, no prior knowledge of genetic variation within a genus or species is necessary.

Annotating and Interpreting Linear and Cyclic Peptide Tandem Mass Spectra.

Nonribosomal peptides often possess pronounced bioactivity, and thus, they are often interesting hit compounds in natural product-based drug discovery programs. Their mass spectrometric characterization is difficult due to the predominant occurrence of non-proteinogenic monomers and, especially in the case of cyclic peptides, the complex fragmentation patterns observed. This makes nonribosomal peptide tandem mass spectra annotation challenging and time-consuming. To meet this challenge, software tools for this task have been developed. In this chapter, the workflow for using the software mMass for the annotation of experimentally obtained peptide tandem mass spectra is described. mMass is freely available (http://www.mmass.org), open-source, and the most advanced and user-friendly software tool for this purpose. The software enables the analyst to concisely annotate and interpret tandem mass spectra of linear and cyclic peptides. Thus, it is highly useful for accelerating the structure confirmation and elucidation of cyclic as well as linear peptides and depsipeptides.

An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis.

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and metabolism. Although powerful, metagenomics alone can only elucidate functional potential. On the other hand, metaproteomics enables the description of the expressed in situ metabolism and function of a community. Here we describe a protocol for cell lysis, protein and DNA isolation, as well as peptide digestion and extraction from marine microbial cells collected on a cartridge filter unit (such as the Sterivex filter unit) and preserved in an RNA stabilization solution (like RNAlater). In mass spectrometry-based proteomics studies, the identification of peptides and proteins is performed by comparing peptide tandem mass spectra to a database of translated nucleotide sequences. Including the metagenome of a sample in the search database increases the number of peptides and proteins that can be identified from the mass spectra. Hence, in this protocol DNA is isolated from the same filter, which can be used subsequently for metagenomic analysis.

The H-Index of ‘An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database’

Over 20years ago a remarkable paper was published in the Journal of American Society for Mass Spectrometry. This paper from Jimmy Eng, Ashley McCormack, and John Yates described the use of protein databases to drive the interpretation of tandem mass spectra of peptides. This paper now has over 3660 citations and continues to average more than 260 per year over the last decade. This is an amazing scientific achievement. The reason for this is the paper was a cutting edge development at the moment in time when genomes of organisms were being sequenced, protein and peptide mass spectrometry was growing into the field of proteomics, and the power of computing was growing quickly in accordance with Moore's law. This work by the Yates lab grew in importance as genomics, proteomics, and computation all advanced and eventually resulted in the widely used SEQUEST algorithm and platform for the analysis of tandem mass spectrometry data. This commentary provides an analysis of the impact of this paper by analyzing the citations it has generated and the impact of these citing papers. Graphical Abstract ᅟ.

Specific tandem mass spectrometric detection of AGE-modified arginine residues in peptides.

Glycation is a non-enzymatic reaction of protein amino and guanidino groups with reducing sugars or dicarbonyl products of their oxidative degradation. Modification of arginine residues by dicarbonyls such as glyoxal and methylglyoxal results in formation of advanced glycation end-products (AGEs). In mammals, these modifications impact in diabetes mellitus, uremia, atherosclerosis and ageing. However, due to the low abundance of individual AGE-peptides in enzymatic digests, these species cannot be efficiently detected by LC-ESI-MS-based data-dependent acquisition (DDA) experiments. Here we report an analytical workflow that overcomes this limitation. We describe fragmentation patterns of synthetic AGE-peptides and assignment of modification-specific signals required for unambiguous structure retrieval. Most intense signals were those corresponding to unique fragment ions with m/z 152.1 and 166.1, observed in the tandem mass spectra of peptides, containing glyoxal- and methylglyoxal-derived hydroimidazolone AGEs, respectively. To detect such peptides, specific and sensitive precursor ion scanning methods were established for these signals. Further, these precursor ion scans were incorporated in conventional bottom-up proteomic approach based on data-dependent acquisition (DDA) LC-MS/MS experiments. The method was successfully applied for the analysis of human serum albumin (HSA) and human plasma protein tryptic digest with subsequent structure confirmation by targeted LC-MS/MS (DDA). Altogether 44 hydroimidazolone- and dihydroxyimidazolidine-derived peptides representing 42 AGE-modified proteins were identified in plasma digests obtained from type 2 diabetes mellitus (T2DM) patients.