Peptide-spectrum Matches Research Articles

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows.

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome.

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

Exact p-value calculation for XCorr scoring of high-resolution MS/MS data.

Exact p-value (XPV)-based methods for dot product-like score functions-such as the XCorr score implemented in Tide, SEQUEST, Comet or shared peak count-based scoring in MSGF+ and ASPV-provide a fairly good calibration for peptide-spectrum-match (PSM) scoring in database searching-based MS/MS spectrum data identification. Unfortunately, standard XPV methods, in practice, cannot handle high-resolution fragmentation data produced by state-of-the-art mass spectrometers because having smaller bins increases the number of fragment matches that are assigned to incorrect bins and scored improperly. In this article, we present an extension of the XPV method, called the high-resolution exact p-value (HR-XPV) method, which can be used to calibrate PSM scores of high-resolution MS/MS spectra obtained with dot product-like scoring such as the XCorr. The HR-XPV carries remainder masses throughout the fragmentation, allowing them to greatly increase the number of fragments that are properly assigned to the correct bin and, thus, taking advantage of high-resolution data. Using four mass spectrometry data sets, our experimental results demonstrate that HR-XPV produces well-calibrated scores, which in turn results in more trusted spectrum annotations at any false discovery rate level.

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides.

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html. Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

Formalin Fixation Followed by Paraffin Embedding Allows Long-Term Storage of Proteins for Liquid Chromatography-Tandem Mass Spectrometry Analysis

In an anatomical pathology laboratory, liquid chromatography-tandem mass spectrometry (LC-MS/MS) is used to characterize amyloid deposits identified in formalin-fixed paraffin-embedded tissue (FFPET). However, the development of additional tests is partially limited by the lack of information the passage of time has on the proteins in FFPET. To investigate the reliability of LC-MS/MS in the analysis of old FFPET specimens, 1 bone marrow aspirate clot was analyzed by LC-MS/MS yearly from 2014 to 2018, in 3 consecutive months. Peptide-spectrum match, number of peptides identified, and percentage of the proteins covered were the parameters collected for the hemoglobin subunits alpha (HbA), beta (HbB), delta (HbD), and gamma (HbG). These proteins are constant components of the peripheral blood and are present in high and low abundance, allowing the monitorization of the performance of the test across varying protein concentrations. The hemoglobin subunits were stable over the years studied; 71% to 74% of HbA, 77% to 80% of HbB, 69% to 77% of HbD, and 57% to 63% of HbG were covered, with no statistical difference between 2014 and 2018. The number of peptides identified was also constant, 11 to 13 for HbA, 13 to 15 for HbB, 11 to 14 for HbD, and 7 to 9 for HbG. Peptide spectrum match was only slightly more variable: 209 to 327 for HbA, 569 to 1052 for HbB, 286 to 533 HbD, and 142 to 292 for HbG. In conclusion, high abundance hemoglobins, HbA and HbB, and relatively low abundance ones, HbD and HbG, are preserved in FFPET and confidently identified by LC-MS/MS for at least 5 years.

Integrated Mass Spectrometry‐based Pipeline for Characterization of Tau in Human Alzheimer’s Brain: Development and Comparison of Sample Preparation Methods

AbstractBackgroundTau protein aggregation into neurofibrillary tangles in the brain is a pathological hallmark of Alzheimer’s disease (AD) and related tauopathies [1]. Though the mechanism of tau destabilization is not fully understood, tau protein exhibits several allele‐specific isoforms and post‐translational modifications (PTMs) specific to tau aggregates [2]. To explore in‐depth tau species in brain tissues of AD patients, we developed novel pipelines based on different tau fractions retrieved by buffer solubility, Filter Aided Sample Preparation (FASP) or immunoprecipitation, before high‐resolution mass spectrometry (HRMS) analysis.MethodAs a proof of concept, we used frozen brain samples from two AD patients (0.6‐1.2 g per replicate, middle frontal gyrus). The samples were divided and homogenized, ultracentrifuged at 180.000xg for 30min and solubilized in different buffers successively (2 replicates): (1) SarkMethodlow‐salt buffer, 10% Triton X‐100, sarkosyl buffer, and urea buffer; (2) RIPAMethodhigh‐salt buffer (twice), RIPA buffer and urea buffer; and, (3) GnHMethodguanidine hydrochloride and urea buffer. Then, we performed an antibody‐free approach by FASP or immunoprecipitation with a panel of commercial tau antibodies and analyzed on reversed‐phase capillary liquid chromatography, coupled with a qExactive HRMS.ResultHRMS demonstrated that the RIPA method performed better than the other methods for Tau analysis: coverage of 63% (full‐length, 2N4R) and 342 tau peptide‐spectrum matches (PSMs) (Sark: 49% and 183 PSMs; GnH: 41% and 169 PSMs). PTM‐modified tau species (phosphorylated, methylated, and acetylated) were primarily seen using the RIPA method (59 PSMs; Sark: 19 PSMs, GnH: 21 PSMs). Immunoprecipitation improved tau coverage and PTM detection compared to FASP.ConclusionThese findings highlight the best performance of the RIPA method followed by immunoprecipitation for the study of AD brain tau allele‐specific isoforms and PTMs using HRMS. Better characterization of Tau molecular profile in the brain of AD patients opens the gate for a better understanding of AD pathogenesis and represents an opportunity in the biomarker field.[1] Mroczko, B. et al. Int J Mol Sci 2019, 20, E4661, https://doi.org/10.3390/ijms20194661.[2] Boyarko, B.; Hook, V. Front. Neurosci. 2021, 15, 702788, https://doi.org/10.3389/fnins.2021.702788.

Assessing Multiple Evidence Streams to Decide on Confidence for Identification of Post-Translational Modifications, within and Across Data Sets.

Phosphorylation is a post-translational modification of great interest to researchers due to its relevance in many biological processes. LC-MS/MS techniques have enabled high-throughput data acquisition, with studies claiming identification and localization of thousands of phosphosites. The identification and localization of phosphosites emerge from different analytical pipelines and scoring algorithms, with uncertainty embedded throughout the pipeline. For many pipelines and algorithms, arbitrary thresholding is used, but little is known about the actual global false localization rate in these studies. Recently, it has been suggested to use decoy amino acids to estimate global false localization rates of phosphosites, among the peptide-spectrum matches reported. Here, we describe a simple pipeline aiming to maximize the information extracted from these studies by objectively collapsing from peptide-spectrum match to the peptidoform-site level, as well as combining findings from multiple studies while maintaining track of false localization rates. We show that the approach is more effective than current processes that use a simpler mechanism for handling phosphosite identification redundancy within and across studies. In our case study using eight rice phosphoproteomics data sets, 6368 unique sites were confidently identified using our decoy approach compared to 4687 using traditional thresholding in which false localization rates are unknown.

Shotgun Proteomics Analysis, Functional Networks, and Peptide Biomarkers for Seafood-Originating Biogenic-Amine-Producing Bacteria.

Biogenic amine-producing bacteria are responsible for the production of basic nitrogenous compounds (histamine, cadaverine, tyramine, and putrescine) following the spoilage of food due to microorganisms. In this study, we adopted a shotgun proteomics strategy to characterize 15 foodborne strains of biogenic-amine-producing bacteria. A total of 10,673 peptide spectrum matches belonging to 4081 peptides and corresponding to 1811 proteins were identified. Relevant functional pathways were determined, and strains were differentiated into hierarchical clusters. An expected protein-protein interaction network was created (260 nodes/1973 interactions). Most of the determined proteins were associated with networks/pathways of energy, putrescine metabolism, and host-virus interaction. Additionally, 556 peptides were identified as virulence factors. Moreover, 77 species-specific peptide biomarkers corresponding to 64 different proteins were proposed to identify 10 bacterial species. This represents a major proteomic dataset of biogenic-amine-producing strains. These results may also be suitable for new treatments for food intoxication and for tracking microbial sources in foodstuffs.

Identification of 113 new histone marks by CHiMA, a tailored database search strategy.

Shotgun proteomics has been widely used to identify histone marks. Conventional database search methods rely on the "target-decoy" strategy to calculate the false discovery rate (FDR) and distinguish true peptide-spectrum matches (PSMs) from false ones. This strategy has a caveat of inaccurate FDR caused by the small data size of histone marks. To address this challenge, we developed a tailored database search strategy, named "Comprehensive Histone Mark Analysis (CHiMA)." Instead of target-decoy-based FDR, this method uses "50% matched fragment ions" as the key criterion to identify high-confidence PSMs. CHiMA identified twice as many histone modification sites as the conventional method in benchmark datasets. Reanalysis of our previous proteomics data using CHiMA led to the identification of 113 new histone marks for four types of lysine acylations, almost doubling the number of previously reported marks. This tool not only offers a valuable approach for identifying histone modifications but also greatly expands the repertoire of histone marks.

Modeling Lower-Order Statistics to Enable Decoy-Free FDR Estimation in Proteomics.

One of the chief objectives in mass spectrometry-based peptide identification in proteomics is the statistical validation of top-scoring peptide-spectrum matches (PSMs) in the form of false discovery rate (FDR) estimation. Existing methods construct a null model that captures the characteristics of incorrect target PSMs to estimate the FDR, most often with the help of decoys. Decoy-based methods, however, increase the computational cost and rely on the difficult-to-verify assumption that decoy PSMs constitute a sufficient and representative sample of the population of possible incorrect target PSMs. On the other hand, the possibility of FDR estimation assisted by the plentiful non-top-scoring PSMs, which are almost always incorrect, has been scarcely explored. In this work, we propose a novel decoy-free procedure for developing null models for top-scoring PSMs using the transformed e-value (TEV) score and the distributions of non-top-scoring target PSMs. The method relies on a theoretically derivable relationship between the parameters of the distributions of lower-order statistics of the TEV score and a necessary empirical optimization to fit a single parameter to actual data. The framework was tested on multiple different data sets and two search engines. We present evidence that our method is comparable to and occasionally outperforms popular decoy-free and decoy-based methods in FDR estimation.

OpenCustomDB: Integration of Unannotated Open Reading Frames and Genetic Variants to Generate More Comprehensive Customized Protein Databases.

Proteomic diversity in biological samples can be characterized by mass spectrometry (MS)-based proteomics using customized protein databases generated from sets of transcripts previously detected by RNA-seq. This diversity has only been increased by the recent discovery that many translated alternative open reading frames rest unannotated at unsuspected locations of mRNAs and ncRNAs. These novel protein products, termed alternative proteins, have been left out of all previous custom database generation tools. Consequently, genetic variations that impact alternative open reading frames and variant peptides from their translated proteins are not detectable with current computational workflows. To fill this gap, we present OpenCustomDB, a bioinformatics tool that uses sample-specific RNaseq data to identify genomic variants in canonical and alternative open reading frames, allowing for more than one coding region per transcript. In a test reanalysis of a cohort of 16 patients with acute myeloid leukemia, 5666 peptides from alternative proteins were detected, including 201 variant peptides. We also observed that a significant fraction of peptide-spectrum matches previously assigned to peptides from canonical proteins got better scores when reassigned to peptides from alternative proteins. Custom protein libraries that include sample-specific sequence variations of all possible open reading frames are promising contributions to the development of proteomics and precision medicine. The raw and processed proteomics data presented in this study can be found in PRIDE repository with accession number PXD029240.

DiagnoMass: A proteomics hub for pinpointing discriminative spectral clusters

MotivationThere are several well-established paradigms for identifying and pinpointing discriminative peptides/proteins using shotgun proteomic data; examples are peptide-spectrum matching, de novo sequencing, open searches, and even hybrid approaches. Such an arsenal of complementary paradigms can provide deep data coverage, albeit some unidentified discriminative peptides remain. ResultsWe present DiagnoMass, software tool that groups similar spectra into spectral clusters and then shortlists those clusters that are discriminative for biological conditions. DiagnoMass then communicates with proteomic tools to attempt the identification of such clusters. We demonstrate the effectiveness of DiagnoMass by analyzing proteomic data from Escherichia coli, Salmonella, and Shigella, listing many high-quality discriminative spectral clusters that had thus far remained unidentified by widely adopted proteomic tools. DiagnoMass can also classify proteomic profiles. We anticipate the use of DiagnoMass as a vital tool for pinpointing biomarkers. AvailabilityDiagnoMass and related documentation, including a usage protocol, are available at http://www.diagnomass.com.

Analysis and identification of suspected snake venom samples using nano-ultra-high performance liquid chromatography-high resolution mass spectrometry

Snake venom is a complex mixture secreted from the glands of poisonous snakes， which contains proteins， peptides， lipids， nucleosides， sugars， amino acids， amines， metal ions， and other components. According to the toxicological classification， snake venoms can be classified as neurotoxins， anticoagulants and procoagulant toxins， cardiac toxins， other toxin proteins， and enzymes. Proteins and peptides are the key components of snake venom. The establishment of rapid， accurate analysis and identification methods for proteins in snake venom is a prerequisite for snake venom-related forensic identification， intoxication events， and pharmaceutical development. Until now， the classical analysis and identification methods have mainly been biochemical or immunoassays for DNA or proteins， such as polymerase chain reaction， agglutination test， enzyme-linked immunosorbent assay， fluorescent immunoassay， and various biosensing approaches. These methods have some limitations such as a high false-positive ratio， low sensitivity， poor anti-interference ability， and limited species discrimination capability. In recent years， with the rapid development of mass spectrometry （MS） techniques， the proteomics of snake venom has also attracted much attention and has contributed to the identification of snake species， in which non-targeted and targeted proteomics represent two main divisions. However， species identification via proteomics is in its infancy in forensic science. First， the tandem MS spectra of peptide sequences are highly complex， which poses a great challenge for the strict and accurate matching of peptides based on the rational speculation of MS fragmentation rules and theoretical calculations in non-targeted proteomics. Second， for the confirmation and identification of unknown substances， reference substances are commonly needed， but those for snake venom are lacking. Proteomics in snake venom identification is still in progress to improve the identification confidence and clarify the identification rules. In this work， a method based on nano-ultra-high performance liquid chromatography-quadrupole-orbitrap high-resolution mass spectrometry （Nano LC-MS/HRMS） and size exclusion chromatography （SEC） was developed for identifying proteins and their source species， with strict rules for five suspected snake venom samples and their contamination in one case. Three SEC elution peaks were obtained from each of the five samples， which were lyophilized and treated with trypsin in solution， and then separated and analyzed by Nano LC-MS/HRMS. First， the Full MS/dd MS2 mode was used for the non-targeted acquisition of peptide information in the samples， and after submission to the Swiss-Prot database， the protein databases of Serpentes， Colubroidea， Elapidae， Elapinae， and Naja were contracted stepwise and compared. A total of 32 proteins from Naja atra were identified under the conditions of both peptide spectrum match and false discovery rate less than 1%， and number of characteristic peptides greater than or equal to two. All of these were derived from ten families of Naja atra， mainly three-finger toxins， metalloproteinases， and phospholipase A2. Proteins D3TTC2， D5LMJ3， Q7T1K6， Q9DEQ3， and Q9YGI4 were the most common among the five samples. Finally， the parallel reaction monitoring mode was adopted to select two unique peptides for each protein for targeted verification. It was considered that a protein in the samples was truly identified when it met the strict standard "the Δm/z of at least 75% y+ and b+ ions of each unique peptide was less than 5 ppm". After these consequently procedures， we identified that all five samples contained the venom of the Naja atra. Our identification method is a systematic and strict example that can provide effective technical support for the forensic identification of snake venom poisoning， as well as for pharmaceutical development toward snake venoms.

Purification, Characterization and Bactericidal Action of Lysozyme, Isolated from Bacillus subtillis BSN314: A Disintegrating Effect of Lysozyme on Gram-Positive and Gram-Negative Bacteria.

In the present study, lysozyme was purified by the following multi-step methodology: salt (ammonium sulfate) precipitation, dialysis, and ultrafiltration. The lysozyme potential was measured by enzymatic activity after each purification step. However, after ultrafiltration, the resulting material was considered extra purified. It was concentrated in an ultrafiltration centrifuge tube, and the resulting protein/lysozyme was used to determine its bactericidal potential against five bacterial strains, including three gram-positive (Bacillus subtilis 168, Micrococcus luteus, and Bacillus cereus) and two gram-negative (Salmonella typhimurium and Pseudomonas aeruginosa) strains. The results of ZOI and MIC/MBC showed that lysozyme had a higher antimicrobial activity against gram-positive than gram-negative bacterial strains. The results of the antibacterial activity of lysozyme were compared with those of ciprofloxacin (antibiotic). For this purpose, two indices were applied in the present study: antimicrobial index (AMI) and percent activity index (PAI). It was found that the purified lysozyme had a higher antibacterial activity against Bacillus cereus (AMI/PAI; 1.01/101) and Bacillus subtilis 168 (AMI/PAI; 1.03/103), compared to the antibiotic (ciprofloxacin) used in this study. Atomic force microscopy (AFM) was used to determine the bactericidal action of the lysozyme on the bacterial cell. The purified protein was further processed by gel column chromatography and the eluate was collected, its enzymatic activity was 21.93 U/mL, while the eluate was processed by native-PAGE. By this analysis, the un-denatured protein with enzymatic activity of 40.9 U/mL was obtained. This step shows that the protein (lysozyme) has an even higher enzymatic potential. To determine the specific peptides (in lysozyme) that may cause the bactericidal potential and cell lytic/enzymatic activity, the isolated protein (lysozyme) was further processed by the SDS-PAGE technique. SDS-PAGE analysis revealed different bands with sizes of 34 kDa, 24 kDa, and 10 kDa, respectively. To determine the chemical composition of the peptides, the bands (from SDS-PAGE) were cut, enzymatically digested, desalted, and analyzed by LC-MS (liquid chromatography-mass spectrometry). LC-MS analysis showed that the purified lysozyme had the following composition: the number of proteins in the sample was 56, the number of peptides was 124, and the number of PSMs (peptide spectrum matches) was 309. Among them, two peptides related to lysozyme and bactericidal activities were identified as: A0A1Q9G213 (N-acetylmuramoyl-L-alanine amidase) and A0A1Q9FRD3 (D-alanyl-D-alanine carboxypeptidase). The corresponding protein sequence and nucleic acid sequence were determined by comparison with the database.

MetaPep: A core peptide database for faster human gut metaproteomics database searches

Metaproteomics has increasingly been applied to study functional changes in the human gut microbiome. Peptide identification is an important step in metaproteomics research, with sequence database search (SDS) and spectral library search (SLS) as the two main methods to identify peptides. However, the large search space in metaproteomics studies causes significant challenges for both identification methods. Moreover, with the development of mass spectrometry, it is now feasible to perform metaproteomic projects involving 100–1000 individual microbiomes. These large-scale projects create a conundrum for searching large databases. In this study, we constructed MetaPep, a core peptide database (including both collections of peptide sequences and tandem MS spectra) greatly accelerating the peptide identifications. Raw files from fifteen metaproteomics projects were re-analyzed and the identified peptide-spectrum matches (PSMs) were used to construct the MetaPep database. The constructed MetaPep database achieved rapid and accurate identification of peptides for human gut metaproteomics. MetaPep has a large collection of peptides and spectra that have been identified in published human gut metaproteomics datasets. MetaPep database can be used as an important resource in the current stage of human gut metaproteomics research. This study showed the possibility of applying a core peptide database as a generic metaproteomics workflow. MetaPep could also be an important resource for future human gut metaproteomics research, such as DIA (data-independent acquisition) analysis.

Psm_utils: A High-Level Python API for Parsing and Handling Peptide-Spectrum Matches and Proteomics Search Results.

A plethora of proteomics search engine output file formats are in circulation. This lack of standardized output files greatly complicates generic downstream processing of peptide-spectrum matches (PSMs) and PSM files. While standards exist to solve this problem, these are far from universally supported by search engines. Moreover, software libraries are available to read a selection of PSM file formats, but a package to parse PSM files into a unified data structure has been missing. Here, we present psm_utils, a Python package to read and write various PSM file formats and to handle peptidoforms, PSMs, and PSM lists in a unified and user-friendly Python-, command line-, and web-interface. psm_utils was developed with pragmatism and maintainability in mind, adhering to community standards and relying on existing packages where possible. The Python API and command line interface greatly facilitate handling various PSM file formats. Moreover, a user-friendly web application was built using psm_utils that allows anyone to interconvert PSM files and retrieve basic PSM statistics. psm_utils is freely available under the permissive Apache2 license at https://github.com/compomics/psm_utils.

FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics.

Microbial community proteomics, also termed metaproteomics, investigates all proteins expressed by a microbiota. Tandem mass spectrometry (MS/MS) is the typical method for identifying proteins in metaproteomics, which involves searching the mass spectra against a protein sequence database. A major post-analysis step is controlling the false discovery rate (FDR), i.e., the ratio of false positives to the total number of annotations. The current popular target-decoy FDR estimation method treats all the peptides and proteins equally and overlooks that they could have varied probabilities of being identified. In this study, we report FineFDR, a framework for FDR assessment at fine-grained levels with taxonomy information considered. FineFDR groups the identified peptide-spectrum matches, peptides, and proteins from different taxonomic units and estimates the FDR in each group separately. Empirical experiments on the simulated and real-world data sets demonstrate that our FineFDR achieved higher precision and more peptide and protein identifications when compared to the state-of-the-art methods, such as Comet, Percolator, TIDD, and Tailor. FineFDR is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/FDR.

False discovery rate estimation using candidate peptides for each spectrum

BackgroundFalse discovery rate (FDR) estimation is very important in proteomics. The target-decoy strategy (TDS), which is often used for FDR estimation, estimates the FDR under the assumption that when spectra are identified incorrectly, the probabilities of the spectra matching the target or decoy peptides are identical. However, no spectra matching target or decoy peptide probabilities are identical. We propose cTDS (target-decoy strategy with candidate peptides) for accurate estimation of the FDR using the probability that the spectrum is identified incorrectly as a target or decoy peptide.ResultsMost spectrum cases result in a probability of having the spectrum identified incorrectly as a target or decoy peptide of close to 0.5, but only about 1.14–4.85% of the total spectra have an exact probability of 0.5. We used an entrapment sequence method to demonstrate the accuracy of cTDS. For fixed FDR thresholds (1–10%), the false match rate (FMR) in cTDS is closer than the FMR in TDS. We compared the number of peptide-spectrum matches (PSMs) obtained with TDS and cTDS at a 1% FDR threshold with the HEK293 dataset. In the first and third replications, the number of PSMs obtained with cTDS for the reverse, pseudo-reverse, shuffle, and de Bruijn databases exceeded those obtained with TDS (about 0.001–0.132%), with the pseudo-shuffle database containing less compared to TDS (about 0.05–0.126%). In the second replication, the number of PSMs obtained with cTDS for all databases exceeds that obtained with TDS (about 0.013–0.274%).ConclusionsWhen spectra are actually identified incorrectly, most probabilities of the spectra matching a target or decoy peptide are not identical. Therefore, we propose cTDS, which estimates the FDR more accurately using the probability of the spectrum being identified incorrectly as a target or decoy peptide.

Challenging Targets or Describing Mismatches? A Comment on Common Decoy Distribution by Madej et al.

In their recent article, Madej et al. (Madej, D.; Wu, L.; Lam, H.Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics. J. Proteome Res.2022, 21 (2), 339-348) proposed an original way to solve the recurrent issue of controlling for the false discovery rate (FDR) in peptide-spectrum-match (PSM) validation. Briefly, they proposed to derive a single precise distribution of decoy matches termed the Common Decoy Distribution (CDD) and to use it to control for FDR during a target-only search. Conceptually, this approach is appealing as it takes the best of two worlds, i.e., decoy-based approaches (which leverage a large-scale collection of empirical mismatches) and decoy-free approaches (which are not subject to the randomness of decoy generation while sparing an additional database search). Interestingly, CDD also corresponds to a middle-of-the-road approach in statistics with respect to the two main families of FDR control procedures: Although historically based on estimating the false-positive distribution, FDR control has recently been demonstrated to be possible thanks to competition between the original variables (in proteomics, target sequences) and their fictional counterparts (in proteomics, decoys). Discriminating between these two theoretical trends is of prime importance for computational proteomics. In addition to highlighting why proteomics was a source of inspiration for theoretical biostatistics, it provides practical insights into the improvements that can be made to FDR control methods used in proteomics, including CDD.