Accurate Peptide Identification Research Articles

SpecEncoder: deep metric learning for accurate peptide identification in proteomics.

Tandem mass spectrometry (MS/MS) is a crucial technology for large-scale proteomic analysis. The protein database search or the spectral library search are commonly used for peptide identification from MS/MS spectra, which, however, may face challenges due to experimental variations between replicated spectra and similar fragmentation patterns among distinct peptides. To address this challenge, we present SpecEncoder, a deep metric learning approach to address these challenges by transforming MS/MS spectra into robust and sensitive embedding vectors in a latent space. The SpecEncoder model can also embed predicted MS/MS spectra of peptides, enabling a hybrid search approach that combines spectral library and protein database searches for peptide identification. We evaluated SpecEncoder on three large human proteomics datasets, and the results showed a consistent improvement in peptide identification. For spectral library search, SpecEncoder identifies 1%-2% more unique peptides (and PSMs) than SpectraST. For protein database search, it identifies 6%-15% more unique peptides than MSGF+ enhanced by Percolator, Furthermore, SpecEncoder identified 6%-12% additional unique peptides when utilizing a combined library of experimental and predicted spectra. SpecEncoder can also identify more peptides when compared to deep-learning enhanced methods (MSFragger boosted by MSBooster). These results demonstrate SpecEncoder's potential to enhance peptide identification for proteomic data analyses. The source code and scripts for SpecEncoder and peptide identification are available on GitHub at https://github.com/lkytal/SpecEncoder. Contact: hatang@iu.edu.

Benchmarking Bioinformatics Pipelines in Data-Independent Acquisition Mass Spectrometry for Immunopeptidomics

Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex [human leukocyte antigen (HLA) in humans]. These HLA–peptide complexes are presented on the cell surface for immune T-cell recognition. Immunopeptidomics denotes the utilization of tandem mass spectrometry to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for quantitative proteomics and deep proteome-wide identification; however, DIA application to immunopeptidomics analyses has so far seen limited use. Further, of the many DIA data processing tools currently available, there is no consensus in the immunopeptidomics community on the most appropriate pipeline(s) for in-depth and accurate HLA peptide identification. Herein, we benchmarked four commonly used spectral library-based DIA pipelines developed for proteomics applications (Skyline, Spectronaut, DIA-NN, and PEAKS) for their ability to perform immunopeptidome quantification. We validated and assessed the capability of each tool to identify and quantify HLA-bound peptides. Generally, DIA-NN and PEAKS provided higher immunopeptidome coverage with more reproducible results. Skyline and Spectronaut conferred more accurate peptide identification with lower experimental false-positive rates. All tools demonstrated reasonable correlations in quantifying precursors of HLA-bound peptides. Our benchmarking study suggests a combined strategy of applying at least two complementary DIA software tools to achieve the greatest degree of confidence and in-depth coverage of immunopeptidome data.

Discrimination of cationic peptides using malaria translocon, EXP2, nanopores.

Protein identification provides crucial information on the extensive biochemical reaction in our life. Although mass spectrometry has generally been used for protein/peptide identification, the scarcity of proteins in biological samples and post-translational modifications have remained a challenge. Nanopores, which provide rapid and sensitive single-molecule analysis have attracted attention as an alternative to these techniques. The sensing accuracy of nanopores mainly depends on the congeniality of electrostatic affinity and size between the nanopores and molecules. In this study, we evaluated the capability of peptide detection on EXP2 nanopore, which intrinsically transports a polypeptide chain in vivo as a malaria translocon, using peptides with different sizes and numbers of charges. Our previous studies found that wild-typed EXP2 (Wt-EXP2) nanopore could detect and identify two types of poly-L-lysine (short poly-L-lysine: 10,000 Da, long poly-L-lysine: 30,000-70,000 Da) with different molecular weights. Although the Wt-EXP2 nanopore could discriminate the poly-cationic peptides, the inherent current noise of the Wt-EXP2 nanopore in the analyte-free state interfered with more accurate single-molecule detection. This current noise probably owing to the fluctuation of its C-terminus region, so we designed ΔD231-EXP2, a variant with the deletion of the C-terminus region. The result shows that the current noise frequency of ΔD231-EXP2 nanopore decreased by half compared with Wt-EXP2, and we succeeded in discriminating the two types of oligo-arginine (R7X, X=W, G). Furthermore, the capture frequencies of these cationic peptides of the EXP2 nanopore were higher than the α-hemolysin nanopore. ΔD231-EXP2 nanopore also could identify the five types of trypsinized fragments (893.4-1,429 Da) from lysozyme. These results suggest that the EXP2 nanopore may be suitable for cationic peptides detection and identification. We are going to apply another variant EXP2NC (K42D+S46F) whose amino acid in the constriction region is exchanged for more accurate peptide identification.

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.

AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides

Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid sequence and its anti-inflammatory property is of great importance for the discovery of new AIEs. To address this issue, we propose a novel prediction tool for accurate identification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. Most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. At the same time, we exploit several feature extraction models and utilize one feature selection model, in order to construct many base classifiers from various feature representations. More specifically, we develop an effective classification model, with which we can extract and learn a set of informative features from the ensemble classifier chain model with different group of base classifiers. Furthermore, in order to test the predictive power of our model, we conduct the comparative experiments on the leave-one-out cross-validation and the independent test. It shows that our novel predictor performs great accurate for identification of AIEs as well as existing outstanding prediction tools. Source codes are available at https://github.com/guofei-tju/Ensemble-classifier-chain-model.

Effects of intramolecular hydrogen bonds on the collision-induced dissociation of tryptic peptide ions

The protonated basic side chains in tryptic peptide ions can form intramolecular hydrogen bonds with backbone amide carbonyls, which inhibit the backbone cleavage and hinder the peptide sequencing using mass spectrometry. To study the strength of intramolecular hydrogen bonds and its effect on the backbone cleavage, we carried out the energy-dependent collision-induced dissociation (CID) of tryptic peptides and their modified ones. The N-acylation of lysine-terminated peptides was used to convert the primary amines at both lysine and the N-terminus into amides in order to block the primary protonation sites. The guanidination was employed to transform lysine into homoarginine in order to imitate arginine-terminated peptides. Then, N-acylation was applied to the guanidinated peptides to prepare N-acylated, homoarginine-terminated peptides. Singly charged peptide ions were generated by electrospray ionization in a Q-TOF instrument and their CID spectra were obtained as a function of collision energy under nearly single-collision conditions with Xe as the target gas. The energy-dependent survival yield of the precursor ion provided information on the change in dissociation energy of peptides after chemical modifications. We also identified possible sites and numbers of hydrogen bonds that stabilized a charge-solvated structure by comparing the variation in product yields with the location of backbone cleavage. By taking these data together, we estimated the relative strength of intramolecular hydrogen bonds formed in lysine- and homoarginine-terminated peptides before and after N-acylation. Our results demonstrate that N-acylation lowers the dissociation energy of tryptic peptides and facilitates the backbone cleavage by preventing the formation of intramolecular hydrogen bonds, enabling accurate identification of peptides.

Peptide fingerprinting of the sea anemone Heteractis magnifica mucus revealed neurotoxins, Kunitz-type proteinase inhibitors and a new β-defensin α-amylase inhibitor

Sea anemone mucus, due to its multiple and vital functions, is a valuable substance for investigation of new biologically active peptides. In this work, compounds of Heteractis magnifica mucus were separated by multistage liquid chromatography and resulting fractions were analyzed by MALDI-TOF MS. Peptide maps constructed according to the molecular masses and hydrophobicity showed presence of 326 both new and known peptides. Several major peptides from mucus were identified, including the sodium channel toxin RpII isolated earlier from H. magnifica, and four Kunitz-type proteinase inhibitors identical to H. crispa ones. Kunitz-type transcript diversity was studied and sequences of mature peptides were deduced. New β-defensin α-amylase inhibitor, a homolog of helianthamide from Stichodactyla helianthus, was isolated and structurally characterized. Overall, H. magnifica is a source of biologically active peptides with great pharmacological potential. Biological significanceProteinase and α-amylase inhibitors along with toxins are major components of H. magnifica mucus which play an important role in the successful existence of sea anemones. Obtained peptide maps create a basis for more accurate identification of peptides during future transcriptomic/genomic studies of sea anemone H. magnifica.

Accurate identification of mass peaks for tandem mass spectra using MCMC model

In proteomics, many methods for the identification of proteins have been developed. However, because of limited known genome sequences, noisy data, incomplete ion sequences, and the accuracy of protein identification,it is challenging to identify peptides using tandem mass spectral data. Noise filtering and removing thus play a key role in accurate peptide identification from tandem mass spectra. In this paper, we employ a Bayesian model to identify proteins based on the prior information of bond cleavages. A Markov Chain Monte Carlo(MCMC)algorithm is used to simulate candidate peptides from the posterior distribution and to estimate the parameters for the Bayesian model. Our simulation and computational experimental results show that the model can identify peptide with a higher accuracy.

Accurate Identification of Deamidated Peptides in Global Proteomics Using a Quadrupole Orbitrap Mass Spectrometer

Deamidation of asparagine and glutamine residues is a common post-translational modification. Researchers often rely on mass spectrometric based proteomic techniques for the identification of these post-translational sites. Mass spectral analysis of deamidated peptides is complicated and often misassigned due to overlapping (13)C peak of the amidated form with the deamidated monoisotopic peak; these two peaks are only separated by 19.34 mDa. For proper assignment, it is inherently important to use a mass spectrometer with high mass measurement accuracy and high resolving power. Herein, mouse brain tissue lysate was prepared using filter-aided sample preparation (FASP) method and Stage Tip fractionation followed by analysis on a nanoLC coupled with a quadrupole orbitrap (Q-Exactive) mass spectrometer to accurately identify more than 5400 proteins. Mass spectral data was processed using MASCOT and ProteoIQ for accurate identification of peptides and proteins. MASCOT search values for precursor and MS/MS mass tolerances were investigated, and it was determined that data searched with greater than 5 ppm precursor mass tolerance resulted in the misassignment of deamidated peptides. Peptides that were identified with a mass measurement accuracy of ±5 ppm were correctly assigned.

Commentz-Walter: Any Better than Aho-Corasick for Peptide Identification?

An algorithm for locating all occurrences of a finite number of keywords in an arbitrary string, also known as multiple strings matching, is commonly required in information retrieval (such as sequence analysis, evolutionary biological studies, gene/protein identification and network intrusion detection) and text editing applications. Although Aho-Corasick was one of the commonly used exact multiple strings matching algorithm, Commentz-Walter has been introduced as a better alternative in the recent past. Comments-Walter algorithm combines ideas from both Aho-Corasick and Boyer Moore. Large scale rapid and accurate peptide identification is critical in computational proteomics. In this paper, we have critically analyzed the time complexity of Aho-Corasick and Commentz-Walter for their suitability in large scale peptide identification. According to the results we obtained for our dataset, we conclude that Aho-Corasick is performing better than Commentz-Walter as opposed to the common beliefs..

Clinical Proteomics

Clinical proteomics involves the analysis of protein expression of disease proteomes, with the aim of solving a specific clinical problem. Discoveries made from proteomicbased studies contribute to the growing need for innovative medical diagnostics for disease detection. Taking into consideration the global health burden of cardiac disease, clinical proteomics is a valuable tool to improve risk stratification associated with this disease. In cardiovascular medicine, the identification of novel proteins, or biomarkers, that are differentially expressed in cardiac disease proteomes may enable early detection of the disease state, thereby preventing progression to disease end points. This review outlines various proteomic platforms and their technical advancements and relates these to the cardiovascular sciences. The entire protein complement of the cell, or proteome, is dynamic and changes in response to the disease state. 1 Proteomic-based experiments can be used to characterize such alterations in protein expression during disease progression. 2 With combined improvements in mass spectrometry (MS) technology as well as innovative molecular biology screening tools, there has been widespread growth in the characterization of cardiac disease proteomes. In fact, proteomic technology has been an important tool in the analysis of heart failure (HF), 3,4 cardiac hypertrophy, 5,6 and dilated cardiomyopathy.7 Several of the overarching aims of such studies include providing greater understanding of general biological mechanisms as well as identifying unique proteins that are clinically useful in the detection of cardiac disease in the early stages or potentially used as novel therapeutic targets. Clinical proteomics continues to benefit from advancements in technologies that allow for fast and consistent identification of proteins with corresponding increases in the dynamic range of proteins detectable in the disease proteome. MS-related technologies have improved in their ability to detect low-abundance proteins as well as membrane proteins and have benefited from sample preprocessing strategies that decrease the complexity of large-scale analyses. With the emergence of different methodologies for biomarker identification, the following 3 criteria must be taken into careful consideration to determine whether the protein is, in fact,

TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules

MotivationAccurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules.MethodWe have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE.ResultThe performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.

PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification

Many software tools have been developed for the automated identification of peptides from tandem mass spectra. The accuracy and sensitivity of the identification software via database search are critical for successful proteomics experiments. A new database search tool, PEAKS DB, has been developed by incorporating the de novo sequencing results into the database search. PEAKS DB achieves significantly improved accuracy and sensitivity over two other commonly used software packages. Additionally, a new result validation method, decoy fusion, has been introduced to solve the issue of overconfidence that exists in the conventional target decoy method for certain types of peptide identification software.

Assay development for the determination of phosphorylation stoichiometry using multiple reaction monitoring methods with and without phosphatase treatment: application to breast cancer signaling pathways.

We have developed a phosphatase-based phosphopeptide quantitation (PPQ) method for determining phosphorylation stoichiometry in complex biological samples. This PPQ method is based on enzymatic dephosphorylation, combined with specific and accurate peptide identification and quantification by multiple reaction monitoring (MRM) with stable-isotope-labeled standard peptides. In contrast with classical MRM methods for the quantitation of phosphorylation stoichiometry, the PPQ-MRM method needs only one nonphosphorylated SIS (stable isotope-coded standard) and two analyses (one for the untreated sample and one for the phosphatase-treated sample), from which the expression and modification levels can accurately be determined. From these analyses, the percent phosphorylation can be determined. In this manuscript, we compare the PPQ-MRM method with an MRM method without phosphatase and demonstrate the application of these methods to the detection and quantitation of phosphorylation of the classic phosphorylated breast cancer biomarkers (ERalpha and HER2), and for phosphorylated RAF and ERK1, which also contain phosphorylation sites of biological importance. Using synthetic peptides spiked into a complex protein digest, we were able to use our PPQ-MRM method to accurately determine the total phosphorylation stoichiometry on specific peptides as well as the absolute amount of the peptide and phosphopeptide present. Analyses of samples containing ERalpha protein revealed that the PPQ-MRM method is capable of determining phosphorylation stoichiometry in proteins from cell lines, and is in good agreement with determinations obtained using the direct MRM approach in terms of phosphorylation and total protein amount.

Postexperiment Monoisotopic Mass Filtering and Refinement (PE-MMR) of Tandem Mass Spectrometric Data Increases Accuracy of Peptide Identification in LC/MS/MS

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.

Rapid and Accurate Peptide Identification from Tandem Mass Spectra

Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biological sample. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program Sequest. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate estimates. Crux also implements two recently described postprocessing methods: a p value calculation based upon fitting a Weibull distribution to the observed scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.

Methods for peptide identification by spectral comparison

BackgroundTandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies.ResultsThis paper investigates computational issues related to this spectral comparison approach. Different methods have been empirically evaluated over several large sets of spectra. First, we illustrate that the peak intensities follow a Poisson distribution. This implies that applying a square root transform will optimally stabilize the peak intensity variance. Our results show that the square root did indeed outperform other transforms, resulting in improved accuracy of spectral matching. Second, different measures of spectral similarity were compared, and the results illustrated that the correlation coefficient was most robust. Finally, we examine how to assemble multiple spectra associated with the same peptide to generate a synthetic reference spectrum. Ensemble averaging is shown to provide the best combination of accuracy and efficiency.ConclusionOur results demonstrate that when combined, these methods can boost the sensitivity and specificity of spectral comparison. Therefore they are capable of enhancing and complementing existing tools for consistent and accurate peptide identification.

‘Information‐Based‐Acquisition’ (IBA) technique with an ion‐trap/time‐of‐flight mass spectrometer for high‐throughput and reliable protein profiling

Highly complex protein mixtures can be analyzed after proteolysis using liquid chromatography/mass spectrometry (LC/MS). In an LC/MS run, intense peptide ions originating from high-abundance proteins are preferentially analyzed using tandem mass spectrometry (MS(2)), so obtaining the MS(2) spectra of peptide ions from low-abundance proteins is difficult even if such ions are detected. Furthermore, the MS(2) spectra may produce insufficient information to identify the peptides or proteins. To solve these problems, we have developed a real-time optimization technique for MS(2), called the Information-Based-Acquisition (IBA) system. In a preliminary LC/MS run, a few of the most intense ions detected in every MS spectrum are selected as precursors for MS(2) and their masses, charge states and retention times are automatically registered in an internal database. In the next run, a sample similar to that used in the first run is analyzed using database searching. Then, the ions registered in the database are excluded from the precursor ion selection to avoid duplicate MS(2) analyses. Furthermore, real-time de novo sequencing is performed just after obtaining the MS(2) spectrum, and an MS(3) spectrum is obtained for accurate peptide identification when the number of interpreted amino acids in the MS(2) spectrum is less than five. We applied the IBA system to a yeast cell lysate which is a typical crude sample, using a nanoLC/ion-trap time-of flight (IT/TOF) mass spectrometer, repeating the same LC/MS run five times. The obtained MS(2) and MS(3) spectra were analyzed by applying the Mascot (Matrix Science, Boston, MA, USA) search engine to identify proteins from the sequence database. The total number of identified proteins in five LC/MS runs was three times higher than that in the first run and the ion scores for peptide identification also significantly increased, by about 70%, when the MS(3) spectra were used, combined with the MS(2) spectra, before being subjected to Mascot analysis.

PREDNOD, a prediction server for peptide binding to the H-2g7 haplotype of the non-obese diabetic mouse

The non-obese diabetic (NOD) mouse is a widely used animal model for study of autoimmune diseases, in particular human type 1 diabetes mellitus (T1DM). Identification of the subset of peptides that bind MHC molecules comprising the H-2g7 haplotype of NOD mouse and thereby representing potential NOD T-cell epitopes is important for research into the pathogenesis and immunotherapy of T1DM. The H-2g7 haplotype comprises the MHC class-I molecules Kd and Db and a single class-II molecule I-Ag7. We have developed a prediction system, PREDNOD, for accurate identification of peptides that bind the MHC molecules constituting the H-2g7 haplotype. PREDNOD is accessible at http://antigen.i2r.a-star.edu.sg/Ag7.

Robust accurate identification of peptides (RAId): deciphering MS2 data using a structured library search with de novo based statistics

The key to MS -based proteomics is peptide sequencing. The major challenge in peptide sequencing, whether library search or de novo, is to better infer statistical significance and better attain noise reduction. Since the noise in a spectrum depends on experimental conditions, the instrument used and many other factors, it cannot be predicted even if the peptide sequence is known. The characteristics of the noise can only be uncovered once a spectrum is given. We wish to overcome such issues. We designed RAId to identify peptides from their associated tandem mass spectrometry data. RAId performs a novel de novo sequencing followed by a search in a peptide library that we created. Through de novo sequencing, we establish the spectrum-specific background score statistics for the library search. When the database search fails to return significant hits, the top-ranking de novo sequences become potential candidates for new peptides that are not yet in the database. The use of spectrum-specific background statistics seems to enable RAId to perform well even when the spectral quality is marginal. Other important features of RAId include its potential in de novo sequencing alone and the ease of incorporating post-translational modifications.