Peptide Datasets Research Articles

IPC - Isoelectric Point Calculator.

BackgroundAccurate estimation of the isoelectric point (pI) based on the amino acid sequence is useful for many analytical biochemistry and proteomics techniques such as 2-D polyacrylamide gel electrophoresis, or capillary isoelectric focusing used in combination with high-throughput mass spectrometry. Additionally, pI estimation can be helpful during protein crystallization trials.ResultsHere, I present the Isoelectric Point Calculator (IPC), a web service and a standalone program for the accurate estimation of protein and peptide pI using different sets of dissociation constant (pKa) values, including two new computationally optimized pKa sets. According to the presented benchmarks, the newly developed IPC pKa sets outperform previous algorithms by at least 14.9 % for proteins and 0.9 % for peptides (on average, 22.1 % and 59.6 %, respectively), which corresponds to an average error of the pI estimation equal to 0.87 and 0.25 pH units for proteins and peptides, respectively. Moreover, the prediction of pI using the IPC pKa’s leads to fewer outliers, i.e., predictions affected by errors greater than a given threshold.ConclusionsThe IPC service is freely available at http://isoelectric.ovh.org Peptide and protein datasets used in the study and the precalculated pI for the PDB and some of the most frequently used proteomes are available for large-scale analysis and future development.ReviewersThis article was reviewed by Frank Eisenhaber and Zoltán GáspáriElectronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0159-9) contains supplementary material, which is available to authorized users.

PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues.

BackgroundIn the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of peptides containing natural and non-natural/modified residues.ResultsPEPstrMOD integrates Forcefield_NCAA and Forcefield_PTM force field libraries to handle 147 non-natural residues and 32 types of post-translational modifications respectively by performing molecular dynamics using AMBER. AMBER was also used to handle other modifications like peptide cyclization, use of D-amino acids and capping of terminal residues. In addition, GROMACS was used to implement 210 non-natural side-chains in peptides using SwissSideChain force field library. We evaluated the performance of PEPstrMOD on three datasets generated from Protein Data Bank; i) ModPep dataset contains 501 non-natural peptides, ii) ModPep16, a subset of ModPep, and iii) CyclicPep contains 34 cyclic peptides. We achieved backbone Root Mean Square Deviation between the actual and predicted structure of peptides in the range of 3.81–4.05 Å.ConclusionsIn summary, the method PEPstrMOD has been developed that predicts the structure of modified peptide from the sequence/structure given as input. We validated the PEPstrMOD application using a dataset of peptides having non-natural/modified residues. PEPstrMOD offers unique advantages that allow the users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allows extended simulation of predicted peptides. This will help the researchers to have prior structural information of modified peptides to further design the peptides for desired therapeutic property. PEPstrMOD is freely available at http://osddlinux.osdd.net/raghava/pepstrmod/.ReviewersThis article was reviewed by Prof Michael Gromiha, Dr. Bojan Zagrovic and Dr. Zoltan Gaspari.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-015-0103-4) contains supplementary material, which is available to authorized users.

Evolution of the avian β-defensin and cathelicidin genes.

Backgroundβ-defensins and cathelicidins are two families of cationic antimicrobial peptides (AMPs) with a broad range of antimicrobial activities that are key components of the innate immune system. Due to their important roles in host defense against rapidly evolving pathogens, the two gene families provide an ideal system for studying adaptive gene evolution. In this study we performed phylogenetic and selection analyses on β-defensins and cathelicidins from 53 avian species representing 32 orders to examine the evolutionary dynamics of these peptides in birds.Results and conclusionsAvian β-defensins are found in a gene cluster consisting of 13 subfamiles. Nine of these are conserved as one to one orthologs in all birds, while the others (AvBD1, AvBD3, AvBD7 and AvBD14) are more subject to gene duplication or pseudogenisation events in specific avian lineages. Avian cathelicidins are found in a gene cluster consisting of three subfamilies with species-specific duplications and gene loss. Evidence suggested that the propiece and mature peptide domains of avian cathelicidins are possibly co-evolving in such a way that the cationicity of the mature peptide is partially neutralised by the negative charge of the propiece prior to peptide secretion (further evidence obtained by repeating the analyses on primate cathelicidins). Negative selection (overall mean dN < dS) was detected in most of the gene domains examined, conserving certain amino acid residues that may be functionally crucial for the avian β-defensins and cathelicidins, while episodic positive selection was also involved in driving the diversification of specific codon sites of certain AMPs in avian evolutionary history. These findings have greatly improved our understanding of the molecular evolution of avian AMPs and will be useful to understand their role in the avian innate immune response. Additionally, the large dataset of β-defensin and cathelicidin peptides may also provide a valuable resource for translational research and development of novel antimicrobial agents in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0465-3) contains supplementary material, which is available to authorized users.

High-order neural networks and kernel methods for peptide-MHC binding prediction.

Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding. The proposed high-order methods improve quality of binding predictions over other prediction methods. With the proposed methods, a significant gain of up to 25-40% is observed on the benchmark and reference peptide datasets and tasks. In addition, for the first time, our experiments show that pre-training with high-order semi-restricted Boltzmann machines significantly improves the performance of feed-forward HONNs. Moreover, our experiments show that the proposed shallow HONN outperform the popular pre-trained deep neural network on most tasks, which demonstrates the effectiveness of modelling high-order feature interactions for predicting major histocompatibility complex-peptide binding. There is no associated distributable software. renqiang@nec-labs.com or mark.gerstein@yale.edu Supplementary data are available at Bioinformatics online.

Empirical estimation of local dielectric constants: Toward atomistic design of collagen mimetic peptides.

One of the key challenges in modeling protein energetics is the treatment of solvent interactions. This is particularly important in the case of peptides, where much of the molecule is highly exposed to solvent due to its small size. In this study, we develop an empirical method for estimating the local dielectric constant based on an additive model of atomic polarizabilities. Calculated values match reported apparent dielectric constants for a series of Staphylococcus aureus nuclease mutants. Calculated constants are used to determine screening effects on Coulombic interactions and to determine solvation contributions based on a modified Generalized Born model. These terms are incorporated into the protein modeling platform protCAD, and benchmarked on a data set of collagen mimetic peptides for which experimentally determined stabilities are available. Computing local dielectric constants using atomistic protein models and the assumption of additive atomic polarizabilities is a rapid and potentially useful method for improving electrostatics and solvation calculations that can be applied in the computational design of peptides.

Human Basal Tear Peptidome Characterization by CID, HCD, and ETD Followed by in Silico and in Vitro Analyses for Antimicrobial Peptide Identification.

Endogenous peptides are valuable targets in the analysis of biological processes. The tear film contains proteins and peptides released by the tear duct mucosal cells, including antimicrobial peptides involved in the protection against exogenous pathogens; however, the peptide content of the tear liquid remains poorly characterized. We analyzed naturally occurring peptides isolated from human basal tears. Mass spectrometry analysis of endogenous peptides presents a number of drawbacks, including size heterogeneity and nonpredictable fragmentation patterns, among others. Therefore, CID, ETD, and HCD methods were used for the characterization of the tear peptide content. The contribution of DMSO as an additive of the chromatographic solvents was also evaluated. We identified 157, 131, and 122 peptides using CID-, ETD-, and HCD-based methods, respectively. Altogether, 234 different peptides were identified, leading to the generation of the biggest data set of endogenous tear peptides to date. The antimicrobial activity prediction analysis performed in silico revealed different putative antimicrobial peptides. Two of the extracellular glycoprotein lacritin peptides were de novo synthesized, and their antimicrobial activity was confirmed in vitro. Our findings demonstrate the benefits of using different fragmentation methods for the analysis of endogenous peptides and provide a useful approach for the discovery of peptides with antimicrobial activity.

PGTools: A Software Suite for Proteogenomic Data Analysis and Visualization.

We describe PGTools, an open source software suite for analysis and visualization of proteogenomic data. PGTools comprises applications, libraries, customized databases, and visualization tools for analysis of mass-spectrometry data using combined proteomic and genomic backgrounds. A single command is sufficient to search databases, calculate false discovery rates, group and annotate proteins, generate peptide databases from RNA-Seq transcripts, identify altered proteins associated with cancer, and visualize genome scale peptide data sets using sophisticated visualization tools. We experimentally confirm a subset of proteogenomic peptides in human PANC-1 cells and demonstrate the utility of PGTools using a colorectal cancer data set that led to the identification of 203 novel protein coding regions missed by conventional proteomic approaches. PGTools should be equally useful for individual proteogenomic investigations as well as international initiatives such as chromosome-centric Human Proteome Project (C-HPP). PGTools is available at http://qcmg.org/bioinformatics/PGTools.

Analyzing a dipeptide library to identify human dipeptidyl peptidase IV inhibitor

Human dipeptidyl peptidase IV (hDPPIV) inhibitors provide an effective strategy for the treatment of type 2 diabetes. Because certain peptides are known to act as hDPPIV inhibitors, a dataset of possible peptides with their inhibition intensities will facilitate the development of functional food for type 2 diabetes. In this study, we examined a total of 337 dipeptides with respect to their hDPPIV inhibitory effects. Amino acid residues at N-termini dominated their inhibition intensities. Particularly highly inhibitory dipeptides discovered included the following novel dipeptides: Thr-His, Asn-His, Val-Leu, Met-Leu, and Met-Met. Using our dataset, prime candidates contributing to the hDPPIV inhibitory effect of soy protein hydrolyzates were successfully identified. Possible dietary proteins potentially able to produce particularly highly hDPPIV inhibitory peptides are also discussed on the basis of the dataset.

Designing of peptides with desired half-life in intestine-like environment.

BackgroundIn past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life.ResultsIn this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides.ConclusionIn summary, this study describes a web server ‘HLP’ that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-282) contains supplementary material, which is available to authorized users.

Neglected tropical diseases and omics science: proteogenomics analysis of the promastigote stage of Leishmania major parasite.

Among the neglected tropical diseases, leishmaniasis is one of the most devastating, resulting in significant mortality and contributing to nearly 2 million disability-adjusted life years. Cutaneous leishmaniasis is a debilitating disorder caused by the kinetoplastid protozoan parasite Leishmania major, which results in disfiguration and scars. L. major genome was the first to be sequenced within the genus Leishmania. Use of proteomic data for annotating genomes is a complementary approach to conventional genome annotation approaches and is referred to as proteogenomics. We have used a proteogenomics-based approach to map the proteome of L. major and also annotate its genome. In this study, we searched L. major promastigote proteomic data against the annotated L. major protein database. Additionally, we searched the proteomic data against six-frame translated L. major genome. In all, we identified 3613 proteins in L. major promastigotes, which covered 43% of its proteome. We also identified 26 genome search-specific peptides, which led to the identification of three novel genes previously not identified in L. major. We also corrected the annotation of N-termini of 15 genes, which resulted in extension of their protein products. We have validated our proteogenomics findings by RT-PCR and sequencing. In addition, our study resulted in identification of 266 N-terminally acetylated peptides in L. major, one of the largest acetylated peptide datasets thus far in Leishmania. This dataset should be a valuable resource to researchers focusing on neglected tropical diseases.

Improving the prediction of HLA class I-binding peptides using a supertype-based method

The computational prediction of peptides that bind to major histocompatibility complex (MHC) molecules has practical importance for the development of epitope-based vaccines. The performance of the prediction methods depends on the verified peptides. However, the available peptide datasets of most alleles contain significant biases. An investigation to the effect of the peptides in the training dataset on the performance of the generated model indicated that there was a discrepancy between the classification of binders from biological data and classification of binders from super-motif-sharing peptides, which was induced by the non-motif-containing peptides. Most human MHC (called HLA) class I molecules could be assigned to supertypes based on their overlapping peptide-binding specificities, therefore, we proposed a supertype-based method for the modeling of the HLA class I-peptide binding: candidates of peptides binding to alleles in a given supertypes were screened using the super-motifs, and then the peptides binding to specific allele in the supertype were predicted by the model trained on the super-motif-sharing peptides. The efficacy of this supertype-based method was examined in two matrix-based methods and one machine learning method for 20 alleles in HLA supertypes A1, A2, A3, A24, B44 and B7. Evaluations on several benchmark datasets indicated that the supertype-based method achieved remarkable success in improving the prediction of HLA-binding peptides.

Cleaved and Missed Sites for Trypsin, Lys-C, and Lys-N Can Be Predicted with High Confidence on the Basis of Sequence Context

Trypsin, Lys-C, and Lys-N are the most broadly used enzymes in proteomics. Here, on the basis of large-scale peptide mass spectrometry (MS) data sets, an approach is described to confidently identify missed cleavage sites in either phosphorylated or unmodified substrates for these three proteases, or any protease, on the basis of side chain species present within 15 residues of the cleavage-specificity residue. Previously known effects of proline, negatively charged side chains, and phospho-modified residues have been quantified, and additional side chain effects were noted. By applying a set of quantitative side chain rules established for each of the three proteases, scissile and nonscissile sites could be established, on the basis of protein sequence alone, with near certainty for Lys-C, and with a high degree of confidence for trypsin or Lys-N. These rules were applicable to orthogonal peptide data sets, including the two largest in the PeptideAtlas database. The approach described here facilitates the comprehensive modeling of substrate recognition in proteolysis.

HomoSAR: Bridging comparative protein modeling with quantitative structural activity relationship to design new peptides

Peptides play significant roles in the biological world. To optimize activity for a specific therapeutic target, peptide library synthesis is inevitable; which is a time consuming and expensive. Computational approaches provide a promising way to simply elucidate the structural basis in the design of new peptides. Earlier, we proposed a novel methodology termed HomoSAR to gain insight into the structure activity relationships underlying peptides. Based on an integrated approach, HomoSAR uses the principles of homology modeling in conjunction with the quantitative structural activity relationship formalism to predict and design new peptide sequences with the optimum activity. In the present study, we establish that the HomoSAR methodology can be universally applied to all classes of peptides irrespective of sequence length by studying HomoSAR on three peptide datasets viz., angiotensin-converting enzyme inhibitory peptides, CAMEL-s antibiotic peptides, and hAmphiphysin-1 SH3 domain binding peptides, using a set of descriptors related to the hydrophobic, steric, and electronic properties of the 20 natural amino acids. Models generated for all three datasets have statistically significant correlation coefficients (r(2)) and predictive r2 (r(pred)2) and cross validated coefficient ( q(LOO)2). The daintiness of this technique lies in its simplicity and ability to extract all the information contained in the peptides to elucidate the underlying structure activity relationships. The difficulties of correlating both sequence diversity and variation in length of the peptides with their biological activity can be addressed. The study has been able to identify the preferred or detrimental nature of amino acids at specific positions in the peptide sequences.

An index for characterization of natural and non-natural amino acids for peptidomimetics.

Bioactive peptides and peptidomimetics play a pivotal role in the regulation of many biological processes such as cellular apoptosis, host defense, and biomineralization. In this work, we develop a novel structural matrix, Index of Natural and Non-natural Amino Acids (NNAAIndex), to systematically characterize a total of 155 physiochemical properties of 22 natural and 593 non-natural amino acids, followed by clustering the structural matrix into 6 representative property patterns including geometric characteristics, H-bond, connectivity, accessible surface area, integy moments index, and volume and shape. As a proof-of-principle, the NNAAIndex, combined with partial least squares regression or linear discriminant analysis, is used to develop different QSAR models for the design of new peptidomimetics using three different peptide datasets, i.e., 48 bitter-tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, and 20 inorganic-binding peptides. A comparative analysis with other QSAR techniques demonstrates that the NNAAIndex method offers a stable and predictive modeling technique for in silico large-scale design of natural and non-natural peptides with desirable bioactivities for a wide range of applications.

Conserved HIV Wide Spectrum Antipeptides-A Hope for HIV Treatment

Antipeptide or inhibiting peptide aims to prevent virus/coreceptor interaction. HIV inhibting peptide dataset collected from HIPdb database was used in this study. There are 110 HIV inhibiting peptide is present in HIPdb database. Multiple Sequence Alignment (MSA) of all total 110 antipeptide has been performed and got some best conserved antipeptides. Next, prediction of antigenicity method was used for finding the maximum antigenicity out of the 14 conserved inhibiting peptides. All peptides were screened for hydrophobicity as low hydrophobicity induces humoral mediated immunity. Afterwards, Antimicrobial Peptide Prediction (AMP) and its classification was performed. In this study, PWQGGRRKFR and KYRRFRWKFK are the promising HIV antipeptide for AIDS treatment. This study will help scientists to promote research for better understanding of HIV treatment in forms of drug and vaccine development.

Tryptic Peptide Reference Data Sets for MALDI Imaging Mass Spectrometry on Formalin-fixed Ovarian Cancer Tissues

MALDI imaging mass spectrometry is a powerful tool for morphology-based proteomic tissue analysis. However, peptide identification is still a major challenge due to low S/N ratios, low mass accuracy and difficulties in correlating observed m/z species with peptide identities. To address this, we have analyzed tryptic digests of formalin-fixed paraffin-embedded tissue microarray cores, from 31 ovarian cancer patients, by LC-MS/MS. The sample preparation closely resembled the MALDI imaging workflow in order to create representative reference data sets containing peptides also observable in MALDI imaging experiments. This resulted in 3844 distinct peptide sequences, at a false discovery rate of 1%, for the entire cohort and an average of 982 distinct peptide sequences per sample. From this, a total of 840 proteins and, on average, 297 proteins per sample could be inferred. To support the efforts of the Chromosome-centric Human Proteome Project Consortium, we have annotated these proteins with their respective chromosome location. In the presented work, the benefit of using a large cohort of data sets was exemplified by correct identification of several m/z species observed in a MALDI imaging experiment. The tryptic peptide data sets generated will facilitate peptide identification in future MALDI imaging studies on ovarian cancer.

Transcriptome database resource and gene expression atlas for the rose

BackgroundFor centuries roses have been selected based on a number of traits. Little information exists on the genetic and molecular basis that contributes to these traits, mainly because information on expressed genes for this economically important ornamental plant is scarce.ResultsHere, we used a combination of Illumina and 454 sequencing technologies to generate information on Rosa sp. transcripts using RNA from various tissues and in response to biotic and abiotic stresses. A total of 80714 transcript clusters were identified and 76611 peptides have been predicted among which 20997 have been clustered into 13900 protein families. BLASTp hits in closely related Rosaceae species revealed that about half of the predicted peptides in the strawberry and peach genomes have orthologs in Rosa dataset. Digital expression was obtained using RNA samples from organs at different development stages and under different stress conditions. qPCR validated the digital expression data for a selection of 23 genes with high or low expression levels. Comparative gene expression analyses between the different tissues and organs allowed the identification of clusters that are highly enriched in given tissues or under particular conditions, demonstrating the usefulness of the digital gene expression analysis. A web interface ROSAseq was created that allows data interrogation by BLAST, subsequent analysis of DNA clusters and access to thorough transcript annotation including best BLAST matches on Fragaria vesca, Prunus persica and Arabidopsis. The rose peptides dataset was used to create the ROSAcyc resource pathway database that allows access to the putative genes and enzymatic pathways.ConclusionsThe study provides useful information on Rosa expressed genes, with thorough annotation and an overview of expression patterns for transcripts with good accuracy.

Consensus classification of human leukocyte antigen class II proteins

Class II human leukocyte antigens (HLA II) are proteins involved in the human immunological adaptive response by binding and exposing some pre-processed, non-self peptides in the extracellular domain in order to make them recognizable by the CD4+ T lymphocytes. However, the understanding of HLA–peptide binding interaction is a crucial step for designing a peptide-based vaccine because the high rate of polymorphisms in HLA class II molecules creates a big challenge, even though the HLA II proteins can be grouped into supertypes, where members of different class bind a similar pool of peptides. Hence, first we performed the supertype classification of 27 HLA II proteins using their binding affinities and structural-based linear motifs to create a stable group of supertypes. For this purpose, a well-known clustering method was used, and then, a consensus was built to find the stable groups and to show the functional and structural correlation of HLA II proteins. Thus, the overlap of the binding events was measured, confirming a large promiscuity within the HLA II–peptide interactions. Moreover, a very low rate of locus-specific binding events was observed for the HLA-DP genetic locus, suggesting a different binding selectivity of these proteins with respect to HLA-DR and HLA-DQ proteins. Secondly, a predictor based on a support vector machine (SVM) classifier was designed to recognize HLA II-binding peptides. The efficiency of prediction was estimated using precision, recall (sensitivity), specificity, accuracy, F-measure, and area under the ROC curve values of random subsampled dataset in comparison with other supervised classifiers. Also the leave-one-out cross-validation was performed to establish the efficiency of the predictor. The availability of HLA II–peptide interaction dataset, HLA II-binding motifs, high-quality amino acid indices, peptide dataset for SVM training, and MATLAB code of the predictor is available at http://sysbio.icm.edu.pl/HLA.Electronic supplementary materialThe online version of this article (doi:10.1007/s00251-012-0665-6) contains supplementary material, which is available to authorized users.

Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach

Proteins recognizing short peptide fragments play a central role in cellular signaling. As a result of high-throughput technologies, peptide-binding protein specificities can be studied using large peptide libraries at dramatically lower cost and time. Interpretation of such large peptide datasets, however, is a complex task, especially when the data contain multiple receptor binding motifs, and/or the motifs are found at different locations within distinct peptides. The algorithm presented in this article, based on Gibbs sampling, identifies multiple specificities in peptide data by performing two essential tasks simultaneously: alignment and clustering of peptide data. We apply the method to de-convolute binding motifs in a panel of peptide datasets with different degrees of complexity spanning from the simplest case of pre-aligned fixed-length peptides to cases of unaligned peptide datasets of variable length. Example applications described in this article include mixtures of binders to different MHC class I and class II alleles, distinct classes of ligands for SH3 domains and sub-specificities of the HLA-A*02:01 molecule. The Gibbs clustering method is available online as a web server at http://www.cbs.dtu.dk/services/GibbsCluster.

A Systematic Investigation into the Nature of Tryptic HCD Spectra

Modern mass spectrometry-based proteomics can produce millions of peptide fragmentation spectra, which are automatically identified in databases using sequence-specific b- or y-ions. Proteomics projects have mainly been performed with low resolution collision-induced dissociation (CID) in ion traps and beam-type fragmentation on triple quadrupole and QTOF instruments. Recently, the latter has also become available with Orbitrap instrumentation as higher energy collisional dissociation (HCD), routinely providing full mass range fragmentation with high mass accuracy. To systematically study the nature of HCD spectra, we made use of a large scale data set of tryptic peptides identified with an FDR of 0.0001, from which we extract a subset of more than 16,000 that have little or no contribution from cofragmented precursors. We employed a newly developed computer-assisted "Expert System", which distills our experience and literature knowledge about fragmentation pathways. It aims to automatically annotate the peaks in high mass accuracy fragment spectra while strictly controlling the false discovery rate. Using this Expert System we determined that sequence specific regular ions covering the entire sequence were present for almost all peptides with up to 10 amino acids (median 100%). Peptides up to 20 amino acid length contained sufficient fragmentation to cover 80% of the sequence. Internal fragments are common in HCD spectra but not in high resolution CID spectra (10% vs 1%). The low mass region contains abundant immonium ions (6% of fragment ion intensity), the characteristic a(2), b(2) ion pair (72% of spectra), side chain fragments and reporter ions for peptide modifications such as tyrosine phosphorylation. B- and y-ions account for only 20% of fragment ions by number but 53% by ion intensity. Overall, 84% of the fragment ion intensity was unambiguously explainable. Thus high mass accuracy HCD and CID data are near comprehensively and automatically interpretable.