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Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5–40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units.

In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accurate ion intensities in theoretical spectra impairs the performance of search engines, in particular, on proteome scales. Here we introduce pDeepXL, a deep neural network to predict MS/MS spectra of cross-linked peptide pairs. To train pDeepXL, we used the transfer-learning technique because it facilitated the training with limited benchmark data of cross-linked peptide pairs. Test results on more than ten data sets showed that pDeepXL accurately predicted the spectra of both noncleavable DSS/BS3/Leiker cross-linked peptide pairs (>80% of predicted spectra have Pearson's r values higher than 0.9) and cleavable DSSO/DSBU cross-linked peptide pairs (>75% of predicted spectra have Pearson's r values higher than 0.9). pDeepXL also achieved the accurate prediction on unseen data sets using an online fine-tuning technique. Lastly, integrating pDeepXL into a database search engine increased the number of identified cross-link spectra by 18% on average.

The remarkable advancement of top-down proteomics in the past decade is driven by the technological development in separation, mass spectrometry (MS) instrumentation, novel fragmentation, and bioinformatics. However, the accurate identification and quantification of proteoforms, all clearly-defined molecular forms of protein products from a single gene, remain a challenging computational task. This is in part due to the complicated mass spectra from intact proteoforms when compared to those from the digested peptides. Herein, pTop 2.0 is developed to fill in the gap between the large-scale complex top-down MS data and the shortage of high-accuracy bioinformatic tools. Compared with pTop 1.0, the first version, pTop 2.0 concentrates mainly on the identification of the proteoforms with unexpected modifications or a terminal truncation. The quantitation based on isotopic labeling is also a new function, which can be carried out by the convenient and user-friendly "one-key operation," integrated together with the qualitative identifications. The accuracy and running speed of pTop 2.0 is significantly improved on the test data sets. This chapter will introduce the main features, step-by-step running operations, and algorithmic developments of pTop 2.0 in order to push the identification and quantitation of intact proteoforms to a higher-accuracy level in top-down proteomics.

Chemoproteomics has emerged as a key technology to expand the functional space in complex proteomes for probing fundamental biology and for discovering new small molecule-based therapies. Here we report a modification-centric computational tool termed pChem to provide a streamlined pipeline for unbiased performance assessment of chemoproteomic probes. The pipeline starts with an experimental setting for isotopically coding probe-derived modifications (PDMs) that can be automatically recognized by pChem, with masses accurately calculated and sites precisely localized. Further, pChem exports on-demand reports by scoring the profiling efficiency, modification-homogeneity and proteome-wide residue selectivity of a tested probe. The performance and robustness of pChem were benchmarked by applying it to eighteen bioorthogonal probes. Of note, the analyses reveal that the formation of unexpected PDMs can be driven by endogenous reactive metabolites (e.g., bioactive aldehydes and glutathione). Together, pChem is a powerful and user-friendly tool that aims to facilitate the development of probes for the ever-growing field of chemoproteomics.

MotivationDe novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.ResultsIn order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29–102% more correct spectra, and the precision was 11–89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21–50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences.Availability and implementationpNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html.Supplementary informationSupplementary data are available at Bioinformatics online.

De novo peptide sequencing for large-scale proteomics remains challenging because of the lack of full coverage of ion series in tandem mass spectra. We developed a mirror protease of trypsin, acetylated LysargiNase (Ac-LysargiNase), with superior activity and stability. The mirror spectrum pairs derived from the Ac-LysargiNase and trypsin treated samples can generate full b and y ion series, which provide mutual complementarity of each other, and allow us to develop a novel algorithm, pNovoM, for de novo sequencing. Using pNovoM to sequence peptides of purified proteins, the accuracy of the sequence was close to 100%. More importantly, from a large-scale yeast proteome sample digested with trypsin and Ac-LysargiNase individually, 48% of all tandem mass spectra formed mirror spectrum pairs, 97% of which contained full coverage of ion series, resulting in precision de novo sequencing of full-length peptides by pNovoM. This enabled pNovoM to successfully sequence 21,249 peptides from 3,753 proteins and interpreted 44-152% more spectra than pNovo+ and PEAKS at a 5% FDR at the spectrum level. Moreover, the mirror protease strategy had an obvious advantage in sequencing long peptides. We believe that the combination of mirror protease strategy and pNovoM will be an effective approach for precision de novo sequencing on both single proteins and proteome samples.

We present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A novel glycan ion-indexing algorithm developed in this work for glycan-first search makes pGlyco3 5-40 times faster than other glycoproteomic search engines without decreasing the accuracies and sensitivities. By combining electron-based dissociation spectra, pGlyco3 integrates a fast, dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization (SSGL). Our evaluation based on synthetic and natural glycopeptides showed that the SSGL probabilities estimated by pGlycoSite were proved to be appropriate to localize site-specific glycans. With pGlyco3, we found that N-glycopeptides and O-mannose glycopeptides in yeast samples were extensively modified by ammonia adducts on Hex (aH) and verified the aH-glycopeptide identifications based on released N-glycans and15N/13C-labeled data. Thus pGlyco3, which is freely available onhttps://github.com/pFindStudio/pGlyco3/releases, is an accurate and flexible tool to identify glycopeptides and modified saccharide units.

Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. We have proposed a validation method pValid before, but its scope of application is limited because two features used in pValid are related to open database search and sub-optimal peptide candidates for tandem mass spectra, and the performance on complex datasets still has room for improvement. In this study, we developed a more comprehensive validation method, pValid 2, to break these limitations by removing the two features and bringing in a new feature related to the retention time predicted by a deep learning-based method pPredRT. pValid 2 yielded an average false positive rate of 0.03% and an average false negative rate of 1.37% on three testing datasets, better than those of pValid, and flagged 8.47% to 11.31% more incorrect identifications than pValid on two complex datasets. Moreover, pValid 2 flagged almost all decoy identifications in validating the open-search datasets. In addition, the function of validating identifications given by MaxQuant and MS-GF+ was implemented in pValid 2, and the validation results showed that pValid 2 performed dramatically better than three metabolic labeling validation methods. Further considering its cost-effectiveness as a pure computational approach, pValid 2 has the potential to be a widely used validation tool for peptide identifications of any proteome search engines in shotgun proteomics. SIGNIFICANCE: Identification results given by shotgun proteomics are vital to life science research. The correctness of identifications deeply affects the precision of the subsequent studies about protein structures and functions, protein-protein interactions, pathogenic mechanism, and targeted drugs. Thus, validating the correctness of identifications is crucial and urgent. In 2019, we developed an identification credibility validation method named pValid, whose false positive rate (FPR) is 0.03% and false negative rate (FNR) is 1.79%, comparable to those of the gold standard, i.e., the Synthetic-peptide validation method. However, pValid can only be used for validating the results from pFind, and its validation performance on a few complex datasets still has room for improvement. So, in this submission, we proposed pValid 2, a more comprehensive computational validation method that can validate identifications from any proteome search engines with increased discriminating power.

Spectrum prediction using deep learning has attracted a lot of attention in recent years. Although existing deep learning methods have dramatically increased the pre-diction accuracy, there is still considerable space for improvement, which is presently limited by the difference of fragmentation types or instrument settings. In this work, we use the few-shot learning method to fit the data online to make up for the shortcoming. The method is evaluated using ten datasets, where the instruments includes Velos, QE, Lumos, and Sciex, with collision energies being differently set. Experimental results show that few-shot learning can achieve higher prediction accuracy with almost negligible computing resources. For example, on the dataset from a untrained instrument Sciex-6600, within about 10 seconds, the prediction accuracy is increased from 69.7% to 86.4%; on the CID (collision-induced dissociation) dataset, the prediction accuracy of the model trained by HCD (higher energy collision dissociation) spectra is increased from 48.0% to 83.9%. It is also shown that, the method is not critical to data quality and is sufficiently efficient to fill the accuracy gap. The source code of pDeep3 is available at http://pfind.ict.ac.cn/software/pdeep3.

In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues., Protein glycosylation is a heterogeneous post-translational modification that generates greater proteomic diversity that is difficult to analyze. Here the authors describe pGlyco 2.0, a workflow for the precise one step identification of intact N-glycopeptides at the proteome scale.

Chemical cross-linking of proteins coupled with mass spectrometry analysis (CXMS) is widely used to study protein-protein interactions (PPI), protein structures, and even protein dynamics. However, structural information provided by CXMS is still limited, partly because most CXMS experiments use lysine-lysine (K-K) cross-linkers. Although superb in selectivity and reactivity, they are ineffective for lysine deficient regions. Herein, we develop aromatic glyoxal cross-linkers (ArGOs) for arginine-arginine (R-R) cross-linking and the lysine-arginine (K-R) cross-linker KArGO. The R-R or K-R cross-links generated by ArGO or KArGO fit well with protein crystal structures and provide information not attainable by K-K cross-links. KArGO, in particular, is highly valuable for CXMS, with robust performance on a variety of samples including a kinase and two multi-protein complexes. In the case of the CNGP complex, KArGO cross-links covered as much of the PPI interface as R-R and K-K cross-links combined and improved the accuracy of Rosetta docking substantially., Cross-linking mass spectrometry can provide insights into protein structures and interactions but its scope depends on the reactivity of the cross-linker. Here, the authors develop Arg-Arg and Lys-Arg cross-linkers, which provide structural information elusive to the widely used Lys-Lys cross-linkers.

In the past decade, tandem mass spectrometry (MS/MS)-based bottom-up proteomics has become the method of choice for analyzing post-translational modifications (PTMs) in complex mixtures. The key to the identification of the PTM-containing peptides and localization of the PTM-modified residues is to measure the similarities between the theoretical spectra and the experimental ones. An accurate prediction of the theoretical MS/MS spectra of the modified peptides will improve the similarity measurement. Here, we proposed the deep-learning-based pDeep2 model for PTMs. We used the transfer learning technique to train pDeep2, facilitating the training with a limited scale of benchmark PTM data. Using the public synthetic PTM data sets, including the synthetic phosphopeptides and 21 synthetic PTMs from ProteomeTools, we showed that the model trained by transfer learning was accurate (>80% Pearson correlation coefficients were higher than 0.9), and was significantly better than the models trained without transfer learning. We also showed that accurate prediction of the fragment ion intensities of the PTM neutral loss, for example, the phosphoric acid loss (-98 Da) of the phosphopeptide, will improve the discriminating power to distinguish the true phosphorylated residue from its adjacent candidate sites. pDeep2 is available at https://github.com/pFindStudio/pDeep/tree/master/pDeep2 .

As the de facto validation method in mass spectrometry-based proteomics, the target-decoy approach determines a threshold to estimate the false discovery rate and then filters those identifications beyond the threshold. However, the incorrect identifications within the threshold are still unknown and further validation methods are needed. In this study, we characterized a framework of validation and investigated a number of common and novel validation methods. We first defined the accuracy of a validation method by its false-positive rate (FPR) and false-negative rate (FNR) and, further, proved that a validation method with lower FPR and FNR led to identifications with higher sensitivity and precision. Then we proposed a validation method named pValid that incorporated an open database search and a theoretical spectrum prediction strategy via a machine-learning technology. pValid was compared with four common validation methods as well as a synthetic peptide validation method. Tests on three benchmark data sets indicated that pValid had an FPR of 0.03% and an FNR of 1.79% on average, both superior to the other four common validation methods. Tests on a synthetic peptide data set also indicated that the FPR and FNR of pValid were better than those of the synthetic peptide validation method. Tests on a large-scale human proteome data set indicated that pValid successfully flagged the highest number of incorrect identifications among all five methods. Further considering its cost-effectiveness, pValid has the potential to be a feasible validation tool for peptide identification.

Chemical cross-linking of proteins coupled with mass spectrometry (CXMS) is a powerful tool to study protein folding and to map the interfaces between interacting proteins. The most commonly used cross-linkers in CXMS are BS(3) and DSS, which have similar structures and generate the same linkages between pairs of lysine residues in spatial proximity. However, there are cases where no cross-linkable lysine pairs are present at certain regions of a protein or at the interface of two interacting proteins. In order to find the cross-linkers that can best complement the performance of BS(3) and DSS, we tested seven additional cross-linkers that either have different spacer arm structures or that target different amino acids (BS(2)G, EGS, AMAS, GMBS, Sulfo-GMBS, EDC, and TFCS). Using BSA, aldolase, the yeast H/ACA protein complex, and E. coli 70S ribosomes, we showed that, in terms of providing structural information not obtained through the use of BS(3) and DSS, EGS and Sulfo-GMBS worked better than the other cross-linkers that we tested. EGS generated a large number of cross-links not seen with the other amine-specific cross-linkers, possibly due to its hydrophilic spacer arm. We demonstrate that incorporating the cross-links contributed by the EGS and amine-sulfhydryl cross-linkers greatly increased the accuracy of Rosetta in docking the structure of the yeast H/ACA protein complex. Given the improved depth of useful information it can provide, we suggest that the multilinker CXMS approach should be used routinely when the amount of a sample permits.

There has been tremendous progress in top-down proteomics (TDP) in the past 5 years, particularly in intact protein separation and high-resolution mass spectrometry. However, bioinformatics to deal with large-scale mass spectra has lagged behind, in both algorithmic research and software development. In this study, we developed pTop 1.0, a novel software tool to significantly improve the accuracy and efficiency of mass spectral data analysis in TDP. The precursor mass offers crucial clues to infer the potential post-translational modifications co-occurring on the protein, the reliability of which relies heavily on its mass accuracy. Concentrating on detecting the precursors more accurately, a machine-learning model incorporating a variety of spectral features was trained online in pTop via a support vector machine (SVM). pTop employs the sequence tags extracted from the MS/MS spectra and a dynamic programming algorithm to accelerate the search speed, especially for those spectra with multiple post-translational modifications. We tested pTop on three publicly available data sets and compared it with ProSight and MS-Align+ in terms of its recall, precision, running time, and so on. The results showed that pTop can, in general, outperform ProSight and MS-Align+. pTop recalled 22% more correct precursors, although it exported 30% fewer precursors than Xtract (in ProSight) from a human histone data set. The running speed of pTop was about 1 to 2 orders of magnitude faster than that of MS-Align+. This algorithmic advancement in pTop, including both accuracy and speed, will inspire the development of other similar software to analyze the mass spectra from the entire proteins.

Database search is the dominant approach in high-throughput proteomic analysis. However, the interpretation rate of MS/MS spectra is very low in such a restricted mode, which is mainly due to unexpected modifications and irregular digestion types. In this study, we developed a new algorithm called Alioth, to be integrated into the search engine of pFind, for fast and accurate unrestricted database search on high-resolution MS/MS data. An ion index is constructed for both peptide precursors and fragment ions, by which arbitrary digestions and a single site of any modifications and mutations can be searched efficiently. A new re-ranking algorithm is used to distinguish the correct peptide-spectrum matches from random ones. The algorithm is tested on several HCD datasets and the interpretation rate of MS/MS spectra using Alioth is as high as 60%-80%. Peptides from semi- and non-specific digestions, as well as those with unexpected modifications or mutations, can be effectively identified using Alioth and confidently validated using other search engines. The average processing speed of Alioth is 5-10 times faster than some other unrestricted search engines and is comparable to or even faster than the restricted search algorithms tested.This article is part of a Special Issue entitled: Computational Proteomics.

Mass spectrometry (MS) analysis of peptides has traditionally been conducted in the positive ion mode on protonated peptides, although negative ion-mode analysis of deprotonated peptides can provide complementary and sometimes critical structural information. This is partly due to insufficient understanding of the fragmentation behaviors of deprotonated peptides. Here, using ion-trap collision-induced dissociation (CID) and higher-energy collisional dissociation (HCD, a beam-type CID), we characterized the fragmentation patterns of 36 deprotonated peptides of 4–16 amino acids on a high-resolution, high-mass accuracy instrument. Our study finds that among the backbone cleavage products, y-, c- and z-type ions (using a nomenclature similar to that of positive ions) are the most dominant species in both CID and HCD spectra of deprotonated peptides, accompanied by abundant neutral loss (NL) peaks. Similar to the charge-dependency of collisional energy of protonated peptides, we find that singly charged deprotonated peptides require significantly higher collisional energy than their doubly or multiply charged counterparts to reach 50% fragmentation. HCD is generally better than CID for peptide sequencing in the negative ion mode, since HCD generates more backbone cleavage products whereas CID produces predominately NL peaks of precursors. For disulfide-bonded peptides and C-terminally amidated peptides, unusual fragmentation is observed in the negative ion mode. The fragmentation behaviors of deprotonated peptides reported in this study will promote further investigation of the fundamental mechanisms and facilitate algorithm development for peptide sequencing in the negative mode.

De novo peptide sequencing for large-scale proteomics remains challenging because of the lack of full coverage of ion series in tandem mass spectra. We developed a mirror protease of trypsin, acetylated LysargiNase (Ac-LysargiNase), with superior activity and stability. The mirror spectrum pairs derived from the Ac-LysargiNase and trypsin treated samples can generate full b and y ion series, which provide mutual complementarity of each other, and allow us to develop a novel algorithm, pNovoM, for de novo sequencing. Using pNovoM to sequence peptides of purified proteins, the accuracy of the sequence was close to 100%. More importantly, from a large-scale yeast proteome sample digested with trypsin and Ac-LysargiNase individually, 48% of all tandem mass spectra formed mirror spectrum pairs, 97% of which contained full coverage of ion series, resulting in precision de novo sequencing of full-length peptides by pNovoM. This enabled pNovoM to successfully sequence 21,249 peptides from 3,753 proteins and interpreted 44–152% more spectra than pNovo+ and PEAKS at a 5% FDR at the spectrum level. Moreover, the mirror protease strategy had an obvious advantage in sequencing long peptides. We believe that the combination of mirror protease strategy and pNovoM will be an effective approach for precision de novo sequencing on both single proteins and proteome samples.

We present a sequence-tag-based search engine, Open-pFind, to identify peptides in an ultra-large search space that includes coeluting peptides, unexpected modifications and digestions. Our method detects peptides with higher precision and speed than seven other search engines. Open-pFind identified 70-85% of the tandem mass spectra in four large-scale datasets and 14,064 proteins, each supported by at least two protein-unique peptides, in a human proteome dataset.

Motivation: Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes. Results: To quantitatively model this problem, we theoretically analyze the subgroup false discovery rates of annotated and novel peptides. Our analysis shows that the annotation completeness ratio of a genome is the dominant factor influencing the subgroup FDR of novel peptides. Experimental results on two real datasets of Escherichia coli and Mycobacterium tuberculosis support our conjecture. Contact: yfu@amss.ac.cn or xupingghy@gmail.com or smhe@ict.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Protein N-terminal profiling is crucial when characterizing biological functions and provides proteomic evidences for genome reannotations. However, most of the current N-terminal enrichment approaches involve multiple chemical derivatizations and chromatographic separation processes which are time consuming and can contribute to N-terminal peptide losses. In this study, a fast, one-step approach utilizing (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) derivatization and StageTip separation was developed to enhance N-terminal peptide enrichment and analysis. Based on the characteristics of TMPP-derivatized samples, such as a higher hydrophobicity and increased likelihood to produce a and b ions in collision-induced dissociation or HCD fragmentation modes, first the SDS-PAGE was optimized to increase protein loading and gel entry and to remove unbound TMPP. Then, this process was combined with a simplified StageTip separation and a new scoring criterion (considering a, b and y ions) to identify more TMPP-modified N-terminal spectra. When utilizing a low amount of starting material (∼20 μg protein), a total of 581 yeast N-terminal peptides were identified, with 485 of them being TMPP modified, in only about one third of the general experimental time. It is hoped that the workflow constructed herein will provide a fast and practical strategy for N-terminomic studies.

Chemical cross-linking coupled with mass spectroscopy (CXMS) provides proximity information for the cross-linked residues and is used increasingly for modeling protein structures. However, experimentally identified cross-links are sometimes incompatible with the known structure of a protein, as the distance calculated between the cross-linked residues far exceeds the maximum length of the cross-linker. The discrepancies may persist even after eliminating potentially false cross-links and excluding intermolecular ones. Thus the "over-length" cross-links may arise from alternative excited-state conformation of the protein. Here we present a method and associated software DynaXL for visualizing the ensemble structures of multidomain proteins based on intramolecular cross-links identified by mass spectrometry with high confidence. Representing the cross-linkers and cross-linking reactions explicitly, we show that the protein excited-state structure can be modeled with as few as two over-length cross-links. We demonstrate the generality of our method with three systems: calmodulin, enzyme I, and glutamine-binding protein, and we show that these proteins alternate between different conformations for interacting with other proteins and ligands. Taken together, the over-length chemical cross-links contain valuable information about protein dynamics, and our findings here illustrate the relationship between dynamic domain movement and protein function.

De novo peptide sequencing is the only tool for extracting peptide sequences directly from tandem mass spectrometry (MS) data without any protein database. However, neither the accuracy nor the efficiency of de novo sequencing has been satisfactory, mainly due to incomplete fragmentation information in experimental spectra. Recent advancement in MS technology has enabled acquisition of higher energy collisional dissociation (HCD) and electron transfer dissociation (ETD) spectra of the same precursor. These spectra contain complementary fragmentation information and can be collected with high resolution and high mass accuracy. Taking these advantages, we have developed a new algorithm called pNovo+, which greatly improves the accuracy and speed of de novo sequencing. On tryptic peptides, 86% of the topmost candidate sequences deduced by pNovo+ from HCD + ETD spectral pairs matched the database search results, and the success rate reached 95% if the top three candidates were included, which was much higher than using only HCD (87%) or only ETD spectra (57%). On Asp-N, Glu-C, or Elastase digested peptides, 69-87% of the HCD + ETD spectral pairs were correctly identified by pNovo+ among the topmost candidates, or 84-95% among the top three. On average, it takes pNovo+ only 0.018 s to extract the sequence from a spectrum or spectral pair on a common personal computer. This is more than three times as fast as other de novo sequencing programs. The increase of speed is mainly due to pDAG, a component algorithm of pNovo+. pDAG finds the k longest paths in a directed acyclic graph without the antisymmetry restriction. We have verified that the antisymmetry restriction is unnecessary for high resolution, high mass accuracy data. The extensive use of HCD and ETD spectral information and the pDAG algorithm make pNovo+ an excellent de novo sequencing tool.

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction. DOI: http://dx.doi.org/10.7554/eLife.12509.001, eLife digest Proteins fold into structures that are determined by the order of the amino acids that they are built from. These structures enable the protein to carry out its role, which often involves interacting with other proteins. Chemical cross-linking coupled with mass spectrometry (CXMS) is a powerful method used to study protein structure and how proteins interact, with a benefit of stabilizing and capturing brief interactions. CXMS uses a chemical compound called a linker that has two arms, each of which can bind specific amino acids in a protein or in multiple proteins. Only when the regions are close to each other can they be “cross-linked” in this way. After cross-linking, the proteins are cut into small pieces known as peptides. The cross-linked peptides are then separated from the non cross-linked ones and characterized. Although CXMS is a popular method, there are aspects about it that limit its use. It does not work well on complex samples that contain lots of different proteins, as it is difficult to separate the cross-linked peptides from the overwhelming amounts of non cross-linked peptides. Also, although it can be used to detect changes in the shape of a protein, which are often crucial to the protein's role, the method has not been smoothed out. Tan, Li et al. have now developed a new cross-linker called Leiker that addresses these limitations. Leiker cross-links the amino acid lysine to another lysine, and contains a molecular tag that allows cross-linked peptides to be efficiently purified away from non cross-linked peptides. As part of a streamlined workflow to detect changes in the shape of a protein, Leiker also contains a region that can be labeled. Analysing a bacterial ribosome, which contains more than 50 proteins, showed that Leiker-based CXMS could detect many more protein interactions than previous studies had. These included interactions that changed too rapidly to be studied by other structural methods. Tan, Li et al. then applied Leiker-based CXMS to the entire contents of bacterial cells at different stages of growth, and identified a protein interaction that is only found in growing cells. In future, Leiker will be useful for analyzing the structure of large protein complexes, probing changes in protein structure, and mapping the interactions between proteins in complex mixtures. DOI: http://dx.doi.org/10.7554/eLife.12509.002

Confident characterization of the microheterogeneity of protein glycosylation through identification of intact glycopeptides remains one of the toughest analytical challenges for glycoproteomics. Recently proposed mass spectrometry (MS)-based methods still have some defects such as lack of the false discovery rate (FDR) analysis for the glycan identification and lack of sufficient fragmentation information for the peptide identification. Here we proposed pGlyco, a novel pipeline for the identification of intact glycopeptides by using complementary MS techniques: 1) HCD-MS/MS followed by product-dependent CID-MS/MS was used to provide complementary fragments to identify the glycans and a novel target-decoy method was developed to estimate the false discovery rate of the glycan identification; 2) data-dependent acquisition of MS3 for some most intense peaks of HCD-MS/MS was used to provide fragments to identify the peptide backbones. By integrating HCD-MS/MS, CID-MS/MS and MS3, intact glycopeptides could be confidently identified. With pGlyco, a standard glycoprotein mixture was analyzed in the Orbitrap Fusion and 309 non-redundant intact glycopeptides were identified with detailed spectral information of both glycans and peptides.

Anthrax, caused by the pathogenic bacterium Bacillus anthracis, is a zoonosis that causes serious disease and is of significant concern as a biological warfare agent. Validating annotated genes and reannotating misannotated genes are important to understand its biology and mechanisms of pathogenicity. Proteomics studies are, to date, the best method for verifying and improving current annotations. To this end, the proteome of B. anthracis A16R was analyzed via one-dimensional gel electrophoresis followed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). In total, we identified 3,712 proteins, including many regulatory and key functional proteins at relatively low abundance, representing the most complete proteome of B. anthracis to date. Interestingly, eight sequencing errors were detected by proteogenomic analysis and corrected by resequencing. More importantly, three unannotated peptide fragments were identified in this study and validated by synthetic peptide mass spectrum mapping and green fluorescent protein fusion experiments. These data not only give a more comprehensive understanding of B. anthracis A16R but also demonstrate the power of proteomics to improve genome annotations and determine true translational elements.

In recent years, electron transfer dissociation (ETD) has enjoyed widespread applications from sequencing of peptides with or without post-translational modifications to top-down analysis of intact proteins. However, peptide identification rates from ETD spectra compare poorly with those from collision induced dissociation (CID) spectra, especially for doubly charged precursors. This is in part due to an insufficient understanding of the characteristics of ETD and consequently a failure of database search engines to make use of the rich information contained in the ETD spectra. In this study, we statistically characterized ETD fragmentation patterns from a collection of 461 440 spectra and subsequently implemented our findings into pFind, a database search engine developed earlier for CID data. From ETD spectra of doubly charged precursors, pFind 2.1 identified 63-122% more unique peptides than Mascot 2.2 under the same 1% false discovery rate. For higher charged peptides as well as phosphopeptides, pFind 2.1 also consistently obtained more identifications. Of the features built into pFind 2.1, the following two greatly enhanced its performance: (1) refined automatic detection and removal of high-intensity peaks belonging to the precursor, charge-reduced precursor, or related neutral loss species, whose presence often set spectral matching askew; (2) a thorough consideration of hydrogen-rearranged fragment ions such as z + H and c - H for peptide precursors of different charge states. Our study has revealed that different charge states of precursors result in different hydrogen rearrangement patterns. For a fragment ion, its propensity of gaining or losing a hydrogen depends on (1) the ion type (c or z) and (2) the size of the fragment relative to the precursor, and both dependencies are affected by (3) the charge state of the precursor. In addition, we discovered ETD characteristics that are unique for certain types of amino acids (AAs), such as a prominent neutral loss of SCH(2)CONH(2) (90.0014 Da) from z ions with a carbamidomethylated cysteine at the N-terminus and a neutral loss of histidine side chain C(4)N(2)H(5) (81.0453 Da) from precursor ions containing histidine. The comprehensive list of ETD characteristics summarized in this paper should be valuable for automated database search, de novo peptide sequencing, and manual spectral validation.

Motivation: Identification of post-translationally modified proteins has become one of the central issues of current proteomics. Spectral library search is a new and promising computational approach to mass spectrometry-based protein identification. However, its potential in identification of unanticipated post-translational modifications has rarely been explored. The existing spectral library search tools are designed to match the query spectrum to the reference library spectra with the same peptide mass. Thus, spectra of peptides with unanticipated modifications cannot be identified. Results: In this article, we present an open spectral library search tool, named pMatch. It extends the existing library search algorithms in at least three aspects to support the identification of unanticipated modifications. First, the spectra in library are optimized with the full peptide sequence information to better tolerate the peptide fragmentation pattern variations caused by some modification(s). Second, a new scoring system is devised, which uses charge-dependent mass shifts for peak matching and combines a probability-based model with the general spectral dot-product for scoring. Third, a target-decoy strategy is used for false discovery rate control. To demonstrate the effectiveness of pMatch, a library search experiment was conducted on a public dataset with over 40 000 spectra in comparison with SpectraST, the most popular library search engine. Additional validations were done on four published datasets including over 150 000 spectra. The results showed that pMatch can effectively identify unanticipated modifications and significantly increase spectral identification rate. Availability: http://pfind.ict.ac.cn/pmatch/ Contact: yfu@ict.ac.cn; rxsun@ict.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Protein sequence database search based on tandem mass spectrometry is an essential method for protein identification. As the computational demand increases, parallel computing has become an important technique for accelerating proteomics data analysis. In this paper, we discuss several factors which could affect the runtime of the pFind search engine and build an estimation model. Based on this model, effective on-line and off-line scheduling methods were developed. An experiment on the public dataset from PhosphoPep consisting of 100 RAW files of phosphopeptides shows that the speedup on 100 processors is 83.7. The parallel version can complete the identification task within 9 min, while a stand-alone process on a single PC takes more than 10 h. On another larger dataset consisting of 1,366,471 spectra, the speedup on 320 processors is 258.9 and the efficiency is 80.9%. Our approach can be applied to other similar search engines.

De novo peptide sequencing has improved remarkably in the past decade as a result of better instruments and computational algorithms. However, de novo sequencing can correctly interpret only approximately 30% of high- and medium-quality spectra generated by collision-induced dissociation (CID), which is much less than database search. This is mainly due to incomplete fragmentation and overlap of different ion series in CID spectra. In this study, we show that higher-energy collisional dissociation (HCD) is of great help to de novo sequencing because it produces high mass accuracy tandem mass spectrometry (MS/MS) spectra without the low-mass cutoff associated with CID in ion trap instruments. Besides, abundant internal and immonium ions in the HCD spectra can help differentiate similar peptide sequences. Taking advantage of these characteristics, we developed an algorithm called pNovo for efficient de novo sequencing of peptides from HCD spectra. pNovo gave correct identifications to 80% or more of the HCD spectra identified by database search. The number of correct full-length peptides sequenced by pNovo is comparable with that obtained by database search. A distinct advantage of de novo sequencing is that deamidated peptides and peptides with amino acid mutations can be identified efficiently without extra cost in computation. In summary, implementation of the HCD characteristics makes pNovo an excellent tool for de novo peptide sequencing from HCD spectra.

Glycosylation is the most versatile and one of the most significant protein post-translational modifications. It is generally classified into three categories according to the amino acid to which the glycan is attached: N-glycosylation, O-glycosylation and C-glycosylation. Synthesis of N-glycoproteins occurs in the rough endoplasmic reticulum (rER), and all N-glycoproteins synthesized in rER have uniform glycan endings with mannose (Man) and glucose (Glc). A systematic strategy was developed to comprehensively profile N-glycoproteins in complex biological samples. The lectin Concanvalin A (ConA), which has a high affinity for glycans ending with Man, was used to extract the N-glycoproteins synthesized or located in the rER, and identified the N-glycoproteins and their glycosylation sites by LTQ-FT MS. The analysis was repeated three times at both the biological sample and mass spectrometry levels. In total, 323 glycosylation sites on 212 N-glycoproteins were identified in the mouse liver. Of these, 131 were known N-glycoproteins in the Swissprot database. The identified N-glycoproteins were classified according to their cell location by the Swissprot database and GO software. The identified N-glycoproteins in this study would serve as a good complement to the N-glycoprotein database for the mouse liver.

Database searching is the technique of choice for shotgun proteomics, and to date much research effort has been spent on improving its effectiveness. However, database searching faces a serious challenge of efficiency, considering the large numbers of mass spectra and the ever fast increase in peptide databases resulting from genome translations, enzymatic digestions, and post-translational modifications. In this study, we conducted systematic research on speeding up database search engines for protein identification and illustrate the key points with the specific design of the pFind 2.1 search engine as a running example. Firstly, by constructing peptide indexes, pFind achieves a speedup of two to three compared with that without peptide indexes. Secondly, by constructing indexes for observed precursor and fragment ions, pFind achieves another speedup of two. As a result, pFind compares very favorably with predominant search engines such as Mascot, SEQUEST and X!Tandem.

Tandem mass spectra contain noisy peaks which make peak picking for peptide identification difficult. Moreover, all spectral peaks can be shifted due to systematic measurement errors. In this paper, a novel use of an isotope pattern vector (IPV) is proposed for denoising and systematic measurement error prediction. By matching the experimental IPVs with the theoretical IPVs of candidate fragment ions, true ionic peaks can be identified. Furthermore, these identified experimental IPVs and their corresponding theoretical IPVs are used in an optimization process to predict the systematic measurement error associated with the target spectrum. In return, the subsequent spectral data calibration based on the predicted systematic measurement error enhances the data quality. We show that such an integrated denoising and calibration process leads to significantly improved peptide and protein identification. Different from the commonly employed chemical calibration methods, our IPV-based method is a purely computational method for individual spectra analysis and globally optimizes the use of spectral data.

Motivation: RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is proved to be NP-hard. Due to kinetic reasons the real RNA secondary structure often has local instead of global minimum free energy. This implies that we may improve the performance of RNA secondary structure prediction by taking kinetics into account and minimize free energy in a local area. Result: we propose a novel algorithm named FlexStem to predict RNA secondary structures with pseudoknots. Still based on MFE criterion, FlexStem adopts comprehensive energy models that allow complex pseudoknots. Unlike classical thermodynamic methods, our approach aims to simulate the RNA folding process by successive addition of maximal stems, reducing the search space while maintaining or even improving the prediction accuracy. This reduced space is constructed by our maximal stem strategy and stem-adding rule induced from elaborate statistical experiments on real RNA secondary structures. The strategy and the rule also reflect the folding characteristic of RNA from a new angle and help compensate for the deficiency of merely relying on MFE in RNA structure prediction. We validate FlexStem by applying it to tRNAs, 5SrRNAs and a large number of pseudoknotted structures and compare it with the well-known algorithms such as RNAfold, PKNOTS, PknotsRG, HotKnots and ILM according to their overall sensitivities and specificities, as well as positive and negative controls on pseudoknots. The results show that FlexStem significantly increases the prediction accuracy through its local search strategy. Availability: Software is available at http://pfind.ict.ac.cn/FlexStem/ Contact: xchen@jdl.ac.cn; wgao@pku.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Scalability considerations drive the evolution of switch design from output queueing to input queueing and further to combined input and crosspoint queueing (CICQ). However, CICQ switches with credit-based flow control face new challenges of scalability and predictability. In this paper, we propose a novel approach of rate-based smoothed switching, and design a CICQ switch called the smoothed buffered crossbar or sBUX. First, the concept of smoothness is developed from two complementary perspectives of covering and spacing, which, commonly known as fairness and jitter, are unified in the same model. Second, a smoothed multiplexer sMUX is designed that allocates bandwidth among competing flows sharing a link and guarantees almost ideal smoothness for each flow. Third, the buffered crossbar sBUX is designed that uses the scheduler sMUX at each input and output, and a two-cell buffer at each crosspoint. It is proved that sBUX guarantees 100% throughput for real-time services and almost ideal smoothness for each flow. Fourth, an on-line bandwidth regulator is designed that periodically estimates bandwidth demand and generates admissible allocations, which enables sBUX to support best-effort services. Simulation shows almost 100% throughput and multi-microsecond average delay. In particular, neither credit-based flow control nor speedup is used, and arbitrary fabric-internal latency is allowed between line cards and the switch core, simplifying the switch implementation.