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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., (Copyright © 2015 Elsevier B.V. All rights reserved.)

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.

Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified.

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.

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/., Supplementary Information: Supplementary data are available at Bioinformatics online.