pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry

Li, Dequan; Fu, Yan; Sun, Rui-Xiang; Ling, Charles X.; Wei, Yonggang; Zhou, Hu; Zeng, Rong; Yang, Qiang; He, Shan; Gao, Wen

doi:10.1093/bioinformatics/bti439

Cited by 155 publications

(130 citation statements)

References 2 publications

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“…In all of the experiments, the most widely used software for peptide identification, Mascot (version 2.2) (7), was used to identify the spectra. Two other search engines, SEQUEST (version 2.7) (6) and pFind (version 2.6) (8,26,27), were additionally used to demonstrate the linearity of the ␥ k ͑x͒ function (Fig. 1).…”

Section: Resultsmentioning

confidence: 99%

Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry

Qian

2014

Molecular & Cellular Proteomics

122

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In shotgun proteomics, high-throughput mass spectrometry experiments and the subsequent data analysis produce thousands to millions of hypothetical peptide identifications. The common way to estimate the false discovery rate (FDR) of peptide identifications is the target-decoy database search strategy, which is efficient and accurate for large datasets. However, the legitimacy of the target-decoy strategy for protein-modification-centric studies has rarely been rigorously validated. It is often the case that a global FDR is estimated for all peptide identifications including both modified and unmodified peptides, but that only a subgroup of identifications with a certain type of modification is focused on. As revealed recently, the subgroup FDR of modified peptide identifications can differ dramatically from the global FDR at the same score threshold, and thus the former, when it is of interest, should be separately estimated. However, rare modifications often result in a very small number of modified peptide identifications, which makes the direct separate FDR estimation inaccurate because of the inadequate sample size. This paper presents a method called the transferred FDR for accurately estimating the FDR of an arbitrary number of modified peptide identifications. Through flexible use of the empirical data from a targetdecoy database search, a theoretical relationship between the subgroup FDR and the global FDR is made computable. Through this relationship, the subgroup FDR can be predicted from the global FDR, allowing one to avoid an inaccurate direct estimation from a limited amount of data. The effectiveness of the method is demonstrated with both simulated and real mass spectra. Molecular & Cellular

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Section: Resultsmentioning

confidence: 99%

Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry

Qian

2014

Molecular & Cellular Proteomics

122

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“…All MS/MS samples were analyzed with MASCOT 2.3 (Matrix Science, London, U.K.) and pFind Studio 2.6 (36,37) to improve the identification of the acetylated peptides. Both pFind and MASCOT were used to search a database of forward and reversed M. tuberculosis H37Ra proteins from National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/) that contained 4034 protein sequences.…”

Section: Methodsmentioning

confidence: 99%

Acetylome Analysis Reveals Diverse Functions of Lysine Acetylation in Mycobacterium tuberculosis

Liu

Yang

Wang

et al. 2014

Molecular & Cellular Proteomics

142

169

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“…The pFind studio package (v3.10) was used for database searching and data analysis (39). Acquired MS/MS spectra were searched against a target-decoy version of the UniProt human protein database (May 16, 2014 release, 88,976 entries) consisting of forward and reverse protein sequences.…”

Section: Nano-lc-ms/ms Analysis and Protein Databasementioning

confidence: 99%

Quantitative Profiling of the Activity of Protein Lysine Methyltransferase SMYD2 Using SILAC-Based Proteomics

Olsen

Cao

Han

et al. 2016

Molecular & Cellular Proteomics

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The significance of non-histone lysine methylation in cell biology and human disease is an emerging area of research exploration. The development of small molecule inhibitors that selectively and potently target enzymes that catalyze the addition of methyl-groups to lysine residues, such as the protein lysine mono-methyltransferase SMYD2, is an active area of drug discovery. Critical to the accurate assessment of biological function is the ability to identify target enzyme substrates and to define enzyme substrate specificity within the context of the cell. Here, using stable isotopic labeling with amino acids in cell culture (SILAC) coupled with immunoaffinity enrichment of mono-methyl-lysine (Kme1) peptides and mass spectrometry, we report a comprehensive, large-scale proteomic study of lysine mono-methylation, comprising a total of 1032 Kme1 sites in esophageal squamous cell carcinoma (ESCC) cells and 1861 Kme1 sites in ESCC cells overexpressing SMYD2. Among these Kme1 sites is a subset of 35 found to be potently down-regulated by both shRNA-mediated knockdown of SMYD2 and LLY-507, a selective small molecule inhibitor of SMYD2. In addition, we report specific protein sequence motifs enriched in Kme1 sites that are directly regulated by endogenous SMYD2 activity, revealing that SMYD2 substrate specificity is more diverse than expected. We further show direct activity of SMYD2 toward BTF3-K2, PDAP1-K126 as well as numerous sites within the repetitive units of two unique and exceptionally large proteins, AHNAK and AHNAK2. Collectively, our findings provide quantitative insights into the cellular activity and substrate recognition of SMYD2 as well as the global landscape and regulation of protein mono-methylation. Protein lysine methyltransferases (PKMTs) 1 catalyze the sequence-specific transfer of one, two, or three methyl groups to the side chains of lysine residues (1-4). In addition to the extensively studied lysine methylation on histones, PKMTs can modify non-histone proteins (3,(5)(6)(7)(8)). An increasing number of non-histone proteins have been reported as PKMT substrates, and, as a result, potential roles for the dysregulation of non-histone lysine methylation in cancer development and progression have been proposed (9). The majority of PKMT substrates have been identified based on biochemical methylation assays using recombinant enzyme and substrate, followed by cell-based assays typically requiring overexpression of enzyme and/or substrate to maximize signal detection (10 -13). However, it is difficult to discern whether these substrates are bona fide physiological substrates of endogenous PKMT activity. With the development of PKMT-targeted drugs emerging as a key area of drug discovery (14 -18), unbiased and quantitative methods enabling the comprehensive identification of histone and non-histone substrates in cells are critical to clarifying the cell-relevant substrates of these enzymes and to more accurately understand the functions of non-histone methylation.Progressing from targeted biochemical...

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pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry

Cited by 155 publications

References 2 publications

Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry

Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry

Acetylome Analysis Reveals Diverse Functions of Lysine Acetylation in Mycobacterium tuberculosis

Quantitative Profiling of the Activity of Protein Lysine Methyltransferase SMYD2 Using SILAC-Based Proteomics

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