Comparison of Novel Decoy Database Designs for Optimizing Protein Identification Searches Using ABRF sPRG2006 Standard MS/MS Data Sets

Blanco, Luca; Mead, Jennifer; Bessant, Conrad

doi:10.1021/pr800792z

Cited by 36 publications

(43 citation statements)

References 25 publications

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“…The expectation value was defined as a composite score for the quality assessment of peptide identification, and the FDR of peptide identifications was consequently estimated using target-decoy searching (61). A decoy search independent from the target one was conducted according to a recommended method (62). The E-value thresholds for single isotope-coded peptide identification and double isotope-coded peptide identification were determined to be 0.275 and 1.65, respectively, for FDR Յ 1% (see "Experimental Procedures"; supplemental Fig.…”

Section: Lc-ms/ms-based Phosphopeptide Profiling Of Ethylenetreated Wmentioning

confidence: 99%

Stable Isotope Metabolic Labeling-based Quantitative Phosphoproteomic Analysis of Arabidopsis Mutants Reveals Ethylene-regulated Time-dependent Phosphoproteins and Putative Substrates of Constitutive Triple Response 1 Kinase

Yang

Guo

Zhang

et al. 2013

Molecular & Cellular Proteomics

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Ethylene is an important plant hormone that regulates numerous cellular processes and stress responses. The mode of action of ethylene is both dose-and time-dependent. Protein phosphorylation plays a key role in ethylene signaling, which is mediated by the activities of ethylene receptors, constitutive triple response 1 (CTR1) kinase, and phosphatase. To address how ethylene alters the cellular protein phosphorylation profile in a time-dependent manner, differential and quantitative phosphoproteomics based on 15 N stable isotope labeling in Arabidopsis was performed on both one-minute ethylene-treated Arabidopsis ethylene-overly-sensitive loss-of-function mutant rcn1-1, deficient in PP2A phosphatase activity, and a pair of long-term ethylene-treated wild-type and loss-of-function ethylene signaling ctr1-1 mutants, deficient in mitogen-activated kinase kinase kinase activity. In total, 1079 phosphopeptides were identified, among which 44 were novel. Several one-minute ethylene-regulated phosphoproteins were found from the rcn1-1. Bioinformatic analysis of the rcn1-1 phosphoproteome predicted nine phosphoproteins as the putative substrates for PP2A phosphatase. In addition, from CTR1 kinase-enhanced phosphosites, we also found putative CTR1 kinase substrates including plastid transcriptionally active protein and calcium-sensing receptor. These regulatory proteins are phosphorylated in the presence of ethylene. Analysis of ethyleneregulated phosphosites using the group-based prediction system with a protein-protein interaction filter revealed a total of 14 kinase-substrate relationships that may function in both CTR1 kinase-and PP2A phosphatase-mediated phosphor-relay pathways. Ethylene is a volatile plant hormone that regulates versatile molecular and physiological processes in higher plants (1). The perception of this gaseous two-carbon hormone is achieved by a group of membrane-associated dimeric ethylene receptors that resemble bacterial two-component signaling systems and are composed of hybrid histidine (or aspartic acid) kinases, a histidine-containing phosphor-transfer domain, and response regulators (2). These receptors are made of two membrane-bound protein subunits cross-linked at the N-terminal region through two disulfide bonds (3). In Arabidopsis, there are five different ethylene receptor subunits: ethylene response 1, ethylene response 2, ethylene insensitive 4 (EIN4), 1 ethylene response sensor 1, and ethylene re- 1 The abbreviations used are: ACC, aminocyclopropane-1-carboxylic acid; ACN, acetonitrile; CIPK1, CBL-interacting protein kinase 1; CTR1, constitutive triple response 1; eer1-1, enhanced ethylene response 1; EIN, ethylene insensitive; FBH3, flowering bHLH 3 protein; FDR, false discovery rate; GPS, group-based prediction system; HMG, high mobility group; IMAC, immobilized metal-ion-affinity chromatography; iTRAQ, isobaric tag for relative and absolute quantitation; LHCB, light harvetsting chlorophyll A/B binding protein; Research

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Section: Lc-ms/ms-based Phosphopeptide Profiling Of Ethylenetreated Wmentioning

confidence: 99%

Stable Isotope Metabolic Labeling-based Quantitative Phosphoproteomic Analysis of Arabidopsis Mutants Reveals Ethylene-regulated Time-dependent Phosphoproteins and Putative Substrates of Constitutive Triple Response 1 Kinase

Yang

Guo

Zhang

et al. 2013

Molecular & Cellular Proteomics

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“…The num-ber of matches to the decoy database, therefore, provides an estimate of the number of random matches one should expect to obtain in the target database. The number of target and decoy hits can then be used to calculate either a local or a global FDR for a given data set (21)(22)(23)(24)(25)(26). This general idea can be applied to control the FDR at the level of PSMs, peptides, and proteins, typically by counting the number of target and decoy observations above a specified score.…”

mentioning

confidence: 99%

A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

Savitski¹,

Wilhelm

Hahne

et al. 2015

Molecular & Cellular Proteomics

394

320

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Calculating the number of confidently identified proteins and estimating false discovery rate (FDR) is a challenge when analyzing very large proteomic data sets such as entire human proteomes. Biological and technical heterogeneity in proteomic experiments further add to the challenge and there are strong differences in opinion regarding the conceptual validity of a protein FDR and no consensus regarding the methodology for protein FDR determination. There are also limitations inherent to the widely used classic target-decoy strategy that particularly show when analyzing very large data sets and that lead to a strong over-representation of decoy identifications. In this study, we investigated the merits of the classic, as well as a novel target-decoy-based protein FDR estimation approach, taking advantage of a heterogeneous data collection comprised of ϳ19,000 LC-MS/MS runs deposited in ProteomicsDB (https://www.proteomicsdb. org). The "picked" protein FDR approach treats target and decoy sequences of the same protein as a pair rather than as individual entities and chooses either the target or the decoy sequence depending on which receives the highest score. We investigated the performance of this approach in combination with q-value based peptide scoring to normalize sample-, instrument-, and search engine-specific differences. The "picked" target-decoy strategy performed best when protein scoring was based on the best peptide q-value for each protein yielding a stable number of true positive protein identifications over a wide range of q-value thresholds. We show that this simple and unbiased strategy eliminates a conceptual issue in the commonly used "classic" protein FDR approach that causes overprediction of false-positive protein identification in large data sets. The approach scales from small to very large data sets without losing performance, consistently increases the number of true-positive protein identifications and is readily implemented in proteomics analysis software. Molecular & Cellular

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“…The dataset was generated by a linear ion trap-orbitrap (LTQ-Orbitrap) mass spectrometer. Additionally, we consider identifications of all expected and 'bonus' proteins to be ground-truth proteins [10].…”

Section: Dataset Twomentioning

confidence: 99%

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

Liang

Lajoie

Zhang

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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Proteins are large, complex molecules that perform a vast array of functions in every living cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches.MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modification (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches are based on subjecting intact protein ions and large fragment ions to tandem MS directly, while bottom-up methods are based on mass spectrometric analysis of peptides derived from proteolytic digestion, usually with trypsin.In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this thesis, we developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, we designed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input dataset as dependent peptides. Moreover, we use linear regression to adjust the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we design two algorithms: assignment of peaks, and protein filtration. The former restricts that a peak can only be assigned to one peptide in order to reduce random matches; and the latter assumes each peak can only be assigned to one protein. In the result validation, we propose two new target-decoy search strategies to estimate the false discovery rate (FDR). The experiments on simulated, authentic, and simulated authentic dataset demonstrate that our NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.i

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Comparison of Novel Decoy Database Designs for Optimizing Protein Identification Searches Using ABRF sPRG2006 Standard MS/MS Data Sets

Cited by 36 publications

References 25 publications

Stable Isotope Metabolic Labeling-based Quantitative Phosphoproteomic Analysis of Arabidopsis Mutants Reveals Ethylene-regulated Time-dependent Phosphoproteins and Putative Substrates of Constitutive Triple Response 1 Kinase

Stable Isotope Metabolic Labeling-based Quantitative Phosphoproteomic Analysis of Arabidopsis Mutants Reveals Ethylene-regulated Time-dependent Phosphoproteins and Putative Substrates of Constitutive Triple Response 1 Kinase

A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

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