Lev I. Levitsky

Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics

AbstractPyteomics is a cross-platform, open-source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the source code repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

Unbiased False Discovery Rate Estimation for Shotgun Proteomics Based on the Target-Decoy Approach

Target-decoy approach (TDA) is the dominant strategy for false discovery rate (FDR) estimation in mass-spectrometry-based proteomics. One of its main applications is direct FDR estimation based on counting of decoy matches above a certain score threshold. The corresponding equations are widely employed for filtering of peptide or protein identifications. In this work we consider a probability model describing the filtering process and find that, when decoy counting is used for q value estimation and subsequent filtering, a correction has to be introduced into these common equations for TDA-based FDR estimation. We also discuss the scale of variance of false discovery proportion (FDP) and propose using confidence intervals for more conservative FDP estimation in shotgun proteomics. The necessity of both the correction and the use of confidence intervals is especially pronounced when filtering small sets (such as in proteogenomics experiments) and when using very low FDR thresholds.

Combination of Edman degradation of peptides with liquid chromatography/mass spectrometry workflow for peptide identification in bottom-up proteomics

RATIONALE High-throughput methods of proteomics are essential for identification of proteins in a cell or tissue under certain conditions. Most of these methods require tandem mass spectrometry (MS/MS). A multidimensional approach including predictive chromatography and partial chemical degradation could be a valuable alternative and/or addition to MS/MS. METHODS In the proposed strategy peptides are identified in a three-dimensional (3D) search space consisting of retention time (RT), mass, and reduced mass after one-step partial Edman degradation. The strategy was evaluated in silico for two databases: bakers yeast and human proteins. Rates of unambiguous identifications were estimated for mass accuracies from 0.001 to 0.05 Da and RT prediction accuracies from 0.1 to 5 min. Rates of Edman reactions were measured for test peptides. RESULTS A 3D description of proteolytic peptides allowing unambiguous identification without employing MS/MS of up to 95% and 80% of tryptic peptides from the yeast and human proteomes, respectively, was considered. Further extension of the search space to a four-dimensional one by incorporating the second N-terminal amino acid residue as the fourth dimension was also considered and was shown to result in up to 90% of human peptides being identified unambiguously. CONCLUSIONS The proposed 3D search space can be a useful alternative to MS/MS-based peptide identification approach. Experimental implementations of the proposed method within the on-line liquid chromatography/mass spectrometry (LC/MS) and off-line matrix-assisted laser desorption/ionization (MALDI) workflows are in progress.

Comparative evaluation of label‐free quantification methods for shotgun proteomics

RATIONALE Label-free quantification (LFQ) is a popular strategy for shotgun proteomics. A variety of LFQ algorithms have been developed recently. However, a comprehensive comparison of the most commonly used LFQ methods is still rare, in part due to a lack of clear metrics for their evaluation and an annotated and quantitatively well-characterized data set. METHODS Five LFQ methods were compared: spectral counting based algorithms SIN , emPAI, and NSAF, and approaches relying on the extracted ion chromatogram (XIC) intensities, MaxLFQ and Quanti. We used three criteria for performance evaluation: coefficient of variation (CV) of protein abundances between replicates; analysis of variance (ANOVA); and the root-mean-square error of logarithmized calculated concentration ratios, referred to as standard quantification error (SQE). Comparison was performed using a quantitatively annotated publicly available data set. RESULTS The best results in terms of inter-replicate reproducibility were observed for MaxLFQ and NSAF, although they exhibited larger standard quantification errors. Using NSAF, all quantitatively annotated proteins were correctly identified in the Bonferronni-corrected results of the ANOVA test. SIN was found to be the most accurate in terms of SQE. Finally, the current implementations of XIC-based LFQ methods did not outperform the methods based on spectral counting for the data set used in this study. CONCLUSIONS Surprisingly, the performances of XIC-based approaches measured using three independent metrics were found to be comparable with more straightforward and simple MS/MS-based spectral counting approaches. The study revealed no clear leader among the latter. Copyright

Pepxmltk—a format converter for peptide identification results obtained from tandem mass spectrometry data using X!Tandem search engine

1598 1 Shotgun proteomics [1], which is based on liquid chromatography—mass spectrometry (LC⎯MS), is the most commonly employed technique for qualita tive and quantitative analysis of protein samples. In the so called bottom up approach, the protein mixture is typically digested with a proteolytic enzyme, followed by separation of the resulting peptide mixture to reduce its complexity. The peptides from the mixture are then identified using tandem mass spectrometry (MS/MS). For each peptide selected for fragmenta tion, its mass to charge ratio (m/z) is determined, along with the m/z ratios of its fragments obtained with one of the available tandem mass spectrometry meth ods. The product ion mass spectra are then processed by proteomics search engines [2, 3], which attempt to associate each MS/MS spectrum with a peptide from a list of corresponding proteins. Quite often, these results are further post processed with specialized software to increase the specificity and sensitivity of peptide/protein identification, or for quantitation. A general data analysis workflow in shotgun proteomics is presented in the figure.

Brute-Force Approach for Mass Spectrometry-Based Variant Peptide Identification in Proteogenomics without Personalized Genomic Data

AbstractIn a proteogenomic approach based on tandem mass spectrometry analysis of proteolytic peptide mixtures, customized exome or RNA-seq databases are employed for identifying protein sequence variants. However, the problem of variant peptide identification without personalized genomic data is important for a variety of applications. Following the recent proposal by Chick et al. (Nat. Biotechnol. 33, 743–749, 2015) on the feasibility of such variant peptide search, we evaluated two available approaches based on the previously suggested “open” search and the “brute-force” strategy. To improve the efficiency of these approaches, we propose an algorithm for exclusion of false variant identifications from the search results involving analysis of modifications mimicking single amino acid substitutions. Also, we propose a de novo based scoring scheme for assessment of identified point mutations. In the scheme, the search engine analyzes y-type fragment ions in MS/MS spectra to confirm the location of the mutation in the variant peptide sequence. Graphical abstractᅟ

Peptide identification in “shotgun” proteomics using tandem mass spectrometry: Comparison of search engine algorithms

High-throughput proteomics technologies are gaining popularity in different areas of life sciences. One of the main objectives of proteomics is characterization of the proteins in biological samples using liquid chromatography/mass spectrometry analysis of the corresponding proteolytic peptide mixtures. Both the complexity and the scale of experimental data obtained even from a single experimental run require specialized bioinformatic tools for automated data mining. One of the most important tools is a so-called proteomics search engine used for identification of proteins present in a sample by comparing experimental and theoretical tandem mass spectra. The latter are generated for the proteolytic peptides derived from a protein database. Peptide identifications obtained with the search engine are then scored according to the probability of a correct peptide-spectrum match. The purpose of this work was to perform a comparison of different search algorithms using data acquired for complex protein mixtures, including both annotated protein standards and clinical samples. The comparison was performed for three popular search engines: commercially available Mascot, as well as open-source X!Tandem and OMSSA. It was shown that the search engine OMSSA identifies in general a smaller number of proteins, while X!Tandem and Mascot deliver similar performance. We found no compelling reasons for using the commercial search engine instead of its open source competitor.

Validation of Peptide Identification Results in Proteomics Using Amino Acid Counting

The efficiency of proteome analysis depends strongly on the configuration parameters of the search engine. One of the murkiest and nontrivial among them is the list of amino acid modifications included for the search. Here, an approach called AA_stat is presented for uncovering the unexpected modifications of amino acid residues in the protein sequences, as well as possible artifacts of data acquisition or processing, in the results of proteome analyses. The approach is based on comparing the amino acid frequencies of different mass shifts observed using the open search method introduced recently. In this work, the proposed approach is applied to publicly available proteomic data is applied and its feasibility for discovering unaccounted modifications or possible pitfalls of the identification workflow is demonstrated.

FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics

AbstractRecent advances in mass spectrometry and separation technologies created the opportunities for deep proteome characterization using shotgun proteomics approaches. The “real world” sample complexity and high concentration range limit the sensitivity of this characterization. The common strategy for increasing the sensitivity is sample fractionation prior to analysis either at the protein or the peptide level. Typically, fractionation at the peptide level is performed using linear gradient high-performance liquid chromatography followed by uniform fraction collection. However, this way of peptide fractionation results in significantly suboptimal operation of the mass spectrometer due to the non-uniform distribution of peptides between the fractions. In this work, we propose an approach based on peptide retention time prediction allowing optimization of chromatographic conditions and fraction collection procedures. An open-source software implementing the approach called FractionOptimizer was developed and is available at http://hg.theorchromo.ru/FractionOptimizer. The performance of the developed tool was demonstrated for human embryonic kidney (HEK293) cell line lysate. In these experiments, we improved the uniformity of the peptides distribution between fractions. Moreover, in addition to 13,492 peptides, we found 6787 new peptides not identified in the experiments without fractionation and up to 800 new proteins (or 25%). Graphical abstractThe analysis workflow employing FractionOptimizer software.

Predictive Liquid Chromatography of Peptides Based on Hydrophilic Interactions for Mass Spectrometry-Based Proteomics

High-performance liquid chromatography (HPLC) is widely used for separation of complex peptide mixtures before mass spectrometry-based proteome analysis. In this analysis, reversed phase HPLC (RPHPLC) using non-polar stationary phases such as surface-modified silica containing alkyl groups (e.g., C18) is typically employed. Because of the high heterogeneity of proteomic samples, multidimensional separation approaches gained increasing attention recently to tackle this complexity and extremely high range of concentrations. In two-dimensional liquid chromatography, hydrophilic interaction chromatography (HILIC) is often a method of choice for combination with RP-HPLC because it uses reversed-phase type eluents and allows efficient separation of polar peptides. Due to the high degree of orthogonality in this two-dimensional separation space, it is tempting to develop approaches for predicting peptide retention times for HILIC-based separations similar to the ones for RP-HPLC. Recent successful efforts in this area were focused on developing retention coefficient (RC)-based approaches. Herein, we explored the feasibility of using a statistical thermodynamic model for prediction of peptide retention times in HILIC separations and determined the phenomenological parameters of the model for a bare silica column. The performance of the developed model was tested using HPLC-MS analysis of a set of synthetic peptides, as well as a tryptic peptide mixture.