Mark V. Ivanov

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

Exome-driven characterization of the cancer cell lines at the proteome level: the NCI-60 case study.

Cancer genome deviates significantly from the reference human genome, and thus a search against standard genome databases in cancer cell proteomics fails to identify cancer-specific protein variants. The goal of this Article is to combine high-throughput exome data [Abaan et al. Cancer Res. 2013] and shotgun proteomics analysis [Modhaddas Gholami et al. Cell Rep. 2013] for cancer cell lines from NCI-60 panel to demonstrate further that the cell lines can be effectively recognized using identified variant peptides. To achieve this goal, we generated a database containing mutant protein sequences of NCI-60 panel of cell lines. The proteome data were searched using Mascot and X!Tandem search engines against databases of both reference and mutant protein sequences. The identification quality was further controlled by calculating a fraction of variant peptides encoded by the own exome sequence for each cell line. We found that up to 92.2% peptides identified by both search engines are encoded by the own exome. Further, we used the identified variant peptides for cell line recognition. The results of the study demonstrate that proteome data supported by exome sequence information can be effectively used for distinguishing between different types of cancer cell lines.

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.

Exome-based proteogenomics of HEK-293 human cell line: Coding genomic variants identified at the level of shotgun proteome.

Genomic and proteomic data were integrated into the proteogenomic workflow to identify coding genomic variants of Human Embryonic Kidney 293 (HEK‐293) cell line at the proteome level. Shotgun proteome data published by Geiger et al. (2012), Chick et al. (2015), and obtained in this work for HEK‐293 were searched against the customized genomic database generated using exome data published by Lin et al. (2014). Overall, 112 unique variants were identified at the proteome level out of ∼1200 coding variants annotated in the exome. Seven identified variants were shared between all the three considered proteomic datasets, and 27 variants were found in any two datasets. Some of the found variants belonged to widely known genomic polymorphisms originated from the germline, while the others were more likely resulting from somatic mutations. At least, eight of the proteins bearing amino acid variants were annotated as cancer‐related ones, including p53 tumor suppressor. In all the considered shotgun datasets, the variant peptides were at the ratio of 1:2.5 less likely being identified than the wild‐type ones compared with the corresponding theoretical peptides. This can be explained by the presence of the so‐called “passenger” mutations in the genes, which were never expressed in HEK‐293 cells. All MS data have been deposited in the ProteomeXchange with the dataset identifier PXD002613 (http://proteomecentral.proteomexchange.org/dataset/PXD002613).

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

Depletion of human serum albumin in embryo culture media for in vitro fertilization using monolithic columns with immobilized antibodies.

Affinity depletion of abundant proteins such as HSA is an important stage in routine sample preparation prior to MS/MS analysis of biological samples with high range of concentrations. Due to the charge competition effects in electrospray ion source that results in discrimination of the low‐abundance species, as well as limited dynamic range of MS/MS, restricted typically by three orders of magnitude, the identification of low‐abundance proteins becomes a challenge unless the sample is depleted from high‐concentration compounds. This dictates a need for developing efficient separation technologies allowing fast and automated protein depletion. In this study, we performed evaluation of a novel immunoaffinity‐based Convective Interaction Media analytical columns (CIMac) depletion column with specificity to HSA (CIMac‐αHSA). Because of the convective flow‐through channels, the polymethacrylate CIMac monoliths afford flow rate independent binding capacity and resolution that results in relatively short analysis time compared with traditional chromatographic supports. Seppro IgY14 depletion kit was used as a benchmark to control the results of depletion. Bottom‐up proteomic approach followed by label‐free quantitation using normalized spectral indexes were employed for protein quantification in G1/G2 and cleavage/blastocyst in vitro fertilization culture media widely utilized in clinics for embryo growth in vitro. The results revealed approximately equal HSA level of 100 ± 25% in albumin‐enriched fractions relative to the nondepleted samples for both CIMac‐αHSA column and Seppro kit. In the albumin‐free fractions concentrated 5.5‐fold by volume, serum albumin was identified at the levels of 5–30% and 20–30% for the CIMac‐αHSA and Seppro IgY14 spin columns, respectively.

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.

Comparative proteomics as a tool for identifying specific alterations within interferon response pathways in human glioblastoma multiforme cells

An acquisition of increased sensitivity of cancer cells to viruses is a common outcome of malignant progression that justifies the development of oncolytic viruses as anticancer therapeutics. Studying molecular changes that underlie the sensitivity to viruses would help to identify cases where oncolytic virus therapy would be most effective. We quantified changes in protein abundances in two glioblastoma multiforme (GBM) cell lines that differ in the ability to induce resistance to vesicular stomatitis virus (VSV) infection in response to type I interferon (IFN) treatment. In IFN-treated samples we observed an up-regulation of protein products of some IFN-regulated genes (IRGs). In total, the proteome analysis revealed up to 20% more proteins encoded by IRGs in the glioblastoma cell line, which develops resistance to VSV infection after pre-treatment with IFN. In both cell lines protein-protein interaction and signaling pathway analyses have revealed a significant stimulation of processes related to type I IFN signaling and defense responses to viruses. However, we observed a deficiency in STAT2 protein in the VSV-sensitive cell line that suggests a de-regulation of the JAK/STAT/IRF9 signaling. The study has shown that the up-regulation of IRG proteins induced by the IFNα treatment of GBM cells can be detected at the proteome level. Similar analyses could be applied for revealing functional alterations within the antiviral mechanisms in glioblastoma samples, accompanying by acquisition of sensitivity to oncolytic viruses. The approach can be useful for discovering the biomarkers that predict a potential sensitivity of individual glioblastoma tumors to oncolytic virus therapy.

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.