Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide–spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies

Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide–spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies

MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides by solving a set covering problem

A C# program implemented for the MinProtMaxVP. Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotation into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. Both approaches may neglect the possibility of variant combinations, e.g., haplotypes, existing in bio-samples for sequence generation, thereby rendering low variant identification. Therefore, we propose a novel approach, called MinProtMaxVP, to minimize the SAV-harboring protein sequences generated by considering all combinations of SAVs, but accommodating all variant peptides for identification.   Choong et al., “MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides by solving a set covering problem". (Manuscript submitted), 2019.     About us: http://ms.iis.sinica.edu.tw/COmics/software.html    </p

MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides by solving a set covering problem

A C# program implemented for the MinProtMaxVP. Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotation into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. Both approaches may neglect the possibility of variant combinations, e.g., haplotypes, existing in bio-samples for sequence generation, thereby rendering low variant identification. Therefore, we propose a novel approach, called MinProtMaxVP, to minimize the SAV-harboring protein sequences generated by considering all combinations of SAVs, but accommodating all variant peptides for identification.   Choong et al., “MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides by solving a set covering problem". (Manuscript submitted), 2019.     About us: http://ms.iis.sinica.edu.tw/COmics/software.html    </p

<i>i</i>HPDM: In Silico Human Proteome Digestion Map with Proteolytic Peptide Analysis and Graphical Visualizations

When conducting proteomics experiments to detect missing proteins and protein isoforms in the human proteome, it is desirable to use a protease that can yield more unique peptides with properties amenable for mass spectrometry analysis. Though trypsin is currently the most widely used protease, some proteins can yield only a limited number of unique peptides by trypsin digestion. Other proteases and multiple proteases have been applied in reported studies to increase the number of identified proteins and protein sequence coverage. To facilitate the selection of proteases, we developed a web-based resource, called in silico Human Proteome Digestion Map (iHPDM), which contains a comprehensive proteolytic peptide database constructed from human proteins, including isoforms, in neXtProt digested by 15 protease combinations of one or two proteases. iHPDM provides convenient functions and graphical visualizations for users to examine and compare the digestion results of different proteases. Notably, it also supports users to input filtering criteria on digested peptides, e.g., peptide length and uniqueness, to select suitable proteases. iHPDM can facilitate protease selection for shotgun proteomics experiments to identify missing proteins, protein isoforms, and single amino acid variant peptides

Evaluating the Possibility of Detecting Variants in Shotgun Proteomics via LeTE-Fusion Analysis Pipeline

In proteogenomic studies, many genome-annotated events, for example, single amino acid variation (SAAV) and short INDEL, are often unobserved in shotgun proteomics. Therefore, we propose an analysis pipeline called LeTE-fusion (Le, peptide length; T, theoretical values; E, experimental data) to first investigate whether peptides with certain lengths are observed more often in mass spectrometry (MS)-based proteomics, which may hinder peptide identification causing difficulty in detecting genome-annotated events. By applying LeTE-fusion on different MS-based proteome data sets, we found peptides within 7–20 amino acids are more frequently identified, possibly attributed to MS-related factors instead of proteases. We then further extended the usage of LeTE-fusion on four variant-containing-sequence data sets (SAAV-only) with various sample complexity up to the whole human proteome scale, which yields theoretically ∼70% variants observable in an ideal shotgun proteomics. However, only ∼40% of variants might be detectable in real shotgun proteomic experiments when LeTE-fusion utilizes the experimentally observed variant-site-containing wild-type peptides in PeptideAtlas to estimate the expected observable coverage of variants. Finally, we conducted a case study on HEK293 cell line with variants reported at genomic level that were also identified in shotgun proteomics to demonstrate the efficacy of LeTE-fusion on estimating expected observable coverage of variants. To the best of our knowledge, this is the first study to systematically investigate the detection limits of genome-annotated events via shotgun proteomics using such analysis pipeline

Decoding the Effect of Isobaric Substitutions on Identifying Missing Proteins and Variant Peptides in Human Proteome

To confirm the existence of missing proteins, we need to identify at least two unique peptides with length of 9–40 amino acids of a missing protein in bottom-up mass-spectrometry-based proteomic experiments. However, an identified unique peptide of the missing protein, even identified with high level of confidence, could possibly coincide with a peptide of a commonly observed protein due to isobaric substitutions, mass modifications, alternative splice isoforms, or single amino acid variants (SAAVs). Besides unique peptides of missing proteins, identified variant peptides (SAAV-containing peptides) could also alternatively map to peptides of other proteins due to the aforementioned issues. Therefore, we conducted a thorough comparative analysis on data sets in PeptideAtlas Tiered Human Integrated Search Proteome (THISP, 2017-03 release), including neXtProt (2017-01 release), to systematically investigate the possibility of unique peptides in missing proteins (PE2–4), unique peptides in dubious proteins, and variant peptides affected by isobaric substitutions, causing doubtful identification results. In this study, we considered 11 isobaric substitutions. From our analysis, we found <5% of the unique peptides of missing proteins and >6% of variant peptides became shared with peptides of PE1 proteins after isobaric substitutions

Accuracies (%) of various predictors on classification of proteins with and without signal peptides.

<p>Accuracies (%) of various predictors on classification of proteins with and without signal peptides.</p

Specificities (%) of various predictors on the non-signal peptide protein benchmark datasets.

<p>Specificities (%) of various predictors on the non-signal peptide protein benchmark datasets.</p