Journal
JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
Volume 32, Issue 5, Pages 1258-1262Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jasms.1c00061
Keywords
mass spectrometry; proteomics; protein inference; homology
Funding
- Russian Science Foundation [19-74-00123]
- Russian Science Foundation [19-74-00123] Funding Source: Russian Science Foundation
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Protein inference is a crucial step in proteome characterization using a bottom-up approach, and this study proposes a method using peptide feature information from precursor mass spectra to assist in distinguishing homologous proteins more accurately and efficiently.
Protein inference is one of the crucial steps in proteome characterization using a bottom-up approach. Multiple algorithms to solve the problem are focused on extensive analysis of shared peptides identified from fragmentation mass spectra (MS/MS). However, many protein homologues with a similar amino acid sequence typically have identical lists of identified peptides due to the problem of proteome undersampling in a bottom-up approach and, thus, cannot be distinguished by existing protein inference methods. Here, we propose the use of peptide feature information extracted from precursor mass spectra to assist in identification of proteins otherwise indistinguishable from MS/ MS. The proposed method was integrated with a protein inference algorithm based on the parsimony principle and built-in in the postsearch utility Scavager. The results demonstrate increasing accuracy and efficiency of homologous protein identifications for the well characterized data sets including the one with known protein sequences from iPRG-2016 study.
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