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 modifi...

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Bibliographic Details
Published in:Proteomics (Weinheim) 2018-12, Vol.18 (23), p.e1800117-n/a
Main Authors: Bubis, Julia A., Levitsky, Lev I., Ivanov, Mark V., Gorshkov, Mikhail V.
Format: Article
Language:English
Subjects:
Acids
Algorithms
Amino acids
Amino Acids - analysis
bottom‐up proteomics
Data acquisition
Data processing
Feasibility studies
modifications
open search
Peptides - analysis
Proteins
Proteomics
Proteomics - methods
Search engines
search parameters
Workflow
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Summary: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. The algorithm is implemented in Python as an open‐source command‐line tool available at https://bitbucket.org/J_Bale/aa_stat/.
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.201800117