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. The algorithm is implemented in Python as an open-source command-line tool available at https://bitbucket.org/J_Bale/aa_stat/.