Validation of Peptide Identification Results in Proteomics Using Amino Acid Counting

Abstract

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/.

AA_stat: intelligent profiling ofin vivoandin vitromodifications from open search results

“…Data processing in AA_stat involves Gaussian fit to group potentially modified peptides, group-specific FDR filtering, amino acid counting, and frequency normalization for the particular mass shifts. 22 In the first version of AA_stat, 22 the zero mass shift (containing unmodified peptides and/or peptides with fixed modifications) was always used for normalization (reference mass shift). The latter restricted applicability of the tool to label-free and TMT-based At the next step, the isoforms for each peptide are generated according to the amino acid candidate list.…”

“…Recently, we introduced AA_stat, an open-source software for analysis and interpretation of the results of ultra-tolerant searches using amino acid frequency counts, which can be easily integrated into the open-search-based workflow. 22 Similar to the other algorithms, 18,19,21 AA_stat selects well-resolved mass shifts, followed by determination of accurate mass shift values using Gaussian fit and group-specific false discovery rate (FDR) estimation with target-decoy approach (TDA), as well as the Unimod-based interpretation.…”

“…Recently, we introduced AA_stat, an open-source software for analysis of ultra-tolerant search results based on amino acid frequency calculation. [24] In this work, the AA_stat algorithm was significantly upgraded by adding MS/MS-based localization and implementing a number of features aimed at improving the method sensitivity and interpretation of the detected mass shifts.…”

“…There are three sources of amino acid candidates (eligible residues) for a specific mass shift: (1) all amino acids (including N-and C-terminus) from Unimod database that correspond to the mass shift with a given mass tolerance; (2) amino acids whose normalized frequency (eq. 2 in our previous study22 ) exceeds a given threshold; (3) if mass shift can be potentially a 13 C isotope within a given mass tolerance, all candidates of monoisotopic mass shift are considered.…”

Rpn4 and proteasome-mediated yeast resistance to ethanol includes regulation of autophagy

“…X!Tandem (Craig and Beavis 2004) (version Cyclone 2012.10.01.1) searches were run against the above-described customized proteomic database using the following parameters: precursor mass tolerance of ±10 ppm, fragment mass tolerance of ±0.01 Da, one Open search profiling was used to optimize a set of potential modifications for the subsequent database close search (SI, Fig. S1) (Chick et al 2015;Bubis et al 2018). Because the open search profiles do not return the abundant mass shifts, variable modifications were switched off.…”