Generating Peptide Sequence Tags for Peptide Identification via Tandem Mass Spectrometry

Large-scale, rapid and accurate protein identification is the crucial basis for further protein analysis in computational proteomics. Searching protein database by use of the protein tandem mass spectra has been a standard solution for solving this problem. Though several algorithms have been proposed, more sensitive and accurate approaches are still needed. In this paper, an effective database search approach is proposed. Prior to searching sequence database, an approach based on a graph-theoretic model is proposed to infer the peptide sequence tag (PST) from the tandem mass spectra data which is the partial sequence of the peptide. Also, an index approach for the protein sequence database is proposed for speeding up the database search and filtering out the incorrect protein sequences. Then, a novel scoring method for evaluating the match between the peptide sequence tag and the protein sequence is proposed for improving the accuracy of the database search result. Finally, we develop an algorithm for solving the problem and implement it as a computer program PepCheck. All the results fore-Check are compared with those of the famous algorithms. Experimental results demonstrate that PepCheck is as accurate as or more accurate than them with the test datasets.