Fast and accurate identification of semi-tryptic peptides in shotgun proteomics

Motivation: One of the major problems in shotgun proteomics is the low peptide coverage when analyzing complex protein samples. Identifying more peptides, e.g. non-tryptic peptides, may increase the peptide coverage and improve protein identification and/or quantification that are based on the peptide identification results. Searching for all potential non-tryptic peptides is, however, time consuming for shotgun proteomics data from complex samples, and poses a challenge for a routine data analysis. Results: We hypothesize that non-tryptic peptides are mainly created from the truncation of regular tryptic peptides before separation. We introduce the notion of truncatability of a tryptic peptide, i.e. the probability of the peptide to be identified in its truncated form, and build a predictor to estimate a peptides truncatability from its sequence. We show that our predictions achieve useful accuracy, with the area under the ROC curve from 76 to 87, and can be used to filter the sequence database for identifying truncated peptides. After filtering, only a limited number of tryptic peptides with the highest truncatability are retained for non-tryptic peptide searching. By applying this method to identification of semi-tryptic peptides, we show that a significant number of such peptides can be identified within a searching time comparable to that of tryptic peptide identification.