De novo peptide identification via mixed-integer linear optimization and tandem mass spectrometry

Abstract

A novel methodology for the de novo identification of peptides via mixedinteger linear optimization (MILP) and tandem mass spectrometry is presented. The overall mathematical model is presented and the key concepts of the proposed approach are described. A pre-processing algorithm is utilized to identify important m/z values in the tandem mass spectrum. Missing peaks, due to residue-dependent fragmentation characteristics, are dealt with using a twostage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to select the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum. The novel proposed de novo method, denoted as PILOT, is compared to existing popular methods such as Lutefisk, PEAKS, PepNovo, EigenMS and NovoHMM for a set of spectra resulting from QTOF instruments.