Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments.

ABSTRACT: Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion in

SUBMITTER: Searle BC 

PROVIDER: S-EPMC4563719 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature