Machine learning based prediction for peptide drift times in ion mobility spectrometry.

ABSTRACT:

Motivation

Ion mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochem

SUBMITTER: Shah AR 

PROVIDER: S-EPMC2913656 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature