PNNL

Abstract

Motivation: Ion mobility spectrometry (IMS) has gained significant traction over the past few years as a proven technique for rapid, high-resolution separations of analytes based upon gas-phase ion structure with significant impact in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques such as 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 calls for accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby, enabling downstream data analysis. Here we introduce a Support Vector Regression-based model that accurately predicts a peptide’s drift time directly from its amino acid sequence. Results: When tested on an experimentally created high confidence database of 8676 peptide sequences with measured drift times, our prediction method statistically significantly outperforms the intrinsic size parameters-based calculations on all charge states (+2,+3 and +4). Availability: The software executable, imPredict, is available for download from http://omics.pnl.gov/software/imPredict.php.