title: Machine learning based prediction for peptide drift times in ion mobility spectrometry.
abstract: BACKGROUND
  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 physicochemical properties of peptides to accurately predict a peptide's drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting.
  
  
  RESULTS
  When tested on an experimentally created high confidence database of 8675 peptide sequences with measured drift times, both techniques statistically significantly outperform the intrinsic size parameters-based calculations, the currently held practice in the field, on all charge states (+2, +3 and +4).
  
  
  BACKGROUND
  The software executable, imPredict, is available for download from http:/omics.pnl.gov/software/imPredict.php
  
  
  BACKGROUND
  rds@pnl.gov
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.
authors.forename: Anuj R
authors.initials: AR
authors.lastname: Shah
authors.forename: Khushbu
authors.initials: K
authors.lastname: Agarwal
authors.forename: Erin S
authors.initials: ES
authors.lastname: Baker
authors.forename: Mudita
authors.initials: M
authors.lastname: Singhal
authors.forename: Anoop M
authors.initials: AM
authors.lastname: Mayampurath
authors.forename: Yehia M
authors.initials: YM
authors.lastname: Ibrahim
authors.forename: Lars J
authors.initials: LJ
authors.lastname: Kangas
authors.forename: Matthew E
authors.initials: ME
authors.lastname: Monroe
authors.forename: Rui
authors.initials: R
authors.lastname: Zhao
authors.forename: Mikhail E
authors.initials: ME
authors.lastname: Belov
authors.forename: Gordon A
authors.initials: GA
authors.lastname: Anderson
authors.forename: Richard D
authors.initials: RD
authors.lastname: Smith
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D007477:Ions; D013058:Mass Spectrometry; D010455:Peptides / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012984:Software; D013057:Spectrum Analysis
chemical_list: D007477:Ions; D010455:Peptides