Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning.

ABSTRACT: Deep learning models for prediction of three key LC-MS/MS properties from peptide sequences were developed. The LC-MS/MS properties or behaviors are indexed retention times (iRT), MS1 or survey scan charge state distributions, and sequence ion intensities of HCD spectra. A common core deep supervised learning architecture, bidirectional long-short term memory (LSTM) recurrent neural networks was used to construct the three prediction models. Two featurization schemes were proposed and demonstrated to allow for efficient encoding of modifications. The iRT and charge state distribution models were trained with on order of 10⁵ data points each. An HCD sequence ion prediction model was trained with 2 × 10⁶ experimental spectra. The iRT prediction model and HCD sequence io

SUBMITTER: Guan S 

PROVIDER: S-EPMC6773555 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature