Artificial Intelligence Understands Peptide Observability and Assists With Absolute Protein Quantification.

ABSTRACT: Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays. The deep neural network is able to learn a regression model that relates the physicochemical properties of a peptide to its ion intensity detected by mass spectrometry. The a

SUBMITTER: Zimmer D 

PROVIDER: S-EPMC6242780 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature