Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.

ABSTRACT: The spontaneous conversion of asparagine residues to aspartic acid or iso-aspartic acid, via deamidation, is a major pathway of protein degradation and is often seriously disruptive to biological systems. Deamidation has been shown to negatively affect both in vitro stability and in vivo biological function of diverse classes of proteins. During protein therapeutics development, deamidation liabilities that are overlooked necessitate expensive and time-consuming remediation strategies, sometimes leading to termination of the project. In this paper, we apply machine learning to a large (n = 776) liquid chromatography-tandem mass spectrometry (LC-MS/MS) dataset of monoclonal antibody peptides to create computational models for the post-translational modification asparagine deam

SUBMITTER: Delmar JA 

PROVIDER: S-EPMC6923510 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature