PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.

ABSTRACT: Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark dat

SUBMITTER: Sun T 

PROVIDER: S-EPMC8604987 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature