Mutation effect estimation on protein-protein interactions using deep contextualized representation learning.

ABSTRACT: The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein-protein Interaction PRediction Using Contextualized Representations), to estimate the ef

SUBMITTER: Zhou G 

PROVIDER: S-EPMC7059401 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature