Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations.

ABSTRACT: Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. MIPPI was specifically trained to determine the types of variant impact (increasing, decreasing, disrupting, and no effect) on protein-protein interactions. We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights, whic

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10494974 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature