Multi-task learning to leverage partially annotated data for PPI interface prediction.

ABSTRACT: Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability. Here we use a multi-task learning strategy that can handle missing data. We start with the multi-task model architecture, and adapted it to carefully handle missing data in the cost function. As related learning tasks we include prediction of secondary structure, solvent accessibility, and buried residu

SUBMITTER: Capel H 

PROVIDER: S-EPMC9213449 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature