PIPENN: protein interface prediction from sequence with an ensemble of neural nets.

ABSTRACT:

Motivation

The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly and challenging task, while protein sequence data are ubiquitous. Consequently, many computational and machine learning approaches have been developed over the years to predict such interface residues from sequence. However, the effectiveness of different Deep Learning (DL) architectures and learning strategies for protein-protein, protein-nucleotide and protein-small molecule interface prediction has not yet been investigated in great detail. Therefore, we here explore the prediction of protein interface residues using six DL architectures and various learning strategies with sequence

SUBMITTER: Stringer B 

PROVIDER: S-EPMC9004643 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature