PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model.

ABSTRACT: The protein-peptide interaction plays a pivotal role in fields such as drug development, yet remains underexplored experimentally and challenging to model computationally. Herein, we introduce PepCA, a sequence-based approach for predicting peptide-binding sites on proteins. A primary obstacle in predicting peptide-protein interactions is the difficulty in acquiring precise protein structures, coupled with the uncertainty of polypeptide configurations. To address this, we first encode protein sequences using the Evolutionary Scale Modeling 2 (ESM-2) pre-trained model to extract latent structural information. Additionally, we have developed a multi-input coattention mechanism to concurrently update the encoding of both peptide and protein residues. PepCA integrates this module within an enc

SUBMITTER: Huang J 

PROVIDER: S-EPMC11465048 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature