TPepPro: a deep learning model for predicting peptide-protein interactions.

ABSTRACT:

Motivation

Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-protein interactions (PepPIs) presents a very important approach but poses considerable technical challenges. In experimental aspects, the transient nature of PepPIs and the high flexibility of peptides contribute to elevated costs and inefficiency. Traditional docking and molecular dynamics simulation methods require substantial computational resources, and the predictive accuracy of their results remain unsatisfactory.

Results

To address this gap, we proposed TPepPro, a Transformer-based model for PepPI prediction. We trained TPep

SUBMITTER: Jin X 

PROVIDER: S-EPMC11681936 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature