Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction.

ABSTRACT: Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address th

SUBMITTER: Yin S 

PROVIDER: S-EPMC10635286 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature