LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification.

ABSTRACT:

Background

Previous deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes.

Results

We proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pocket

SUBMITTER: Zhou R 

PROVIDER: S-EPMC11229186 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature