PUResNet: prediction of protein-ligand binding sites using deep residual neural network.

ABSTRACT:

Background

Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required.

Results

In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for

SUBMITTER: Kandel J 

PROVIDER: S-EPMC8424938 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature