A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction

Chen, Peng; Hu, ShanShan; Zhang, Jun; Gao, Xin; Li, Jinyan; Xia, Junfeng; Wang, Bing

A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction

Files

07346422.pdf (4.53 MB)

Type

Article

Authors

Chen, Peng
Hu, ShanShan
Zhang, Jun
Gao, Xin

Li, Jinyan
Xia, Junfeng
Wang, Bing

KAUST Department

Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date

2015-12-03

Print Publication Date

2016-09-01

Date

2015-12-03

Abstract

Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures

Citation

A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction 2015:1 IEEE/ACM Transactions on Computational Biology and Bioinformatics