TY - JOUR
T1 - A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction
AU - Chen, Peng
AU - Hu, ShanShan
AU - Zhang, Jun
AU - Gao, Xin
AU - Li, Jinyan
AU - Xia, Junfeng
AU - Wang, Bing
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2015/12/3
Y1 - 2015/12/3
N2 - 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
AB - 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
UR - http://hdl.handle.net/10754/584251
UR - http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7346422
UR - http://www.scopus.com/inward/record.url?scp=84990937919&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2015.2505286
DO - 10.1109/TCBB.2015.2505286
M3 - Article
SN - 1545-5963
VL - 13
SP - 901
EP - 912
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 5
ER -