A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction
Type
ArticleKAUST Department
Computational Bioscience Research Center (CBRC)Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2015-12-03Online Publication Date
2015-12-03Print Publication Date
2016-09-01Permanent link to this record
http://hdl.handle.net/10754/584251
Metadata
Show full item recordAbstract
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 structuresCitation
A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction 2015:1 IEEE/ACM Transactions on Computational Biology and Bioinformaticsae974a485f413a2113503eed53cd6c53
10.1109/TCBB.2015.2505286