Consensus of sample-balanced classifiers for identifying ligand-binding residue by co-evolutionary physicochemical characteristics of amino acids
Type
Conference PaperAuthors
Chen, PengDate
2013Permanent link to this record
http://hdl.handle.net/10754/564671
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Protein-ligand binding is an important mechanism for some proteins to perform their functions, and those binding sites are the residues of proteins that physically bind to ligands. So far, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available. In this paper, we propose a sequence-based approach to identify protein-ligand binding residues. Due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets, for each of which a random forest (RF)-based classifier was trained. The ensemble of these RF classifiers formed a sequence-based protein-ligand binding site predictor. Experimental results on CASP9 targets demonstrated that our method compared favorably with the state-of-the-art. © Springer-Verlag Berlin Heidelberg 2013.Citation
Chen, P. (2013). Consensus of Sample-Balanced Classifiers for Identifying Ligand-Binding Residue by Co-evolutionary Physicochemical Characteristics of Amino Acids. Emerging Intelligent Computing Technology and Applications, 206–212. doi:10.1007/978-3-642-39678-6_35Publisher
Springer NatureConference/Event name
9th International Conference on Intelligent Computing, ICIC 2013ISBN
9783642396779ae974a485f413a2113503eed53cd6c53
10.1007/978-3-642-39678-6_35