title: Protein ligand-specific binding residue predictions by an ensemble classifier.
abstract: BACKGROUND
  Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands.
  
  
  RESULTS
  In this study, we present ligand-specific methods to predict the binding sites of protein-ligand interactions. First, a sequence-based method is proposed that only extracts features from protein sequence information, including evolutionary conservation scores and predicted structure properties. An improved AdaBoost algorithm is applied to address the serious imbalance problem between the binding and non-binding residues. Then, a combined method is proposed that combines the current template-free method and four other well-established template-based methods. The above two methods predict the ligand binding sites along the sequences using a ligand-specific strategy that contains metal ions, acid radical ions, nucleotides and ferroheme. Testing on a well-established dataset showed that the proposed sequence-based method outperformed the profile-based method by 4-19% in terms of the Matthews correlation coefficient on different ligands. The combined method outperformed each of the individual methods, with an improvement in the average Matthews correlation coefficients of 5.55% over all ligands. The results also show that the ligand-specific methods significantly outperform the general-purpose methods, which confirms the necessity of developing elaborate ligand-specific methods for ligand binding site prediction.
  
  
  CONCLUSIONS
  Two efficient ligand-specific binding site predictors are presented. The standalone package is freely available for academic usage at http://dase.ecnu.edu.cn/qwdong/TargetCom/TargetCom_standalone.tar.gz  or request upon the corresponding author.
authors.affiliation: College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China.
authors.forename: Xiuzhen
authors.initials: X
authors.lastname: Hu
authors.affiliation: College of Animal Science and Technology, Jilin Agricultural University, Changchun, 130118, People's Republic of China.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.affiliation: Institute for Data Science and Engineering, East China Normal University, Shanghai, 200062, People's Republic of China. qwdong@sei.ecnu.edu.cn.|Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, People's Republic of China. qwdong@sei.ecnu.edu.cn.|Present Address: School of Computer Science and Software Engineering, East China Normal University, #3663, North Zhongshan RD, Shanghai, 200062, China. qwdong@sei.ecnu.edu.cn.
authors.forename: Qiwen
authors.initials: Q
authors.lastname: Dong
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D030562:Databases, Protein; D008024:Ligands; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D008024:Ligands; D011506:Proteins
keywords: Binding residue prediction; Ensemble classifier; Protein function