title: Sequence-based predictor of ATP-binding residues using random forest and mRMR-IFS feature selection.
abstract: We develop a computational and statistical approach (ATPBR) for predicting ATP-binding residues in proteins from amino acid sequences by using random forests with a novel hybrid feature. The hybrid feature incorporates a new feature called PSSMPP, the predicted secondary structure and orthogonal binary vectors. The mRMR-IFS feature selection method is utilized to construct the best prediction model. At last, ATPBR achieves significantly improved performance over existing methods, with 87.53% accuracy and a Matthew׳s correlation coefficient of 0.554. In addition, our further analysis demonstrates that PSSMPP distinguishes more effectively between ATP-binding and non-binding residues. Besides, the optimal features selected by the mRMR-IFS method improve the prediction performance and may provide useful insights for revealing the mechanisms of ATP and proteins interactions.
authors.affiliation: Golden Audit College, Nanjing Audit University, Nanjing 210029, China. Electronic address: maxin@nau.edu.cn.
authors.forename: Xin
authors.initials: X
authors.lastname: Ma
authors.affiliation: State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China. Electronic address: xsun@seu.edu.cn.
authors.forename: Xiao
authors.initials: X
authors.lastname: Sun
mesh_terms: D000255:Adenosine Triphosphate / Q000378:metabolism*; D000465:Algorithms; D008954:Models, Biological; D011485:Protein Binding / Q000235:genetics*; D017433:Protein Structure, Secondary / Q000235:genetics*; D011506:Proteins / Q000235:genetics / Q000378:metabolism*
chemical_list: D011506:Proteins; D000255:Adenosine Triphosphate
keywords: Binding propensity; Physicochemical property; Position-specific scoring matrix