title: A combinatorial scoring function for protein-RNA docking.
abstract: Protein-RNA docking is still an open question. One of the main challenges is to develop an effective scoring function that can discriminate near-native structures from the incorrect ones. To solve the problem, we have constructed a knowledge-based residue-nucleotide pairwise potential with secondary structure information considered for nonribosomal protein-RNA docking. Here we developed a weighted combined scoring function RpveScore that consists of the pairwise potential and six physics-based energy terms. The weights were optimized using the multiple linear regression method by fitting the scoring function to L_rmsd for the bound docking decoys from Benchmark II. The scoring functions were tested on 35 unbound docking cases. The results show that the scoring function RpveScore including all terms performs best. Also RpveScore was compared with the statistical mechanics-based method derived potential ITScore-PR, and the united atom-based statistical potentials QUASI-RNP and DARS-RNP. The success rate of RpveScore is 71.6% for the top 1000 structures and the number of cases where a near-native structure is ranked in top 30 is 25 out of 35 cases. For 32 systems (91.4%), RpveScore can find the binding mode in top 5 that has no lower than 50% native interface residues on protein and nucleotides on RNA. Additionally, it was found that the long-range electrostatic attractive energy plays an important role in distinguishing near-native structures from the incorrect ones. This work can be helpful for the development of protein-RNA docking methods and for the understanding of protein-RNA interactions. RpveScore program is available to the public at http://life.bjut.edu.cn/kxyj/kycg/2017116/14845362285362368_1.html Proteins 2017; 85:741-752. © 2016 Wiley Periodicals, Inc.
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
authors.forename: Zhao
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Lu
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
authors.forename: Chun
authors.identifier: 0000-0002-0895-3506
authors.initials: C
authors.lastname: Hua Li
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
authors.forename: Cun Xin
authors.initials: CX
authors.lastname: Wang
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
authors.forename: Xiao Yi
authors.initials: XY
authors.lastname: Zhang
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
authors.forename: Jian Jun
authors.initials: JJ
authors.lastname: Tan
mesh_terms: D000604:Amino Acyl-tRNA Synthetases / Q000737:chemistry* / Q000378:metabolism; D001665:Binding Sites; D006801:Humans; D062105:Molecular Docking Simulation; D016328:NF-kappa B / Q000737:chemistry* / Q000378:metabolism; D011485:Protein Binding; D011487:Protein Conformation; D012313:RNA / Q000737:chemistry* / Q000378:metabolism; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D012107:Research Design
chemical_list: D016328:NF-kappa B; D016601:RNA-Binding Proteins; D012313:RNA; D000604:Amino Acyl-tRNA Synthetases
keywords: combinatorial scoring function; electrostatic energy; protein-RNA interactions; statistical pairwise potential; van der Waals interactions