title: Improved Accuracy in RNA-Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function.
abstract: RNA-protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA-protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA-protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA-protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein-protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA-protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA-protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA-protein complexes.
authors: Graduate School of Frontier Sciences, The University of Tokyo , 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan.
authors: Junichi
authors: J
authors: Iwakiri
authors: Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University , 55N-06-10, 3-4-1, Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST) , 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.
authors: Michiaki
authors: M
authors: Hamada
authors: Graduate School of Frontier Sciences, The University of Tokyo , 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST) , 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.
authors: Kiyoshi
authors: K
authors: Asai
authors: Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST) , 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.
authors: Tomoshi
authors: T
authors: Kameda
mesh_terms: D001665:Binding Sites; D062105:Molecular Docking Simulation; D009690:Nucleic Acid Conformation; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012313:RNA / Q000737:chemistry / Q000378:metabolism*; D012984:Software; D055672:Static Electricity
chemical_list: D011506:Proteins; D012313:RNA