Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein

被引:141
作者
Fu, Yi [1 ,2 ,3 ]
Zhao, Ji [1 ,2 ]
Chen, Zhiguo [3 ]
机构
[1] Wuxi Res Ctr Environm Sci & Engn, Wuxi 214153, Jiangsu, Peoples R China
[2] Wuxi City Coll Vocat Technol, Sch Internet Things Engn, Wuxi 214153, Jiangsu, Peoples R China
[3] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
SWARM OPTIMIZATION; GENETIC ALGORITHM; PARTICLE SWARM; CONVERGENCE; COMPLEX;
D O I
10.1155/2018/3502514
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Protein-ligand interactions are a necessary prerequisite for signal transduction, immunoreaction, and gene regulation. Protein-ligand interaction studies are important for understanding the mechanisms of biological regulation, and they provide a theoretical basis for the design and discovery of new drug targets. In this study, we analyzed the molecular interactions of protein-ligand which was docked by AutoDock 4.2 software. In AutoDock 4.2 software, we used a new search algorithm, hybrid algorithm of random drift particle swarm optimization and local search (LRDPSO), and the classical Lamarckian genetic algorithm (LGA) as energy optimization algorithms. The best conformations of each docking algorithm were subjected to molecular dynamic (MD) simulations to further analyze the molecular mechanisms of protein-ligand interactions. Here, we analyze the binding energy between protein receptors and ligands, the interactions of salt bridges and hydrogen bonds in the docking region, and the structural changes during complex unfolding. Our comparison of these complexes highlights differences in the protein-ligand interactions between the two docking methods. It also shows that salt bridge and hydrogen bond interactions play a crucial role in protein-ligand stability. The present work focuses on extracting the deterministic characteristics of docking interactions from their dynamic properties, which is important for understanding biological functions and determining which amino acid residues are crucial to docking interactions.
引用
收藏
页数:12
相关论文
共 35 条
[1]   Exponential Repulsion Improves Structural Predictability of Molecular Docking [J].
Bazgier, Vaclav ;
Berka, Karel ;
Otyepka, Michal ;
Banas, Pavel .
JOURNAL OF COMPUTATIONAL CHEMISTRY, 2016, 37 (28) :2485-2494
[2]  
Becker W, 2018, CHEMPHYSCHEM, V19, P895, DOI 10.1002/cphc.201701253
[3]   A mechanistic insight into protein-ligand interaction, folding, misfolding, aggregation and inhibition of protein aggregates: An overview [J].
Chandel, Tajalli Ilm ;
Zaman, Masihuz ;
Khan, Mohsin Vahid ;
Ali, Maroof ;
Rabbani, Gulam ;
Ishtikhar, Mohd ;
Khan, Rizwan Hasan .
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES, 2018, 106 :1115-1129
[4]   SODOCK: Swarm optimization for highly flexible protein-ligand docking [J].
Chen, Hung-Ming ;
Liu, Bo-Fu ;
Huang, Hui-Ling ;
Hwang, Shiow-Fen ;
Ho, Shinn-Ying .
JOURNAL OF COMPUTATIONAL CHEMISTRY, 2007, 28 (02) :612-623
[5]   The particle swarm - Explosion, stability, and convergence in a multidimensional complex space [J].
Clerc, M ;
Kennedy, J .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (01) :58-73
[6]   Statistical and machine learning approaches to predicting protein-ligand interactions [J].
Colwell, Lucy J. .
CURRENT OPINION IN STRUCTURAL BIOLOGY, 2018, 49 :123-128
[7]   PARTICLE MESH EWALD - AN N.LOG(N) METHOD FOR EWALD SUMS IN LARGE SYSTEMS [J].
DARDEN, T ;
YORK, D ;
PEDERSEN, L .
JOURNAL OF CHEMICAL PHYSICS, 1993, 98 (12) :10089-10092
[8]   DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina [J].
Di Muzio, Elena ;
Toti, Daniele ;
Polticelli, Fabio .
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2017, 31 (02) :213-218
[9]   Random drift particle swarm optimisation algorithm for highly flexible protein-ligand docking [J].
Fu, Yi ;
Chen, Zhiguo ;
Sun, Jun .
JOURNAL OF THEORETICAL BIOLOGY, 2018, 457 :180-189
[10]   Statistical Estimation of the Protein-Ligand Binding Free Energy Based On Direct Protein-Ligand Interaction Obtained by Molecular Dynamics Simulation [J].
Fukunishi, Yoshifumi ;
Nakamura, Haruki .
PHARMACEUTICALS, 2012, 5 (10) :1064-1079