Di Wang1, Ling Geng1, Yu-Jun Zhao1, Yang Yang2, Yan Huang3, Yang Zhang4, Hong-Bin Shen1,2. 1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China. 2. Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China. 3. State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China. 4. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Abstract
MOTIVATION: Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures. RESULTS: We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising. AVAILABILITY AND IMPLEMENTATION: The AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures. RESULTS: We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising. AVAILABILITY AND IMPLEMENTATION: The AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: David A Case; Thomas E Cheatham; Tom Darden; Holger Gohlke; Ray Luo; Kenneth M Merz; Alexey Onufriev; Carlos Simmerling; Bing Wang; Robert J Woods Journal: J Comput Chem Date: 2005-12 Impact factor: 3.376
Authors: Jalil Villalobos-Alva; Luis Ochoa-Toledo; Mario Javier Villalobos-Alva; Atocha Aliseda; Fernando Pérez-Escamirosa; Nelly F Altamirano-Bustamante; Francine Ochoa-Fernández; Ricardo Zamora-Solís; Sebastián Villalobos-Alva; Cristina Revilla-Monsalve; Nicolás Kemper-Valverde; Myriam M Altamirano-Bustamante Journal: Front Bioeng Biotechnol Date: 2022-07-07