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.
Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China.
Bioinformatics. 2020 Jan 15;36(2):437-448. doi: 10.1093/bioinformatics/btz544.
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.
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.
The AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/.
Supplementary data are available at Bioinformatics online.
蛋白质结构精修是蛋白质结构预测的重要步骤。现有的方法通常使用单个打分函数结合蒙特卡罗方法或分子动力学算法。单一能量函数的一维优化可能会在没有约束的情况下使结构偏离太远。我们研究的基本动机是，通过最小化单一能量函数来减少由于不同蛋白质结构的多样性而导致的偏差问题。
我们报告了一种新的基于人工智能的蛋白质结构精修方法，称为 AIR。其基本思想是使用多个能量函数作为多目标，以努力纠正单一函数可能存在的潜在不准确问题。设计了一种基于多目标粒子群优化算法的结构精修方法，其中每个结构都被视为协议中的一个粒子。随着精修迭代的进行，粒子会四处移动。每个迭代中的粒子质量由三个能量函数进行评估，非支配粒子被放入一个称为 Pareto 集的集合中。经过足够的迭代次数后，从 Pareto 集中筛选出粒子，并输出部分最优解作为最终的精修结构。在 AIR 协议中设计的多目标能量函数优化策略为结构提供了不同的约束视角，通过将一维优化扩展到由多目标粒子群优化引擎驱动的新三维空间优化。在 CASP11、CASP12 精修靶标和 CASP13 中的盲测实验中的实验结果表明该方法很有前景。
AIR 可在以下网址在线使用：www.csbio.sjtu.edu.cn/bioinf/AIR/。
补充数据可在 Bioinformatics 在线获取。
