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.
Int J Mol Sci. 2021 Apr 23;22(9):4408. doi: 10.3390/ijms22094408.
Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0.
蛋白质结构精修是更准确地预测蛋白质结构的关键步骤。大多数现有方法将其视为能量最小化问题，通过搜索具有更低能量的结构来直观地提高初始模型的质量。考虑到单个能量函数无法反映所有蛋白质的准确能量景观，我们之前的 AIR 1.0 管道使用多个能量函数来实现基于多目标粒子群优化的模型精修。它有望提供一种通用的平衡构象搜索协议，从不同的能量评估中进行指导。然而，AIR 1.0 作为一个整体解决多目标优化问题，这可能导致某些目标的解决方案多样性和收敛性不佳。在这项研究中，我们报告了一种基于分解的方法 AIR 2.0，这是 AIR 的更新版本，用于蛋白质结构精修。AIR 2.0 将多目标优化问题分解为多个子问题，并使用粒子群优化算法同时对它们进行优化。与之前的版本相比，AIR 2.0 生成的解决方案具有更好的收敛性和多样性，这增加了挖掘更好结构构象的可能性。在 CASP13 精修基准目标和 CASP 14 中的盲测实验中的实验结果证明了 AIR 2.0 的有效性。
