Zhu Jiang, Xie Li, Honig Barry
Howard Hughes Medical Institute, Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biophysics, Columbia University, 1130 St. Nicholas Avenue, Room 815, New York, New York 10032, USA.
Proteins. 2006 Nov 1;65(2):463-79. doi: 10.1002/prot.21085.
In this article, we present an iterative, modular optimization (IMO) protocol for the local structure refinement of protein segments containing secondary structure elements (SSEs). The protocol is based on three modules: a torsion-space local sampling algorithm, a knowledge-based potential, and a conformational clustering algorithm. Alternative methods are tested for each module in the protocol. For each segment, random initial conformations were constructed by perturbing the native dihedral angles of loops (and SSEs) of the segment to be refined while keeping the protein body fixed. Two refinement procedures based on molecular mechanics force fields - using either energy minimization or molecular dynamics - were also tested but were found to be less successful than the IMO protocol. We found that DFIRE is a particularly effective knowledge-based potential and that clustering algorithms that are biased by the DFIRE energies improve the overall results. Results were further improved by adding an energy minimization step to the conformations generated with the IMO procedure, suggesting that hybrid strategies that combine both knowledge-based and physical effective energy functions may prove to be particularly effective in future applications.
在本文中，我们提出了一种用于含有二级结构元件（SSEs）的蛋白质片段局部结构优化的迭代模块化优化（IMO）方案。该方案基于三个模块：扭转空间局部采样算法、基于知识的势函数和构象聚类算法。对方案中的每个模块都测试了替代方法。对于每个片段，通过扰动待优化片段中环（和SSEs）的天然二面角同时保持蛋白质主体固定来构建随机初始构象。还测试了基于分子力学力场的两种优化程序——使用能量最小化或分子动力学——但发现它们不如IMO方案成功。我们发现DFIRE是一种特别有效的基于知识的势函数，并且受DFIRE能量偏置的聚类算法改善了整体结果。通过在IMO程序生成的构象上添加能量最小化步骤，结果得到了进一步改善，这表明结合基于知识和物理有效能量函数的混合策略在未来应用中可能会被证明特别有效。
