Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, Osaka 565-0871, Japan.
J Comput Chem. 2011 Jun;32(8):1680-6. doi: 10.1002/jcc.21747. Epub 2011 Mar 4.
We describe the development of new force fields for protein side chain modeling called optimized side chain atomic energy (OSCAR). The distance-dependent energy functions (OSCAR-d) and side-chain dihedral angle potential energy functions were represented as power and Fourier series, respectively. The resulting 802 adjustable parameters were optimized by discriminating the native side chain conformations from non-native conformations, using a training set of 12,000 side chains for each residue type. In the course of optimization, for every residue, its side chain was replaced by varying rotamers, whereas conformations for all other residues were kept as they appeared in the crystal structure. Then, the OSCAR-d were multiplied by an orientation-dependent function to yield OSCAR-o. A total of 1087 parameters of the orientation-dependent energy functions (OSCAR-o) were optimized by maximizing the energy gap between the native conformation and subrotamers calculated as low energy by OSCAR-d. When OSCAR-o with optimized parameters were used to model side chain conformations simultaneously for 218 recently released protein structures, the prediction accuracies were 88.8% for χ(1) , 79.7% for χ(1 + 2) , 1.24 Å overall root mean square deviation (RMSD), and 0.62 Å RMSD for core residues, respectively, compared with the next-best performing side-chain modeling program which achieved 86.6% for χ(1) , 75.7% for χ(1 + 2) , 1.40 Å overall RMSD, and 0.86 Å RMSD for core residues, respectively. The continuous energy functions obtained in this study are suitable for gradient-based optimization techniques for protein structure refinement. A program with built-in OSCAR for protein side chain prediction is available for download at http://sysimm.ifrec.osaka-u.ac.jp/OSCAR/.
我们描述了一种用于蛋白质侧链建模的新力场的开发，称为优化侧链原子能（OSCAR）。距离依赖能量函数（OSCAR-d）和侧链二面角势能函数分别表示为幂级数和傅里叶级数。通过从非天然构象中区分出天然侧链构象，使用每种残基类型的12000个侧链训练集，对得到的802个可调参数进行了优化。在优化过程中，对于每个残基，其侧链被不同的旋转异构体取代，而所有其他残基的构象保持其在晶体结构中的样子。然后，将OSCAR-d乘以一个取向依赖函数以得到OSCAR-o。通过最大化天然构象与由OSCAR-d计算为低能量的亚旋转异构体之间的能量差，对总共1087个取向依赖能量函数（OSCAR-o）的参数进行了优化。当使用具有优化参数的OSCAR-o对218个最近发布的蛋白质结构同时进行侧链构象建模时，与次优性能的侧链建模程序相比，χ(1)的预测准确率分别为88.8%，χ(1 + 2)的预测准确率为79.7%，整体均方根偏差（RMSD）为1.24 Å，核心残基的RMSD为0.62 Å，而次优程序的χ(1)预测准确率为86.6%，χ(1 + 2)预测准确率为75.7%，整体RMSD为1.40 Å，核心残基的RMSD为0.86 Å。本研究中获得的连续能量函数适用于基于梯度的蛋白质结构优化技术。一个内置OSCAR用于蛋白质侧链预测的程序可在http://sysimm.ifrec.osaka-u.ac.jp/OSCAR/下载。
