Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Biophys J. 2011 Nov 16;101(10):2525-34. doi: 10.1016/j.bpj.2011.10.024. Epub 2011 Nov 15.
Most protein structural prediction algorithms assemble structures as reduced models that represent amino acids by a reduced number of atoms to speed up the conformational search. Building accurate full-atom models from these reduced models is a necessary step toward a detailed function analysis. However, it is difficult to ensure that the atomic models retain the desired global topology while maintaining a sound local atomic geometry because the reduced models often have unphysical local distortions. To address this issue, we developed a new program, called ModRefiner, to construct and refine protein structures from Cα traces based on a two-step, atomic-level energy minimization. The main-chain structures are first constructed from initial Cα traces and the side-chain rotamers are then refined together with the backbone atoms with the use of a composite physics- and knowledge-based force field. We tested the method by performing an atomic structure refinement of 261 proteins with the initial models constructed from both ab initio and template-based structure assemblies. Compared with other state-of-art programs, ModRefiner shows improvements in both global and local structures, which have more accurate side-chain positions, better hydrogen-bonding networks, and fewer atomic overlaps. ModRefiner is freely available at http://zhanglab.ccmb.med.umich.edu/ModRefiner.
大多数蛋白质结构预测算法将结构组装为简化模型，通过减少原子数量来表示氨基酸，以加快构象搜索速度。从这些简化模型构建准确的全原子模型是进行详细功能分析的必要步骤。然而，由于简化模型通常存在不合理的局部扭曲，因此很难确保原子模型在保持良好的局部原子几何形状的同时保留所需的全局拓扑结构。为了解决这个问题，我们开发了一个名为 ModRefiner 的新程序，用于基于两步原子级能量最小化，从 Cα 轨迹构建和精炼蛋白质结构。首先从初始 Cα 轨迹构建主链结构，然后使用组合物理和基于知识的力场与骨架原子一起精修侧链旋转异构体。我们通过使用从头开始和基于模板的结构组装构建初始模型，对 261 个蛋白质进行原子结构精修来测试该方法。与其他最先进的程序相比，ModRefiner 在全局和局部结构方面都有所改进，具有更准确的侧链位置、更好的氢键网络和更少的原子重叠。ModRefiner 可在 http://zhanglab.ccmb.med.umich.edu/ModRefiner 免费获取。
