Eyrich V A, Standley D M, Felts A K, Friesner R A
Department of Chemistry and Center for Biomolecular Simulation, Columbia University, New York, New York 10027, USA.
Proteins. 1999 Apr 1;35(1):41-57.
We report a new method for predicting protein tertiary structure from sequence and secondary structure information. The predictions result from global optimization of a potential energy function, including van der Waals, hydrophobic, and excluded volume terms. The optimization algorithm, which is based on the alphaBB method developed by Floudas and coworkers (Costas and Floudas, J Chem Phys 1994;100:1247-1261), uses a reduced model of the protein and is implemented in both distance and dihedral angle space, enabling a side-by-side comparison of methodologies. For a set of eight small proteins, representing the three basic types--all alpha, all beta, and mixed alpha/beta--the algorithm locates low-energy native-like structures (less than 6A root mean square deviation from the native coordinates) starting from an unfolded state. Serial and parallel implementations of this methodology are discussed.
我们报告了一种根据序列和二级结构信息预测蛋白质三级结构的新方法。预测结果来自势能函数的全局优化，该势能函数包括范德华力、疏水力和排除体积项。优化算法基于弗洛达斯及其同事开发的alphaBB方法（科斯塔斯和弗洛达斯，《化学物理杂志》1994年；100:1247 - 1261），使用蛋白质的简化模型，并在距离和二面角空间中实现，从而能够对不同方法进行并行比较。对于一组代表三种基本类型（全α型、全β型和混合α/β型）的八个小蛋白质，该算法从非折叠状态开始定位低能量的天然样结构（与天然坐标的均方根偏差小于6埃）。本文还讨论了该方法的串行和并行实现。
