Department of Chemistry, French Family Science Center, Duke University, Durham, North Carolina 27708-0346, USA.
J Comput Chem. 2010 Aug;31(11):2164-8. doi: 10.1002/jcc.21506.
We develop a new global optimization strategy, gradient-directed Monte Carlo (GDMC) sampling, to optimize protein sequence for a target structure using RosettaDesign. GDMC significantly improves the sampling of sequence space, compared to the classical Monte Carlo search protocol, for a fixed backbone conformation as well as for the simultaneous optimization of sequence and structure. As such, GDMC sampling enhances the efficiency of protein design.
我们开发了一种新的全局优化策略——梯度导向的蒙特卡罗（GDMC）采样，用于使用 RosettaDesign 优化目标结构的蛋白质序列。与经典的蒙特卡罗搜索协议相比，GDMC 大大改善了序列空间的采样，无论是对于固定的骨架构象还是同时优化序列和结构都是如此。因此，GDMC 采样提高了蛋白质设计的效率。
