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SMART WALKING - A NEW METHOD FOR BOLTZMANN SAMPLING OF PROTEIN CONFORMATIONS

机译:智能行走-蛋白质构象的Boltzmann采样的新方法

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A new Monte Carlo algorithm is presented for the efficient sampling of protein conformation space called the Smart-Walking (S-Walking) method. The method is implemented using a hybrid Monte Carlo protocol. The S-Walking method is closely related to the J-Walking method proposed by Frantz et al. (J. Chem. Phys. 93, 2769, 1990). Like the J-Walking method, the S-Walking method runs two walkers, one at the temperature of interest, the other at a higher temperature which more efficiently generates ergodic distributions. Instead of sampling from the Boltzmann distribution of the higher temperature walker as in J-Walking, S-Walking first approximately minimizes the structures being jumped into, and then uses the relaxed structures as the trial moves at the low temperature. By jumping into a relaxed structure, or a local minimum, the jump acceptance ratio increases dramatically, which makes the protein system easily undergo barrier-crossing events from one basin to another, thus greatly improving the ergodicity of the sampling. The method approximately preserves detailed balance provided the time between jumps is large enough to allow effective sampling of conformations in each local basin. (C) 1997 American Institute of Physics. [References: 36]
机译:提出了一种新的蒙特卡洛算法,用于有效采样蛋白质构象空间,称为智能行走(S-Walking)方法。该方法是使用混合蒙特卡洛协议实现的。 S-步行方法与Frantz等人提出的J-步行方法密切相关。 (J.Chem.Phys.93,2769,1990)。与J-Walking方法类似,S-Walking方法运行两个助步器,一个在目标温度下运行,另一个在更高的温度下运行,从而更有效地生成遍历分布。不同于像J-Walking那样从高温助行器的Boltzmann分布中采样,S-Walking首先大致最小化了跳入其中的结构,然后随着试验在低温下移动而使用了松弛的结构。通过跳入松弛结构或局部最小值,跳变接受率急剧增加,这使得蛋白质系统很容易经历从一个盆地到另一个盆地的障碍穿越事件,从而大大提高了采样的遍历性。如果跳跃之间的时间足够长,可以在每个局部盆地中对构造进行有效采样,则该方法可以大致保持详细的平衡。 (C)1997美国物理研究所。 [参考:36]

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