Ytreberg F Marty, Zuckerman Daniel M
Department of Physics, University of Idaho, Moscow, ID 83844-0903, USA.
Proc Natl Acad Sci U S A. 2008 Jun 10;105(23):7982-7. doi: 10.1073/pnas.0706063105.
There is a great need for improved statistical sampling in a range of physical, chemical, and biological systems. Even simulations based on correct algorithms suffer from statistical error, which can be substantial or even dominant when slow processes are involved. Further, in key biomolecular applications, such as the determination of protein structures from NMR data, non-Boltzmann-distributed ensembles are generated. We therefore have developed the "black-box" strategy for re-weighting a set of configurations generated by arbitrary means to produce an ensemble distributed according to any target distribution. In contrast to previous algorithmic efforts, the black-box approach exploits the configuration-space density observed in a simulation, rather than assuming a desired distribution has been generated. Successful implementations of the strategy, which reduce both statistical error and bias, are developed for a one-dimensional system, and a 50-atom peptide, for which the correct 250-to-1 population ratio is recovered from a heavily biased ensemble.
在一系列物理、化学和生物系统中，对改进统计抽样有巨大需求。即使基于正确算法的模拟也存在统计误差，当涉及缓慢过程时，这种误差可能很大甚至占主导地位。此外，在关键的生物分子应用中，例如从核磁共振数据确定蛋白质结构时，会生成非玻尔兹曼分布的系综。因此，我们开发了一种“黑箱”策略，用于对通过任意方式生成的一组构型进行重新加权，以产生根据任何目标分布分布的系综。与先前的算法努力不同，黑箱方法利用在模拟中观察到的构型空间密度，而不是假设已经生成了所需的分布。该策略在降低统计误差和偏差方面的成功实现，是针对一维系统和一个50原子的肽开发的，对于该肽，从严重偏差的系综中恢复了正确的250比1的种群比率。
