Nakamura Hideaki
Department of Functional Materials Engineering, Fukuoka Institute of Technology, 3-30-1 Wajirohigashi, Higashi-ku, Fukuoka 811-0295, Japan.
J Comput Chem. 2002 Mar;23(4):511-6. doi: 10.1002/jcc.10034.
A Monte Carlo sampling algorithm for searching a scale-transformed conformational energy space of polypeptides is presented. This algorithm is based on the assumption that energy barriers can be overcome by a uniform sampling of the logarithmically transformed energy space. This algorithm is tested with Met-enkephalin. For comparison, the entropy sampling Monte Carlo (ESMC) simulation is performed. First, the global minimum is easily found by the optimization of a scale-transformed energy space. With a new Monte Carlo sampling, energy barriers of 3000 kcal/mol are frequently overcome, and low-energy conformations are sampled more efficiently than with ESMC simulations. Several thermodynamic quantities are calculated with good accuracy.
提出了一种用于搜索多肽尺度变换构象能量空间的蒙特卡罗采样算法。该算法基于这样的假设：通过对对数变换后的能量空间进行均匀采样可以克服能量障碍。用甲硫氨酸脑啡肽对该算法进行了测试。为作比较，进行了熵采样蒙特卡罗（ESMC）模拟。首先，通过对尺度变换后的能量空间进行优化，很容易找到全局最小值。采用新的蒙特卡罗采样，经常能克服3000千卡/摩尔的能量障碍，并且与ESMC模拟相比，能更有效地对低能量构象进行采样。以较高的精度计算了几个热力学量。
