Ozkan S Banu, Meirovitch Hagai
Center for Computational Biology and Bioinformatics & Department of Molecular Genetics and Biochemistry, University of Pittsburgh School of Medicine, BST W1058, Pittsburgh, Pennsylvania 15261, USA.
J Comput Chem. 2004 Mar;25(4):565-72. doi: 10.1002/jcc.10399.
The energy function of a protein consists of a tremendous number of minima. Locating the global energy minimum (GEM) structure, which corresponds approximately to the native structure, is a severe problem in global optimization. Recently we have proposed a conformational search technique based on the Monte Carlo minimization (MCM) method of Li and Scheraga, where trial dihedral angles are not selected at random within the range [-180 degrees,180 degrees ] (as with MCM) but with biased probabilities depending on the increased structure-energy correlations as the GEM is approached during the search. This method, called the Monte Carlo minimization with an adaptive bias (MCMAB), was applied initially to the pentapeptide Leu-enkephalin. Here we study its properties further by applying it to the larger peptide with bulky side chains, deltorphin (H-Tyr-D-Met-Phe-His-Leu-Met-Asp-NH(2)). We find that on average the number of energy minimizations required by MCMAB to locate the GEM for the first time is smaller by a factor of approximately three than the number required by MCM-in accord with results obtained for Leu-enkephalin.
蛋白质的能量函数由大量的极小值组成。找到与天然结构大致对应的全局能量最小值（GEM）结构，是全局优化中的一个严峻问题。最近我们提出了一种基于Li和Scheraga的蒙特卡罗最小化（MCM）方法的构象搜索技术，其中试验二面角不是在[-180度，180度]范围内随机选择（如MCM方法那样），而是根据搜索过程中接近GEM时结构-能量相关性的增加，以有偏概率进行选择。这种方法称为带自适应偏差的蒙特卡罗最小化（MCMAB），最初应用于五肽亮氨酸脑啡肽。在此，我们将其应用于具有庞大侧链的较大肽类——强啡肽（H-Tyr-D-Met-Phe-His-Leu-Met-Asp-NH₂），进一步研究其性质。我们发现，平均而言，MCMAB首次找到GEM所需的能量最小化次数比MCM所需的次数少约三倍——这与亮氨酸脑啡肽的结果一致。
