Ramya L, Nehru Viji Shankaran, Arun Prasad Pandurangan, Kanagasabai Vadivel, Gautham Namasivayam
Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Chennai, 600025, India.
Institute of Structural and Molecular Biology and Crystallography, Department of Biological Sciences, Birkbeck College, University of London, London, UK.
Biophys Rev. 2010 Dec;2(4):169-179. doi: 10.1007/s12551-010-0039-y. Epub 2010 Nov 16.
This review describes the MOLS method and its applications. This computational method has been developed in our laboratory primarily to explore the conformational space of small peptides and identify features of interest, particularly the minima, i.e., the low energy conformations. A systematic "brute-force" search through the vast conformational space for such features faces the insurmountable problem of combinatorial explosion, whilst other techniques, e.g., Monte Carlo searches, are somewhat limited in their region of exploration and may be considered inexhaustive. The MOLS method, on the other hand, uses a sampling technique commonly employed in experimental design theory to identify a small sample of the conformational space that nevertheless retains information about the entire space. The information is extracted using a technique that is a variant of the self-consistent mean field technique, which has been used to identify, for example, the optimal set of side-chain conformations in a protein. Applications of the MOLS method to understand peptide structure, predict the structures of loops in proteins, predict three-dimensional structures of small proteins, and arrive at the best conformation, orientation, and positions of a small molecule ligand in a protein receptor site have all yielded satisfactory results.
本综述介绍了MOLS方法及其应用。这种计算方法是在我们实验室开发的，主要用于探索小肽的构象空间并识别感兴趣的特征，特别是最小值，即低能量构象。通过在巨大的构象空间中进行系统的“暴力”搜索来寻找此类特征，面临着组合爆炸这一无法克服的问题，而其他技术，如蒙特卡罗搜索，在其探索区域上存在一定限制，可能被认为是不彻底的。另一方面，MOLS方法使用了实验设计理论中常用的一种采样技术，来识别构象空间的一个小样本，该样本仍然保留了关于整个空间的信息。信息是使用一种自洽平均场技术的变体提取的，例如，该技术已被用于识别蛋白质中侧链构象的最佳集合。MOLS方法在理解肽结构、预测蛋白质中环的结构、预测小蛋白质的三维结构以及确定小分子配体在蛋白质受体位点的最佳构象、取向和位置等方面的应用均取得了令人满意的结果。
