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Potential function smoothing with applications to molecular docking.

机译:应用于分子对接的潜在功能平滑处理。

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Structure prediction is often modelled as an optimization problem in which a computer must minimize the potential energy of a molecular system in terms of its atomic coordinates. Potential energy surfaces for chemical systems present a large number of local minima which frustrate the search for global minima. This roughness of conformational space is the primary impediment to conformational search methods. Most present methods for optimization and conformational search do not address the roughness issue directly, but instead rely on stochastic or heuristic mechanisms to traverse surface barriers.; The method investigated herein mathematically and analytically transforms an original potential energy surface into a continuous series of progressively smoother surfaces. Among the achievements of this research are a deformable variant of the AMBER/OPLS potential function and its application to conformational search problems. A thorough analysis of potential smoothing applied to capped dialanine peptide reveals that the deformed surfaces retain the most dominant features of the original surface but have many fewer minima. Consequently, deformed surfaces are easier to search. Strong qualitative and quantitative correlations are identified between potential smoothing and simulated annealing, the present state-of-the-art technique for conformational optimization. These observations lead to the conclusion that potential smoothing is a deterministic analog of simulated annealing.; The analysis of potential smoothing applied to capped dialanine peptide identifies three characteristics of potential smoothing: merging, shifting, and crossing. Each of these effects has a direct correlate in simulated annealing. Crossing is shown to account for the reduced efficacy of potential smoothing and simulated annealing for the global optimization of chemical systems. Analysis of this feature of potential smoothing led to the coupling of potential smoothing to a local search procedure which attempts to correct for crossing events. The results of the hybrid Potential Smoothing and Search (PSS) method applied cycloheptadecane and molecular docking of trypsin-benzamidine and HIV protease-XK263 are presented. Refinements to the concepts presented herein will enable investigations of greater computational complexity than currently possible.
机译:结构预测通常被建模为一个优化问题,其中计算机必须根据其原子坐标将分子系统的势能最小化。化学系统的势能面呈现大量局部最小值,这阻碍了对整体最小值的搜索。构象空间的这种粗糙度是构象搜索方法的主要障碍。目前,大多数用于优化和构象搜索的方法并不直接解决粗糙度问题,而是依靠随机或启发式机制来遍历表面障碍。本文研究的方法在数学上和分析上将原始势能表面转换为连续系列的逐渐平滑的表面。这项研究的成就包括AMBER / OPLS势函数的可变形变体及其在构象搜索问题中的应用。彻底分析了应用于封端的二嘌呤肽的潜在平滑度,发现变形的表面保留了原始表面的最主要特征,但极小值却少得多。因此,变形的表面更易于搜索。在潜在的平滑和模拟退火之间确定了很强的定性和定量相关性,这是目前用于构象优化的最新技术。这些观察得出的结论是,电位平滑是模拟退火的确定性模拟。应用于封端的二嘌呤肽的潜在平滑分析确定了潜在平滑的三个特征:合并,移位和交叉。这些效应中的每一个在模拟退火中都具有直接相关性。交叉表明,对于化学系统的整体优化,潜在的平滑和模拟退火效率降低。对电势平滑特征的分析导致电势平滑与试图纠正交叉事件的局部搜索过程耦合。介绍了使用环庚烷和胰蛋白酶-苯甲m和HIV蛋白酶-XK263分子对接的混合电位平滑和搜索(PSS)方法的结果。对本文提出的概念的改进将使得能够进行比当前可能的计算复杂度更高的研究。

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