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Statistical energy potential: reduced representation of Dehouck-Gilis-Rooman function by selecting against decoy datasets

机译:统计能量势:通过选择诱饵数据集来减少Dehouck-Gilis-Rooman函数的表示

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摘要

Statistical effective energy function (SEEF) is derived from the statistical analysis of the database of known protein structures. Dehouck-Gilis-Rooman (DGR) group has recently created a new generation of SEEF in which the additivity of the energy terms was manifested by decomposing the total folding free energy into a sum of lower order terms. We have tried to optimize the potential function based on their work. By using decoy datasets as screening filter, and through modification of algorithms in calculation of accessible surface area and residue-residue interaction cutoff, four new combinations of the energy terms were found to be comparable to DGR potential in performance test. Most importantly, the term number was reduced from the original 30 terms to only 5 in our results, thereby substantially decreasing the computation time while the performance was not sacrificed. Our results further proved the additivity and manipulability of the DGR original energy function, and our new combination of the energy could be used in prediction of protein structures.
机译:统计有效能量函数(SEEF)来自已知蛋白质结构数据库的统计分析。 Dehouck-Gilis-Rooman(DGR)组最近创建了新一代SEEF,其中,通过将总折叠自由能分解为低阶项的总和来体现能量项的可加性。我们已尝试根据他们的工作来优化潜在功能。通过使用诱饵数据集作为筛选过滤器,并通过修改可访问表面积和残渣-残渣相互作用截止值的计算算法,发现在性能测试中四种新的能量项组合可与DGR潜力相媲美。最重要的是,在我们的结果中,术语数从最初的30个术语减少到只有5个,从而在不牺牲性能的情况下大大减少了计算时间。我们的结果进一步证明了DGR原始能量函数的可加性和可操作性,并且我们新的能量组合可用于预测蛋白质结构。

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