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首页> 外文期刊>Biophysical Chemistry: An International Journal Devoted to the Physical Chemistry of Biological Phenomena >A simple procedure to weight empirical potentials in a fitness function so as to optimize its performance in ab initio protein-folding problem
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A simple procedure to weight empirical potentials in a fitness function so as to optimize its performance in ab initio protein-folding problem

机译：权衡适应度函数中的经验势的简单方法，以优化其从头算蛋白质折叠问题的性能

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In an approach to the protein folding problem by a Genetic Algorithm, the fitness function plays a critical role. Empirical potentials are generally used to build the fitness function, and they must be weighted to obtain a valuable one. The weights are generally found by the comparison with a set of misfolded structures (decoys), but a dependence of the obtained fitness generally arises on the used decoys. Here we describe a general procedure to find out, from a given set of potentials, their better linear combination that could either identify the wild structure or prove their powerlessness. We use topological considerations over the hyperspace of the potentials, and a multiple linear inequalities solver. The iterated method flows through the following steps: it determines a direction in the hyperspace of the potentials, which identifies the native structure as a vertex among a set of misfolded decoys. A multiple linear inequalities solver obtains the direction. A Genetic Algorithm, tailored to the specific problem, uses the fitness function defined by this direction and generally reaches a new structure better than the experimental one, which is added to the ensemble. The decoys so generated are not dependent on a deterministic criterion. This iterative procedure can be stopped either by identifying an effective fitness function or by proving the impossibility of its achievement. In order to test the method under the hardest conditions, we choose numerous and heterogeneous quantities as components of the fitness function. This method could be a useful tool for the scientific community in order to test any fitness proposed and to recognize the most important components on which it is built. (C) 2003 Elsevier B.V. All rights reserved. [References: 42]
机译：在通过遗传算法解决蛋白质折叠问题的方法中，适应度函数起着至关重要的作用。通常使用经验势来建立适应度函数，并且必须对它们进行加权以获得有价值的势能。通常通过与一组错误折叠的结构（诱饵）进行比较来找到权重，但是获得的适应性通常取决于所使用的诱饵。在这里，我们描述了一种通用程序，可以从给定的一组电位中找出它们更好的线性组合，这些组合可以识别野生结构或证明其无能为力。我们在势能的超空间上使用拓扑考虑，并使用多个线性不等式求解器。迭代方法执行以下步骤：确定电位超空间中的方向，该方向将本机结构标识为一组错误折叠的诱饵中的顶点。多个线性不等式求解器获得方向。针对特定问题量身定制的遗传算法使用了该方向定义的适应度函数，通常比添加到整体中的实验结构更好地达到了新的结构。这样产生的诱饵不取决于确定性标准。可以通过确定有效的适应度函数或证明不可能实现此迭代过程来停止此迭代过程。为了在最困难的条件下测试该方法，我们选择大量不同种类的量作为适应度函数的组成部分。该方法对于科学界来说可能是有用的工具，以便测试所建议的适用性并识别其基础上最重要的组成部分。 （C）2003 Elsevier B.V.保留所有权利。 [参考：42]

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