基于HP模型的蛋白质结构预测评价函数的比较性分析
Comparative Analysis of Different Evaluation Functions for Protein Structure Prediction Under the HP Model
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摘要: 用于蛋白质结构预测的HP模型抓住了蛋白质组合过程中疏水性显著的事实。这个富有挑战性的组合优化问题已经被广泛地使用元启发式方法来解决。评价函数是元启发式方法成功的关键因素。传统的HP函数的评价函数性能差,因此目前已有一些相应的替代方法。本文分析了7个不同的HP函数的评价函数的有效性,包括:每个函数所提供的区分能力,保持潜在解决方案排名和HP模型初始目标一致的能力,以及它们对于局部搜索方法性能的影响。分析结果表明HP模型不同评价模式的研究值得引起更多关注。Abstract: The HP model for protein structure prediction abstracts the fact that hydrophobicity is a dominant force in the protein folding process. This challenging combinatorial optimization problem has been widely addressed through metaheuristics. The evaluation function is a key component for the success of metaheuristics; the poor discrimination of the conventional evaluation function of the HP model has motivated the proposal of alternative formulations for this component. This comparative analysis inquires into the effectiveness of seven different evaluation functions for the HP model. The degree of discrimination provided by each of the studied functions, their capability to preserve a rank ordering among potential solutions which is consistent with the original objective of the HP model, as well as their effect on the performance of local search methods are analyzed. The obtained results indicate that studying alternative evaluation schemes for the HP model represents a highly valuable direction which merits more attention.