BACKGROUND Elucidating the native structure of a protein molecule from its sequence of amino acids, a problem known as de novo structure prediction, is a long standing challenge in computational structural biology. Difficulties in silico arise due to the high dimensionality of the protein conformational space and the ruggedness of the associated energy surface. The issue of multiple minima is a particularly troublesome hallmark of energy surfaces probed with current energy functions. In contrast to the true energy surface, these surfaces are weakly-funneled and rich in comparably deep minima populated by non-native structures. For this reason, many algorithms seek to be inclusive and obtain a broad view of the low-energy regions through an ensemble of low-energy (decoy) conformations. Conformational diversity in this ensemble is key to increasing the likelihood that the native structure has been captured. METHODS We propose an evolutionary search approach to address the multiple-minima problem in decoy sampling for de novo structure prediction. Two population-based evolutionary search algorithms are presented that follow the basic approach of treating conformations as individuals in an evolving population. Coarse graining and molecular fragment replacement are used to efficiently obtain protein-like child conformations from parents. Potential energy is used both to bias parent selection and determine which subset of parents and children will be retained in the evolving population. The effect on the decoy ensemble of sampling minima directly is measured by additionally mapping a conformation to its nearest local minimum before considering it for retainment. The resulting memetic algorithm thus evolves not just a population of conformations but a population of local minima. RESULTS AND CONCLUSIONS Results show that both algorithms are effective in terms of sampling conformations in proximity of the known native structure. The additional minimization is shown to be key to enhancing sampling capability and obtaining a diverse ensemble of decoy conformations, circumventing premature convergence to sub-optimal regions in the conformational space, and approaching the native structure with proximity that is comparable to state-of-the-art decoy sampling methods. The results are shown to be robust and valid when using two representative state-of-the-art coarse-grained energy functions.

中文翻译：

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一种基于种群的进化搜索方法来解决从头蛋白质结构预测中的多重最小值问题。

背景从蛋白质分子的氨基酸序列阐明蛋白质分子的天然结构，称为从头结构预测的问题，是计算结构生物学中长期存在的挑战。由于蛋白质构象空间的高维性和相关能量表面的坚固性，在硅胶中出现了困难。多重最小值的问题是用当前能量函数探测的能量表面的一个特别麻烦的标志。与真正的能量表面相反，这些表面呈弱漏斗状，并且富含由非原生结构填充的相对较深的最小值。出于这个原因，许多算法寻求包容性并通过低能量（诱饵）构象的集合获得低能量区域的广阔视野。这个集合中的构象多样性是增加原生结构被捕获的可能性的关键。方法我们提出了一种进化搜索方法来解决从头结构预测的诱饵采样中的多重最小值问题。提出了两种基于种群的进化搜索算法，它们遵循将构象视为进化种群中的个体的基本方法。粗粒化和分子片段置换用于有效地从父母那里获得类似蛋白质的子构象。势能既用于偏向父母选择，也用于确定哪些父母和孩子的子集将保留在不断发展的种群中。在考虑保留之前，通过将构象额外映射到其最近的局部最小值来直接测量对采样最小值的诱饵集合的影响。由此产生的模因算法不仅进化了一个构象群，而且进化了一个局部最小值群。结果与结论 结果表明，这两种算法在对已知天然结构附近的构象进行采样方面都是有效的。额外的最小化被证明是提高采样能力和获得诱饵构象的多样化集合的关键，避免过​​早收敛到构象空间中的次优区域，并以与状态相当的接近度接近原生结构-艺术诱饵采样方法。