Three-dimensional protein structure prediction based on memetic algorithms
Three-dimensional protein structure prediction based on memetic algorithms
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DOI：
10.1016/j.cor.2017.11.015
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
L. Corrêa;Bruno Borguesan;M. Krause;M. Dorn
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文献类型：
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作者：
L. Corrêa;Bruno Borguesan;M. Krause;M. Dorn
蛋白质三级结构预测是结构生物信息学中一个具有挑战性的问题，根据计算复杂性理论，蛋白质三级结构预测属于NP难问题。在本文中，我们提出了一种第一原理方法，利用已知蛋白质结构的优先信息来解决蛋白质三维结构预测问题。我们这样做，通过设计一个多模态模因算法，使用一个三元树结构的人口结盟的局部搜索策略的进化方法。该方法已开发的基础上，使用有前途的进化组件的组合，以解决有关的多模态问题的增量方法。针对该问题提出了三种模因算法。第一个修改了一个基本版本的模因算法引入修改后的全局搜索算子。第二种算法使用不同的种群结构作为模因算法。最后，最后一个算法由全局算子和多峰策略的集成，以处理蛋白质结构预测问题固有的多峰性。该实现利用蛋白质数据库中存储的结构知识来指导蛋白质构象搜索空间的开发和限制。预测的三维蛋白质结构进行了分析，关于均方根偏差和全球距离总分测试。三个版本的结果优于基本版本的模因算法。第三个算法克服了前两个的结果，证明了适应的方法来处理问题的复杂性的重要性。此外，所取得的结果是拓扑兼容的实验对应，证实了我们的方法有前途的性能。
Tertiary protein structure prediction is a challenging problem in Structural Bioinformatics and is classified according to the computational complexity theory as aNP-hardproblem. In this paper, we proposed a first-principle method that makes use ofa prioriinformation about known protein structures to tackle the three-dimensional protein structure prediction problem. We do so by designing a multimodal memetic algorithm that uses an evolutionary approach with a ternary tree-structured population allied to a local search strategy. The method has been developed based on an incremental approach using the combination of promising evolutionary components to address the concerned multimodal problem. Three memetic algorithms focused on the problem are proposed. The first one modifies a basic version of a memetic algorithm by introducing modified global search operators. The second uses a different population structure for the memetic algorithm. And finally, the last algorithm consists of the integration of global operators and multimodal strategies to deal with the inherent multimodality of the protein structure prediction problem. The implementations take advantage of structural knowledge stored in theProtein Data Bankto guide the exploiting and restrict the protein conformational search space. Predicted three-dimensional protein structures were analyzed regarding root mean square deviation and the global distance total score test. Obtained results for the three versions outperformed the basic version of the memetic algorithm. The third algorithm overcomes the results of the previous two, demonstrating the importance of adapting the method to deal with the complexities of the problem. In addition, the achieved results are topologically compatible with the experimental correspondent, confirming the promising performance of our approach.