Improving protein structure prediction with model-based search
Improving protein structure prediction with model-based search
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DOI：
10.1093/bioinformatics/bti1029
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发表时间：
2005-06-01
期刊：
影响因子：
5.8
通讯作者：
Brock, O
中科院分区：
文献类型：
--
作者：
Brunette, TJ;Brock, O
动机：从头蛋白质结构预测可以表述为在高维空间中的搜索。最常用的计算工具来解决这样的搜索问题之一是蒙特卡罗方法。我们提出了一种新的搜索技术，称为基于模型的搜索。该方法对高维搜索空间进行采样，以构建底层函数的近似模型。该模型在感兴趣的领域中逐步完善，而不感兴趣的领域则被排除在进一步的探索之外。基于模型的搜索的效率来自于这样一个事实，即在搜索空间的探索期间获得的信息用于指导进一步的探索。相比之下，基于蒙特卡罗的技术缺乏内存和探索是基于随机游走，忽略了在前面的steps.Results中获得的信息：基于模型的搜索应用于蛋白质结构预测，搜索是用来找到蛋白质的能量景观的全局最小值。我们表明，基于模型的搜索更有效地利用计算资源，找到比领先的蛋白质结构预测方法之一，它依赖于一个定制的蒙特卡罗方法来执行搜索的低能量构象的蛋白质。随着搜索问题维度的增加，性能改进变得更加明显。我们认为，基于模型的搜索将使更准确的蛋白质结构预测比以前可能的。此外，我们相信，类似的性能改善，可以预期在其他问题，目前解决基于蒙特卡洛的搜索方法。
Motivation: De novo protein structure prediction can be formulated as search in a high-dimensional space. One of the most frequently used computational tools to solve such search problems is the Monte Carlo method. We present a novel search technique, called model-based search. This method samples the high-dimensional search space to build an approximate model of the underlying function. This model is incrementally refined in areas of interest, whereas areas that are not of interest are excluded from further exploration. Model-based search derives its efficiency from the fact that the information obtained during the exploration of the search space is used to guide further exploration. In contrast, Monte Carlo-based techniques lack memory and exploration is performed based on random walks, ignoring the information obtained in previous steps.Results: Model-based search is applied to protein structure prediction, where search is employed to find the global minimum of the protein's energy landscape. We show that model-based search uses computational resources more efficiently to find lower-energy conformations of proteins than one of the leading protein structure prediction methods, which relies on a tailored Monte Carlo method to perform a search. The performance improvements become more pronounced as the dimensionality of the search problem increases. We argue that model-based search will enable more accurate protein structure prediction than was previously possible. Furthermore, we believe that similar performance improvements can be expected in other problems that are currently solved using Monte Carlo-based search methods.