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TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints

机译:TOUCHSTONE:一种使用基于线程的三级约束的从头算蛋白质结构的预测方法

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摘要

The successful prediction of protein structure from amino acid sequence requires two features: an efficient conformational search algorithm and an energy function with a global minimum in the native state. As a step toward addressing both issues, a threading-based method of secondary and tertiary restraint prediction has been developed and applied to ab initio folding. Such restraints are derived by extracting consensus contacts and local secondary structure from at least weakly scoring structures that, in some cases, can lack any global similarity to the sequence of interest. Furthermore, to generate representative protein structures, a reduced lattice-based protein model is used with replica exchange Monte Carlo to explore conformational space. We report results on the application of this methodology, termed TOUCHSTONE, to 65 proteins whose lengths range from 39 to 146 residues. For 47 (40) proteins, a cluster centroid whose rms deviation from native is below 6.5 (5) Å is found in one of the five lowest energy centroids. The number of correctly predicted proteins increases to 50 when atomic detail is added and a knowledge-based atomic potential is combined with clustered and nonclustered structures for candidate selection. The combination of the ratio of the relative number of contacts to the protein length and the number of clusters generated by the folding algorithm is a reliable indicator of the likelihood of successful fold prediction, thereby opening the way for genome-scale ab initio folding.
机译:从氨基酸序列成功预测蛋白质结构需要两个特征:有效的构象搜索算法和在天然状态下具有全局最小值的能量函数。作为解决这两个问题的步骤,已经开发了基于线程的第二和第三约束预测方法,并将其应用于从头开始折叠。通过从至少在某些情况下可能与目标序列缺乏任何全局相似性的得分较低的结构中提取共有的接触和局部二级结构,可以得出这种约束。此外,为了生成代表性的蛋白质结构,将基于简化晶格的蛋白质模型与副本交换Monte Carlo一起使用以探索构象空间。我们报告了这种方法(称为TOUCHSTONE)对65种蛋白质的应用结果,这些蛋白质的长度范围为39至146个残基。对于47(40)种蛋白质,在五个最低能量的质心之一中发现了簇质心,其均方根与天然有效值的偏差低于6.5(5)Å。当添加原子详细信息并将基于知识的原子势与簇状和非簇状结构结合在一起以进行候选物选择时,正确预测的蛋白质数量将增加到50。接触的相对数量与蛋白质长度的比值以及由折叠算法生成的簇数的组合是成功进行折叠预测的可能性的可靠指标,从而为基因组规模的从头计算折叠开辟了道路。

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