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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests
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Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests

机译：使用基于UNRES力场的分层协议进行基于物理的蛋白质结构预测：两次盲测中的评估

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摘要

Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of α and α+β proteins [60-70 residues with C~α rms deviation (rmsd) <6 A]. However, for α+β proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two a and three α+β, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue α+β protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-A C ~α ,rmsd. So far this protein is the largest α+β protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly α-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 A rmsd, except the 32 C-terminal residues, most of which form a β-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.
机译：基于热力学假设，描述了在我们实验室中开发的蛋白质结构预测方法的最新改进。利用我们基于物理学的UNRES能量函数和全局优化的构象空间退火方法，可以在统一残基水平上广泛地搜索构象空间。然后将最低能量的粗粒度结构转换为全原子表示，并使用ECEPP / 3力场将其能量最小化。最近在两项蛋白质结构预测盲测中评估了该程序。在第一个盲法测试中，我们预测了α和α+β蛋白的大片段[60-70个残基，C〜αrms偏差（rmsd）<6 A]。但是，对于α+β蛋白，尽管rmsd值较低，但仍会出现明显的拓扑错误。在第二个练习中，我们预测了五个蛋白质（两个a和三个α+β，具有53-235个残基的大小）的整体结构，其准确性非常好。特别是，对于基因组靶标TM0487（来自海洋栖热菌的102个残基的α+β蛋白），我们预测了7.3-A C〜α，rmsd的完整，拓扑正确的结构。迄今为止，该蛋白质是仅根据氨基酸序列以及基于物理学的势能函数和搜索程序预测的最大的α+β蛋白质。对于目标T0198，即来自海藻的磷酸盐转运系统调节剂PhoU（一种235个残基，主要是α螺旋蛋白），我们预测了整个六螺旋束的拓扑结构在8 A rmsd内正确，除了32个C端残基，大多数形成β-发夹。本工作中描述的这些和其他示例说明了基于物理学的蛋白质结构预测的重大进展。

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