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使用基于全原子ECEPP力场、隐式溶剂模型和结构弛豫的评分函数识别类天然蛋白质结构。

Identifying native-like protein structures with scoring functions based on all-atom ECEPP force fields, implicit solvent models and structure relaxation.

作者信息

Arnautova Yelena A, Vorobjev Yury N, Vila Jorge A, Scheraga Harold A

机构信息

Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca New York 14853-1301, USA.

出版信息

Proteins. 2009 Oct;77(1):38-51. doi: 10.1002/prot.22414.

Abstract

Availability of energy functions which can discriminate native-like from non-native protein conformations is crucial for theoretical protein structure prediction and refinement of low-resolution protein models. This article reports the results of benchmark tests for scoring functions based on two all-atom ECEPP force fields, that is, ECEPP/3 and ECEPP05, and two implicit solvent models for a large set of protein decoys. The following three scoring functions are considered: (i) ECEPP05 plus a solvent-accessible surface area model with the parameters optimized with a set of protein decoys (ECEPP05/SA); (ii) ECEPP/3 plus the solvent-accessible surface area model of Ooi et al. (Proc Natl Acad Sci USA 1987;84:3086-3090) (ECEPP3/OONS); and (iii) ECEPP05 plus an implicit solvent model based on a solution of the Poisson equation with an optimized Fast Adaptive Multigrid Boundary Element (FAMBEpH) method (ECEPP05/FAMBEpH). Short Monte Carlo-with-Minimization (MCM) simulations, following local energy minimization, are used as a scoring method with ECEPP05/SA and ECEPP3/OONS potentials, whereas energy calculation is used with ECEPP05/FAMBEpH. The performance of each scoring function is evaluated by examining its ability to distinguish between native-like and non-native protein structures. The results of the tests show that the new ECEPP05/SA scoring function represents a significant improvement over the earlier ECEPP3/OONS version of the force field. Thus, it is able to rank native-like structures with C(alpha) root-mean-square-deviations below 3.5 A as lowest-energy conformations for 76% and within the top 10 for 87% of the proteins tested, compared with 69 and 80%, respectively, for ECEPP3/OONS. The use of the FAMBEpH solvation model, which provides a more accurate description of the protein-solvent interactions, improves the discriminative ability of the scoring function to 89%. All failed tests in which the native-like structures cannot be discriminated as those with low energy, are due to omission of protein-protein interactions. The results of this study represent a benchmark in force-field development, and may be useful for evaluation of the performance of different force fields.

摘要

能够区分天然样与非天然蛋白质构象的能量函数对于理论蛋白质结构预测和低分辨率蛋白质模型的优化至关重要。本文报告了基于两种全原子ECEPP力场(即ECEPP/3和ECEPP05)以及两种隐式溶剂模型对大量蛋白质诱饵进行评分函数基准测试的结果。考虑了以下三种评分函数:(i)ECEPP05加上一个溶剂可及表面积模型,其参数用一组蛋白质诱饵进行了优化(ECEPP05/SA);(ii)ECEPP/3加上大井等人的溶剂可及表面积模型(《美国国家科学院院刊》1987年;84:3086 - 3090)(ECEPP3/OONS);(iii)ECEPP05加上基于泊松方程解并采用优化的快速自适应多重网格边界元(FAMBEpH)方法的隐式溶剂模型(ECEPP05/FAMBEpH)。在局部能量最小化之后进行的短蒙特卡罗最小化(MCM)模拟被用作ECEPP05/SA和ECEPP3/OONS势的评分方法,而能量计算则用于ECEPP05/FAMBEpH。通过检查其区分天然样和非天然蛋白质结构的能力来评估每个评分函数的性能。测试结果表明,新的ECEPP05/SA评分函数相对于早期的ECEPP3/OONS版本的力场有显著改进。因此,对于测试的76%的蛋白质,它能够将Cα均方根偏差低于3.5 Å的天然样结构列为最低能量构象,87%的蛋白质其天然样结构在前10名,而ECEPP3/OONS分别为69%和80%。使用能更准确描述蛋白质 - 溶剂相互作用的FAMBEpH溶剂化模型,将评分函数的区分能力提高到了89%。所有未能将天然样结构识别为低能量结构的测试失败情况,都是由于遗漏了蛋白质 - 蛋白质相互作用。本研究结果代表了力场开发中的一个基准,可能有助于评估不同力场的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/205d/4502597/48ac365d124e/nihms130348f1.jpg

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