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预测蛋白质和蛋白质复合物稳定性的变化:对1000多个突变的研究

Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations.

作者信息

Guerois Raphael, Nielsen Jens Erik, Serrano Luis

机构信息

EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany.

出版信息

J Mol Biol. 2002 Jul 5;320(2):369-87. doi: 10.1016/S0022-2836(02)00442-4.

DOI:10.1016/S0022-2836(02)00442-4
PMID:12079393
Abstract

We have developed a computer algorithm, FOLDEF (for FOLD-X energy function), to provide a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF was tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins. FOLDEF uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FOLDEF have been weighted using empirical data obtained from protein engineering experiments. First, we considered a training database of 339 mutants in nine different proteins and optimised the set of parameters and weighting factors that best accounted for the changes in stability of the mutants. The predictive power of the method was then tested using a blind test mutant database of 667 mutants, as well as a database of 82 protein-protein complex mutants. The global correlation obtained for 95 % of the entire mutant database (1030 mutants) is 0.83 with a standard deviation of 0.81 kcal mol(-1) and a slope of 0.76. The present energy function uses a minimum of computational resources and can therefore easily be used in protein design algorithms, and in the field of protein structure and folding pathways prediction where one requires a fast and accurate energy function. FOLDEF is available via a web-interface at http://fold-x.embl-heidelberg.de

摘要

我们开发了一种计算机算法FOLDEF(基于FOLD-X能量函数),用于快速定量评估对蛋白质和蛋白质复合物稳定性有贡献的相互作用的重要性。FOLDEF的预测能力在一组非常大的点突变体(1088个突变体)上进行了测试,这些突变体涵盖了蛋白质中发现的大部分结构环境。FOLDEF使用蛋白质结构的完整原子描述。FOLDEF中考虑的不同能量项已根据从蛋白质工程实验获得的经验数据进行了加权。首先,我们考虑了九个不同蛋白质中339个突变体的训练数据库,并优化了最能解释突变体稳定性变化的参数集和加权因子。然后使用一个包含667个突变体的盲测突变体数据库以及一个包含82个蛋白质-蛋白质复合物突变体的数据库来测试该方法的预测能力。对于整个突变体数据库(1030个突变体)中95%的数据,得到的全局相关性为0.83,标准差为0.81千卡摩尔(-1),斜率为0.76。当前的能量函数使用的计算资源最少,因此可以很容易地用于蛋白质设计算法以及蛋白质结构和折叠途径预测领域,在这些领域中需要快速准确的能量函数。可通过网页界面http://fold-x.embl-heidelberg.de获取FOLDEF。

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