Near-native structure refinement using in vacuo energy minimization

被引:109
作者
Summa, Christopher M. [1 ]
Levitt, Michael [1 ]
机构
[1] Stanford Univ, Sch Med, Dept Biol Struct, Stanford, CA 94305 USA
关键词
protein; empirical potential; molecular mechanics potential; knowledge-based; protein structure refinement;
D O I
10.1073/pnas.0611593104
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
One of the greatest shortcomings of macromolecular energy minimization and molecular dynamics techniques is that they generally do not preserve the native structure of proteins as observed by x-ray crystallography. This deformation of the native structure means that these methods are not generally used to refine structures produced by homology-modeling techniques. Here, we use a database of 75 proteins to test the ability of a variety of popular molecular mechanics force fields to maintain the native structure. Minimization from the native structure is a weak test of potential energy functions: It is complemented by a much stronger test in which the same methods are compared for their ability to attract a near-native decoy protein structure toward the native structure. We use a powerfully convergent energy-minimization method and show that, of the traditional molecular mechanics potentials tested, only one showed a modest net improvement over a large data set of structurally diverse proteins. A smooth, differentiable knowledge-based pairwise atomic potential performs better on this test than traditional potential functions. This work is expected to have important implications for protein structure refinement, homology modeling, and structure prediction.
引用
收藏
页码:3177 / 3182
页数:6
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