Predictive and reproducible de novo all-atom folding of a β-hairpin loop in an improved free-energy forcefield

被引:9
作者
Verma, Abhinav [1 ]
Wenzel, Wolfgang
机构
[1] Forschungszentrum Karlsruhe, Inst Comp Sci, D-76021 Karlsruhe, Germany
[2] Forschungszentrum Karlsruhe, Inst Nanotechnol, D-76021 Karlsruhe, Germany
关键词
STOCHASTIC OPTIMIZATION METHODS; PROTEIN-STRUCTURE PREDICTION; DYNAMICS; MODEL; MINIMIZATION; LANDSCAPES;
D O I
10.1088/0953-8984/19/28/285213
中图分类号
O469 [凝聚态物理学];
学科分类号
070205 ;
摘要
We have recently improved our free- energy forcefield for all- atom de novo protein folding to address the folding of proteins with beta- sheet secondary structure. The folding of beta strands is nevertheless more difficult than that of helical proteins, because of non- local interactions between regions of the protein chain that are not consecutive in the amino acid sequence. Here we use a greedy version of the basin- hopping technique with our free- energy forcefield PFF02 to reproducibly and predictively fold the structure of a beta- hairpin loop. The lowest energy structure found in the simulation has a backbone root mean square deviation of only 2.62 Alpha to the native conformation. The side- chain alignment is also correctly predicted, as are four of the five backbone hydrogen bonds found in the native structure.
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页数:9
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