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在分子动力学过程中对二面角势垒进行可逆缩放以改善环肽的结构预测。

Reversible scaling of dihedral angle barriers during molecular dynamics to improve structure prediction of cyclic peptides.

作者信息

Riemann R N, Zacharias M

机构信息

International University Bremen, School of Engineering and Science, D-28759 Bremen, Germany.

出版信息

J Pept Res. 2004 Apr;63(4):354-64. doi: 10.1111/j.1399-3011.2004.00110.x.

Abstract

Peptide cyclization or chemical cross-linking has frequently been used to restrict the conformational freedom of a peptide, for example, to enhance its capacity for selective binding to a target receptor molecule. Structure prediction of cyclic peptides is important to evaluate possible conformations prior to synthesis. Because of the conformational constraints imposed by cyclization low energy conformations of cyclic peptides can be separated by large energy barriers. In order to improve the conformational search properties of molecular dynamics (MD) simulations a potential scaling method has been designed. The approach consists of several consecutive MD simulations with a specific lowering of dihedral energy barriers and reduced nonbonded interactions between atoms separated by three atoms followed by gradually scaling the potential until the original barriers are reached. Application to four cyclic penta- and hexa-peptide test cases and a protein loop of known structure indicates that the potential scaling method is more efficient and faster in locating low energy conformations than standard MD simulations. Combined with a generalized Born implicit solvation model the low energy cyclic peptide conformations and the loop structure are in good agreement with experiment. Applications in the presence of explicit water molecules during the simulations showed also improved convergence to structures close to experiment compared with regular MD.

摘要

肽环化或化学交联常常被用于限制肽的构象自由度,例如,增强其与靶受体分子选择性结合的能力。环肽的结构预测对于在合成前评估可能的构象很重要。由于环化所施加的构象限制,环肽的低能构象可被较大的能垒分隔开。为了改善分子动力学(MD)模拟的构象搜索特性,设计了一种势能缩放方法。该方法由几个连续的MD模拟组成,通过特定方式降低二面角能垒,并减少被三个原子分隔的原子之间的非键相互作用,随后逐渐缩放势能直至达到原始能垒。应用于四个环五肽和环六肽测试案例以及一个已知结构的蛋白质环表明,与标准MD模拟相比,势能缩放方法在定位低能构象方面更高效、更快。与广义玻恩隐式溶剂化模型相结合,低能环肽构象和环结构与实验结果吻合良好。与常规MD相比,在模拟过程中存在明确水分子的情况下的应用也显示出向接近实验的结构的收敛性得到改善。

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