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使用 GNEIMO 扭转动力学方法对 CASP 目标蛋白进行结构精修。

Protein structure refinement of CASP target proteins using GNEIMO torsional dynamics method.

机构信息

Division of Immunology, Beckman Research Institute of the City of Hope , 1500, E. Duarte Road, Duarte, California 91010, United States.

出版信息

J Chem Inf Model. 2014 Feb 24;54(2):508-17. doi: 10.1021/ci400484c. Epub 2014 Jan 16.

Abstract

A longstanding challenge in using computational methods for protein structure prediction is the refinement of low-resolution structural models derived from comparative modeling methods into highly accurate atomistic models useful for detailed structural studies. Previously, we have developed and demonstrated the utility of the internal coordinate molecular dynamics (MD) technique, generalized Newton-Euler inverse mass operator (GNEIMO), for refinement of small proteins. Using GNEIMO, the high-frequency degrees of freedom are frozen and the protein is modeled as a collection of rigid clusters connected by torsional hinges. This physical model allows larger integration time steps and focuses the conformational search in the low frequency torsional degrees of freedom. Here, we have applied GNEIMO with temperature replica exchange to refine low-resolution protein models of 30 proteins taken from the continuous assessment of structure prediction (CASP) competition. We have shown that GNEIMO torsional MD method leads to refinement of up to 1.3 Å in the root-mean-square deviation in coordinates for 30 CASP target proteins without using any experimental data as restraints in performing the GNEIMO simulations. This is in contrast with the unconstrained all-atom Cartesian MD method performed under the same conditions, where refinement requires the use of restraints during the simulations.

摘要

使用计算方法进行蛋白质结构预测的一个长期挑战是改进源自比较建模方法的低分辨率结构模型,使其成为用于详细结构研究的高度准确的原子模型。以前,我们已经开发并证明了内部坐标分子动力学 (MD) 技术、广义牛顿-欧拉逆质量算子 (GNEIMO) 在小蛋白 refinement 中的实用性。使用 GNEIMO,可以冻结高频自由度,并将蛋白质建模为通过扭转铰链连接的刚性簇的集合。这种物理模型允许更大的积分时间步长,并将构象搜索集中在低频扭转自由度上。在这里,我们已经将具有温度副本交换的 GNEIMO 应用于从结构预测连续评估 (CASP) 竞赛中提取的 30 种蛋白质的低分辨率蛋白质模型的 refinement。我们已经表明,GNEIMO 扭转 MD 方法导致 30 个 CASP 目标蛋白质的坐标均方根偏差(RMSD)的 refinement 高达 1.3Å,而在执行 GNEIMO 模拟时不使用任何实验数据作为约束。这与在相同条件下执行的无约束全原子笛卡尔 MD 方法形成对比,在该方法中,refinement 需要在模拟过程中使用约束。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d0c/3985798/24738c5b223e/ci-2013-00484c_0002.jpg

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