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一种用于蛋白质展开/折叠的全面多维嵌入式一维反应坐标。

A comprehensive multidimensional-embedded, one-dimensional reaction coordinate for protein unfolding/folding.

机构信息

Department of Bioengineering, University of Washington, Seattle, Washington, USA.

出版信息

Biophys J. 2010 Jun 2;98(11):2671-81. doi: 10.1016/j.bpj.2010.02.048.

Abstract

The goal of the Dynameomics project is to perform, store, and analyze molecular dynamics simulations of representative proteins, of all known globular folds, in their native state and along their unfolding pathways. To analyze unfolding simulations, the location of the protein along the unfolding reaction coordinate (RXN) must be determined. Properties such as the fraction of native contacts and radius of gyration are often used; however, there is an issue regarding degeneracy with these properties, as native and nonnative species can overlap. Here, we used 15 physical properties of the protein to construct a multidimensional-embedded, one-dimensional RXN coordinate that faithfully captures the complex nature of unfolding. The unfolding RXN coordinates for 188 proteins (1534 simulations and 22.9 mus in explicit water) were calculated. Native, transition, intermediate, and denatured states were readily identified with the use of this RXN coordinate. A global native ensemble based on the native-state properties of the 188 proteins was created. This ensemble was shown to be effective for calculating RXN coordinates for folds outside the initial 188 targets. These RXN coordinates enable, high-throughput assignment of conformational states, which represents an important step in comparing protein properties across fold space as well as characterizing the unfolding of individual proteins.

摘要

Dynameomics 项目的目标是对所有已知球状折叠的代表性蛋白质进行分子动力学模拟,以其天然状态和展开途径进行执行、存储和分析。为了分析展开模拟,必须确定蛋白质沿着展开反应坐标 (RXN) 的位置。通常使用蛋白质的部分天然接触和旋转半径等性质;然而,这些性质存在简并性问题,因为天然和非天然物种可能重叠。在这里,我们使用蛋白质的 15 种物理性质来构建多维嵌入的一维 RXN 坐标,该坐标忠实地捕捉展开的复杂性质。计算了 188 种蛋白质(1534 次模拟和 22.9 μs 在显式水中)的展开 RXN 坐标。使用这种 RXN 坐标可以轻松识别天然状态、过渡状态、中间状态和变性状态。基于 188 种蛋白质的天然状态特性创建了一个全局天然集合。该集合被证明可有效用于计算初始 188 个目标之外的折叠的 RXN 坐标。这些 RXN 坐标能够实现构象状态的高通量分配,这是在折叠空间中比较蛋白质性质以及表征单个蛋白质展开的重要步骤。

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