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用于研究蛋白质构象变化的粗粒度自由能函数:双阱网络模型

Coarse-grained free energy functions for studying protein conformational changes: a double-well network model.

作者信息

Chu Jhih-Wei, Voth Gregory A

机构信息

Center for Biophysical Modeling and Simulation and Department of Chemistry, University of Utah, Salt Lake City, Utah, USA.

出版信息

Biophys J. 2007 Dec 1;93(11):3860-71. doi: 10.1529/biophysj.107.112060. Epub 2007 Aug 17.

Abstract

In this work, a double-well network model (DWNM) is presented for generating a coarse-grained free energy function that can be used to study the transition between reference conformational states of a protein molecule. Compared to earlier work that uses a single, multidimensional double-well potential to connect two conformational states, the DWNM uses a set of interconnected double-well potentials for this purpose. The DWNM free energy function has multiple intermediate states and saddle points, and is hence a "rough" free energy landscape. In this implementation of the DWNM, the free energy function is reduced to an elastic-network model representation near the two reference states. The effects of free energy function roughness on the reaction pathways of protein conformational change is demonstrated by applying the DWNM to the conformational changes of two protein systems: the coil-to-helix transition of the DB-loop in G-actin and the open-to-closed transition of adenylate kinase. In both systems, the rough free energy function of the DWNM leads to the identification of distinct minimum free energy paths connecting two conformational states. These results indicate that while the elastic-network model captures the low-frequency vibrational motions of a protein, the roughness in the free energy function introduced by the DWNM can be used to characterize the transition mechanism between protein conformations.

摘要

在这项工作中,提出了一种双阱网络模型(DWNM),用于生成粗粒度自由能函数,该函数可用于研究蛋白质分子参考构象状态之间的转变。与早期使用单个多维双阱势连接两个构象状态的工作相比,DWNM为此使用了一组相互连接的双阱势。DWNM自由能函数具有多个中间状态和鞍点,因此是一种“粗糙”的自由能景观。在DWNM的这种实现中,自由能函数在两个参考状态附近简化为弹性网络模型表示。通过将DWNM应用于两个蛋白质系统的构象变化,证明了自由能函数粗糙度对蛋白质构象变化反应途径的影响:G-肌动蛋白中DB环的卷曲到螺旋转变以及腺苷酸激酶的开放到关闭转变。在这两个系统中,DWNM的粗糙自由能函数导致识别出连接两个构象状态的不同最小自由能路径。这些结果表明,虽然弹性网络模型捕获了蛋白质的低频振动运动,但DWNM引入的自由能函数粗糙度可用于表征蛋白质构象之间的转变机制。

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