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本文引用的文献

1
Conformational Statistics of Semi-Flexible Macromolecular Chains with Internal Joints.具有内部关节的半柔性大分子链的构象统计
Macromolecules. 2006 Feb 11;39(5):1950-1960. doi: 10.1021/ma0512556.
2
Group theory and biomolecular conformation: I. Mathematical and computational models.群论与生物分子构象:I. 数学与计算模型。
J Phys Condens Matter. 2010 Aug 18;22(32):323103. doi: 10.1088/0953-8984/22/32/323103.
3
Computing the conformational entropy for RNA folds.计算 RNA 折叠的构象熵。
J Chem Phys. 2010 Jun 21;132(23):235104. doi: 10.1063/1.3447385.
4
Nonparametric Second-Order Theory of Error Propagation on Motion Groups.运动群上误差传播的非参数二阶理论
Int J Rob Res. 2008;27(11-12):1258-1273. doi: 10.1177/0278364908097583.
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A Unified Approach to Conformational Statistics of Classical Polymer and Polypeptide Models.经典聚合物和多肽模型构象统计的统一方法。
Polymer (Guildf). 2005 Nov 28;46(25):11904. doi: 10.1016/j.polymer.2005.09.012.
6
The role of dynamic conformational ensembles in biomolecular recognition.动态构象集合在生物分子识别中的作用。
Nat Chem Biol. 2009 Nov;5(11):789-96. doi: 10.1038/nchembio.232.
7
Position and Orientation Distributions for Locally Self-Avoiding Walks in the Presence of Obstacles.存在障碍物时局部自回避行走的位置和方向分布
Polymer (Guildf). 2008 Mar 17;49(6):1701-1715. doi: 10.1016/j.polymer.2008.01.056.
8
Discrete state model and accurate estimation of loop entropy of RNA secondary structures.离散状态模型与RNA二级结构环熵的精确估计
J Chem Phys. 2008 Mar 28;128(12):125107. doi: 10.1063/1.2895050.
9
Conformational entropy in molecular recognition by proteins.蛋白质分子识别中的构象熵。
Nature. 2007 Jul 19;448(7151):325-9. doi: 10.1038/nature05959.
10
Modeling protein conformational ensembles: from missing loops to equilibrium fluctuations.蛋白质构象集合体建模:从缺失环到平衡涨落
Proteins. 2006 Oct 1;65(1):164-79. doi: 10.1002/prot.21060.

模拟环熵。

Modeling loop entropy.

作者信息

Chirikjian Gregory S

机构信息

Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.

出版信息

Methods Enzymol. 2011;487:99-132. doi: 10.1016/B978-0-12-381270-4.00004-4.

DOI:10.1016/B978-0-12-381270-4.00004-4
PMID:21187223
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3035855/
Abstract

Proteins fold from a highly disordered state into a highly ordered one. Traditionally, the folding problem has been stated as one of predicting "the" tertiary structure from sequential information. However, new evidence suggests that the ensemble of unfolded forms may not be as disordered as once believed, and that the native form of many proteins may not be described by a single conformation, but rather an ensemble of its own. Quantifying the relative disorder in the folded and unfolded ensembles as an entropy difference may therefore shed light on the folding process. One issue that clouds discussions of "entropy" is that many different kinds of entropy can be defined: entropy associated with overall translational and rotational Brownian motion, configurational entropy, vibrational entropy, conformational entropy computed in internal or Cartesian coordinates (which can even be different from each other), conformational entropy computed on a lattice, each of the above with different solvation and solvent models, thermodynamic entropy measured experimentally, etc. The focus of this work is the conformational entropy of coil/loop regions in proteins. New mathematical modeling tools for the approximation of changes in conformational entropy during transition from unfolded to folded ensembles are introduced. In particular, models for computing lower and upper bounds on entropy for polymer models of polypeptide coils both with and without end constraints are presented. The methods reviewed here include kinematics (the mathematics of rigid-body motions), classical statistical mechanics, and information theory.

摘要

蛋白质从高度无序的状态折叠成高度有序的状态。传统上,折叠问题被表述为根据序列信息预测“唯一的”三级结构。然而,新的证据表明,未折叠形式的集合可能不像曾经认为的那样无序,而且许多蛋白质的天然形式可能不是由单一构象描述的,而是由其自身的构象集合描述的。因此,将折叠和未折叠集合中的相对无序量化为熵差可能有助于揭示折叠过程。一个使关于“熵”的讨论变得模糊的问题是,可以定义许多不同种类的熵:与整体平移和旋转布朗运动相关的熵、构型熵、振动熵、在内部或笛卡尔坐标中计算的构象熵(甚至可能彼此不同)、在晶格上计算的构象熵,上述每种都有不同的溶剂化和溶剂模型、实验测量的热力学熵等等。这项工作的重点是蛋白质中卷曲/环区域的构象熵。引入了新的数学建模工具,用于近似从未折叠到折叠集合转变过程中构象熵的变化。特别地,给出了计算有无末端约束的多肽卷曲聚合物模型熵的下限和上限的模型。这里回顾的方法包括运动学(刚体运动的数学)、经典统计力学和信息论。