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[交流] 【求助】关于构型熵变的计算

请问哪位高人可以推荐一种计算受体和配体结合后,构型熵变的计算方法,最好有程序.而且计算速度上尽量快.谢谢了

[ Last edited by zdhlover on 2009-12-14 at 14:17 ]

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今天早上和同学讨论构型熵(configuration entropy)和构象熵(conformation entropy)之间的区别，发现构型熵一般都比构象熵要大很多。构型和构象是表征分子结构特征的易混淆的两个概念，因而有必要阐述一下它们之间的区别与联系

一、构型和构象：
分子的化学结构：是指组成分子的原子种类÷数量以及它们之间的键合(化学键)形式。

构型(configuration)：在给定的化学结构下，分子中原子在键合时所形成的不同的空间排列，称为分子的configuration。化学结构相同，而configuration不同的分子成为立体异构体，这样的异构体是稳定的，并且如果它们混合在一起是可以一一分离出来的。

   构象(conformation)：在化学结构和configuration相同的分子中，其原子由于单键的内旋转(internal totation/有的文献有称为torsion)而在空间形成的不同的构象体可以相互转化。内旋转角、键长和键角是表征构象的参数，但是一般地可以认为在内旋转时单键的键长不变而键角的变化也只是在几度的范围。因此，在描写内旋转异构体的能量时，往往视其为只是内旋转角的函数。
构象体可相互转化并在一定温度下达到动态平衡，却无法通过分离得到纯净的成分。
由于分子的conformation源于单键的内旋转，因此如果分子中不存在单键的内旋转也就无分子的构象可言。(这一点就决定了构象统计学所讨论的范围)。一个分子可能有多少构象体或者说有多少中构象，决定于其内部单键的数目。
eg: 丁烷其内部单键数为1 构象数是 3
      戊烷其内部单键数为2 构象数是  3^2
      n个单键的线性聚乙烯分子构象数可能是 3^(n-2)
但由于对称性等原因，实际的构象数不会大于3^(n-2)，但量级上并无大的区别，这些构象出现的几率并不相等，而是遵从一定的统计分布所以高分子键的构象问题的一个重要课题。
   构象体所对应的旋转角的平衡值也可以由振动光谱数据得到.

广义的构象是指分子中原子或基团在三维空间的取向和定位, 狭义的构象是指具有相同构造和构型分子中原子或基团在空间的取向和定位.

二、构型熵(Configuration entropy)和构象熵(Conformational entropy)
Configuration entropy is the entropy associated with the geometric configuration of individual components comprising a distributed physical system. Configuration entropy of a given configuration can be evaluated using an adaptation of the Boltzmann formula of statistical thermodynamics,

where kB is the Boltzmann constant and W probability of this configuration. Probability W is the ratio between the number of possible (spatial) arrangements of system components that can give the current configuration and the total number of possible arrangements yielding all possible configurations of the system. Note that number of configurations is different from the number of arrangements, for instance, because each given configuration (overall geometry pattern of the system) may allow permutations of system components without changing this pattern. Example: permutation of individual monomers in a macromolecule.

In application to macromolecules, configuration entropy is also known as conformational entropy.

It can be shown that the variation of configuration entropy of thermodynamic systems (e.g., ideal gas, and other systems with a vast number of internal degrees of freedom) on the course of thermodynamic processes is equivalent to the variation of the macroscopicentropy defined as dS = δQ/T, δQ amount of heat exchanged by the system with the surrounding media, and T system temperature. In application to thermodynamics systems, the Boltzmann equation shown in above is also known as microscopic definition of entropy.

Conformational entropy is the entropy associated with the physical arrangement of a polymer chain that assumes a compact or globular state in solution. The concept is most commonly applied to biological macromolecules such as proteins and RNA, but can also be used for polysaccharides and other polymeric organic compounds. To calculate the conformational entropy, the possible conformations assumed by the polymer may first be discretized into a finite number of states, usually characterized by unique combinations of certain structural parameters, each of which has been assigned an energy level. In proteins, backbone dihedral angles and side chain rotamers are commonly used as descriptors, and in RNA the base pairing pattern is used. These characteristics are used to define the degrees of freedom (in the statistical mechanics sense of a possible "microstate"

. The conformational entropy associated with a particular conformation is then dependent on the probability associated with the occupancy of that state, as determined by the sum of the energies associated with the value of each parameter assumed in the state.

The entropy of heterogeneous random coil or denatured proteins is significantly higher than that of the folded native state tertiary structure. In particular, the conformational entropy of the amino acid side chains in a protein is thought to be a major contributor to the energetic stabilization of the denatured state and thus a barrier to protein folding. The conformational entropy of RNA and proteins can be estimated; for example, empirical methods to estimate the loss of conformational entropy in a particular side chain on incorporation into a folded protein can roughly predict the effects of particular point mutations in a protein. Side-chain conformational entropies can be defined as Boltzmann sampling over all possible rotameric states:

where R is the gas constant and pi is the probability of a residue being in rotamer i.

The limited conformational range of proline residues lowers the conformational entropy of the denatured state and thus increases the energy difference between the denatured and native states. A correlation has been observed between the thermostability of a protein and its proline residue content.