STATISTICAL MECHANICS OF PROTEIN FOLDING

对于蛋白质折叠的统计机制，已经提出了许多理论，通常是从结构较少的系统的理论中衍生出来的，例如随机杂聚合物、扩散-成核、随机能量或自旋玻璃。虽然它们确实捕捉了真实蛋白质物理化学的一些重要特征，例如折叠的协同性和从随机卷曲状态快速折叠，但它们的推导需要对多肽的平均行为做出一些广泛的假设。事实上，具有生物相关性的蛋白质由明显非常罕见的中等长度的氨基酸序列组成，这些氨基酸序列允许链快速折叠成独特的复杂天然构象，这在很大程度上取决于序列。这表明，专注于长链杂化聚合物的平均性质的理论可能过于概括，而忽略了稀有序列折叠成稀有构象的重要特征。另一方面，自然和计算机都没有足够的时间来详尽地探索所有的构象和所有的序列，即使是小的蛋白质。这里的想法是通过减少链长、每个残基的构象状态数和氨基酸类型的选择，直到可以穷尽地列举所有序列和所有构象，来简单地表示多肽。通过在计算上可行的范围内改变这些参数，可以检测到一般结论并将其外推到与真实蛋白质相对应的参数值。由于这个模型与大多数理论有很大的不同，它能够检验他们关于蛋白质折叠的假设和结论，例如描述向自然态发展的能量景观和序参数的性质。需要解决的问题包括：是否有一个通用的方法来描述所有蛋白质的折叠，或者一些蛋白质是通过一条可识别的途径进行的，而另一些蛋白质则有无数条路线？这个模型能否重现和解释目前关于某些特定蛋白质的折叠机制和中间体的实验结果。

Many theories have been proposed for the statistical mechanics of protein folding, typically derived from theories for less structured systems, such as random heteropolymers, diffusion-nucleation, random energies, or spin glasses. While these have certainly captured some important features of the physical chemistry of real proteins, such as cooperativity of folding and rapid folding from the random coil state, their derivation requires making some broad assumptions about the average behavior of polypeptides. In fact, the proteins of biological relevance consists of apparently very rare, moderately long amino acid sequences that permit the chain to fold rapidly- to a unique complicated native conformation, which depends greatly on the sequence. This suggests that theories focussing on average properties of long-chain heteropolymers may be over-generalizing and neglecting the important features of rare sequences folding to rare conformations. On the other hand, neither nature nor computer has sufficient time to exhaustively explore all conformations and all sequences for even small proteins. The idea here is to simply an otherwise realistic representation of polypeptides by reducing chain length, number of conformation states per residue, and choices of amino acid types until all sequences and all conformations can be exhaustively enumerated. By varying these parameters in the computationally feasible range, general conclusions can be detected and extrapolated to parameter values corresponding to real proteins. Since this model is so different from most theories, it is able to test their assumptions and conclusions about protein folding, such as the nature of the energy landscape and order parameters to describe the progress toward the native state. Questions to be addressed include: is there a general way to describe the folding of all proteins, or do some proceed by a recognizable pathway while others have innumerable routes? Can this model reproduce and explain the currently available experimental results on folding mechanisms and intermediates for certain particular proteins.