ENERGY DISTRIBUTION OF THE COMPACT STATES OF A PEPTIDE

如何确定编码蛋白质的三维结构 由一个初级多肽序列被认为是最重要的 生物物理学中的问题。对此问题的一般建议解决方案 涉及蛋白质的简化结构模型、哈密顿量和 一种用于搜索最低能量状态的动态算法 模特。也许最简单的哈密顿量是所谓的随机 能量模型(REM)。虽然REM可以提供一些见解，但它不能 允许人们检查特定的天然折叠，就像在真实蛋白质中看到的那样。 为此，我们选择研究更复杂的模型 其中哈密顿量是具有特定相互作用的能量矩阵 在残基之间，我们允许接触者具有依赖关系 自然会出现，因为有些会涉及相同的残留物。我们的研究 迄今已取得了以下进展：一)能谱 在实际合同数量的极限下表现为高斯分布 正如在蛋白质中看到的那样。二)我们找到了一种通用的统计方法 计算唯一的本征状态的概率时 致密构象的能量分布 被抽样是已知的。Iii)我们开发了一种基于随机的方法 带着障碍物行走，让我们准确地描绘出 具有能量的模型分子的能量密度的极尾 由能量矩阵确定。我们已经用这种方法研究了 蛋白质不同结构特征中的信息量 如二级结构、三级接触和溶剂可及性。 在另一项研究中，我们将统计预测方法提供给 估计随机产生的特定能量的概率 行走将对应于一种本地(占主导地位的博尔兹曼概率)状态。 对应于天然状态序列和结构对为 然后用来测试从触点恢复的能量矩阵 数据。我们期望通过这种方式能够检查这种方法的有效性 统计恢复方法。

How to determine the three dimensional structures of the protein coded by a primary peptide sequence is considered one of the most important problems in biophysics. Suggested solutions to this problem generally involve a simplified structural model for proteins, a Hamiltonian, and a dynamic algorithm with which to search for the lowest energy state of the model. Perhaps the simplest Hamiltonian is the so-called random energy model (REM). While the REM can provide some insights it does not permit one to examine specific native folding as seen in real proteins. For this reason we have chosen to study the more complicated model in which the Hamiltonian is an energy matrix with specific interactions between residues and we allow contacts to have the dependencies that naturally arise because some will involve the same residue. Our studies have thus far led to the following developments: i) The energy spectrum is shown to be Gaussian in the limit of realistic numbers of contracts as seen in proteins. ii) We have found a general statistical method of computing the probability of a unique native state when the energy distribution from which the energies of the compact conformations are to be sampled is known. iii) We have developed a method based on random walks with barriers which allow us to give an accurate picture of the extreme tails of the energy density for model molecules with energies determined by an energy matrix. We have used the method to study the amount of information in different structural features of proteins such as secondary structure, tertiary contacts, and solvent accessibility. In another study we are supplying our statistical prediction methods to estimate the probability that a particular energy generated by the random walk will correspond to a native (dominant Boltzman probability) state. The sequence and structure pairs that correspond to native states are then used to test the recovery of the energy matrix from the contact data. We expect in this way to be able to examine the validity of such statistical recover methods.