EMPIRICAL ENERGY FUNCTIONS FOR MOLECULAR MODELING

这个项目的目标是开发计算方法，用于 分子模型的评价。评估是基于统计的 三级模型中非局域、非键相互作用的分布 结构。将此分布与 结晶学数据库，使用类似玻尔兹曼的概率定律。我们 将以这种方式导出的评价函数解释为均值势 力，或经验自由能函数。数据的近似有效性 通过分析论证了类玻尔兹曼概率定律 蛋白质晶体结构中的离子对频率。估计静电 势作为原子间距离的函数很好地符合 来自物理原理和实验的期望。我们用过这个 用概率定律推导能量函数，用于计算初始 在同源建模中产生的模型。这些最初的型号分配了一个新的 已知的多肽骨架结构的氨基酸序列 疑似同源，但未指明侧链原子的位置。在……里面 用平均坐标表示氨基酸残基的能量函数 对于多肽和侧链基团，我们将其归类为非键 按残基对类型和距离间隔进行的相互作用。能量用于 通过分析晶体结构数据得出每种类型的触点 基础上，使用适用于分类数据的统计模型。我们有 证明了这个能量函数可以从数百万个模型中识别出正确的模型 不正确的、“折叠错误的”替代方案。基于结构的模型 相关的蛋白质可以提供对生物功能的有价值的见解 只知道序列的蛋白质。现有的计算方法 然而，无法确认它们的正确性。这项工作的重要性在于 在为模型评估提供量化标准方面。

The goal of this project is to develop computational methods for evaluation of molecular models. Evaluation is based on the statistical distribution of non-local, nonbonded interactions in the model tertiary structure. This distribution is compared to that of correct structures in a crystallographic data base, using a Boltzmann-like probability law. We interpret evaluation functions derived in this way as potentials of mean force, or empirical free energy functions. The approximate validity of the Boltzmann-like probability law has been demonstrated by an analysis ion-pair frequency in protein crystal structures. Estimated electrostatic potential as a function of inter-atomic distance agrees well with expectations from physical principle and experiment. We have used this probability law to derive an energy function for evaluation of the initial models produced in homology modeling. These initial models assign a new amino acid sequence to the known polypeptide backbone structure of a suspected homolog, but do not specify locations of side-chain atoms. In the energy function we represent amino acid residues by mean coordinates for peptide and side chain groups, and we categorize nonbonded interactions by residue pair type and distance interval. Energies for contacts of each type are derived by analysis of a crystal structure data base, using a statistical model appropriatefor categorical data. We have shown that this energy function can identify correct models among millions of incorrect, "misfolded" alternatives. Models based on the structures of related proteins can provide valuable insight into the biological function of proteins where only sequence is known. Existing computational methods cannot confirm their correctness, however. The importance of this work is in providing quantitative criteria for model evaluation.