EMPIRICAL ENERGY FUNCTIONS FOR MOLECULAR MODELING

该项目的目标是开发计算方法， 分子模型的评价。 评估基于统计数据， 模型三级中非局部、非键相互作用的分布 结构 将该分布与以下中的正确结构的分布进行比较： 一个晶体学数据库，使用玻尔兹曼概率定律。 我们 将以这种方式导出的评价函数解释为平均值的势 力或经验自由能函数。的近似有效性。 通过分析证明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与 物理原理和实验的期望。 我们已经用这种 概率法推导出一个能量函数的初始评估 在同源建模中产生的模型。这些初始模型分配了一个新的 氨基酸序列与已知的多肽骨架结构的互补性。 疑似同源物，但未指明侧链原子的位置。 在 能量函数我们用平均坐标表示氨基酸残基 对于肽和侧链基团，我们将非键合 通过残基对类型和距离间隔的相互作用。能的 通过分析晶体结构数据来导出每种类型的接触 base，using使用a statistical统计model模型appropriatefor categorical分类data数据. 我们有 表明该能量函数可以在数百万个模型中识别出正确的模型， 不正确的，“错误折叠”的替代品。模型基于的结构， 相关蛋白质可以提供有价值的洞察生物功能 只有序列是已知的。 现有计算方法 但无法证实其正确性。 这项工作的重要性在于 为模型评估提供定量标准。

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.