New Methods for High-Resolution Comparative Modeling

该项目的目标是提高30-90%序列中比较建模的准确性 同一性范围和10-30%范围内。这项工作将由一个由6人组成的多学科小组完成 生物物理学、数学、统计学和计算机科学的研究人员。根据新的统计分析 使用图形模型（Jordan）和非参数贝叶斯分析同源蛋白质结构对 方法（约旦，邓布拉克），托姆帕将设计一个粗略的抽样程序，基于回溯和 分支定界算法，设计用于从起始模型搜索同源结构的空间 由贝克或邓布拉克集团生产。Tseng和Baker将拓展拟牛顿 专门针对蒙特卡罗最小化轨迹的优化方法。这些方法将采取 在轨迹中较早地从邻近的局部优化中获得的信息的优势 景观的区域。对于局部最小化结构的大样本，Jordan将使用响应 表面方法和高斯过程来拟合这些局部最小值的表面。在这个表面上的搜索 然后产生空间中有希望的低能量区域，这些区域可以通过精细采样进一步搜索 方法，包括禁忌搜索（贝克）。使用块坐标下降法的进一步优化 （曾）也将实施。庞德将测试他最近开发的可极化多极力场， 同时发展这个力场进一步与广义玻恩，表面区域溶剂化模型。邓布拉克会 在项目的各个阶段对预测结构的准确性进行基准测试。预测侧链 构象将与沉积的坐标以及电子密度计算进行比较， 实验结构因子（Dunbrack）。最后，本提案中提出的方法将适用于 与癌症发展有关的蛋白质，包括DNA修复、凋亡和细胞生长中的蛋白质 信号传导，优先考虑癌症治疗的靶点。三种蛋白质结构的新结构 倡议中心将被用作预测目标（在它们被解决之前）和模板， 预测具有重要生物学或临床意义的结构。

The goal of this project is to improve the accuracy of comparative modeling both in the 30-90% sequence identity range and in the 10-30% range. This will be accomplished by a multi-disciplinary team of six investigators in biophysics, mathematics, statistics, and computer science. Based on new statistical analysis of homologous protein structure pairs using graphical models (Jordan) and non-parametric Bayesian methods (Jordan, Dunbrack), Tompa will devise a coarse sampling procedure, based on backtracking and branch-and-bound algorithms, designed to search the space of homologous structures from a starting model produced by the Baker or Dunbrack groups. Tseng and Baker will develop extensions of quasi-Newton optimization methods specifically tailored to Monte Carlo Minimization trajectories. These methods will take advantage of information gained in local optimizations carried out earlier in the trajectory from neighboring regions of the landscape. With a large sample of locally minimized structures, Jordan will use response surface methodology and Gaussian processes to fit a surface to these local minima. A search on this surface then produces promising low-energy regions of the space that can be searched further with fine sampling methods, including tabu search (Baker). Further optimizations with block-coordinate descent methods (Tseng) will also be implemented. Ponder will test his recently developed polarizable multi-pole force field, while developing this force field further with a generalized-Born, surface-area solvation model. Dunbrack will benchmark the accuracy of predicted structures at all stages of the project. Predicted side-chain conformations will be compared to deposited coordinates as well as electron density calculations from the experimental structure factors (Dunbrack). Finally, the methods developed in this proposal will be applied to proteins implicated in cancer development, including those in DNA repair, apoptosis, and cell-growth signaling, with a priority on targets for cancer therapeutics. New structures from three Protein Structure Initiative centers will be used both as prediction targets (before they are solved) and as templates for prediction of structures of important biological or clinical interest.