New Methods for High-Resolution Comparative Modeling

高分辨率比较建模的新方法

• 批准号: 7216862
• 负责人: ROLAND L DUNBRACK
• 金额: $ 67.37万
• 依托单位: RESEARCH INST OF FOX CHASE CAN CTR
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2009-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7216862
• 关键词: Algorithms; Apoptosis; Area; Bayesian Method; Benchmarking; Binding; Biological; Biophysics; Clinical; Collaborations; DNA Repair; Deposition; Development; Dimensions; Disease; Drug Delivery Systems; Elements; Environment; Fox Chase Cancer Center; Goals; Homologous Protein; Human; Jordan; Malignant Neoplasms; Mathematics; Medical; Methodology; Methods; Modeling; Molecular Conformation; Numbers; Persons; Postdoctoral Fellow; Procedures; Process; Property; Protein Structure Initiative; Proteins; Range; Research; Research Personnel; Resolution; Sampling; Scientist; Series; Side; Signal Transduction; Staging; Structure; Students; Surface; Testing; Therapeutic; Universities; Update; Variant; Washington; Work; anticancer research; base; catalyst; cell growth; comparative; computer science; design; electron density; improved; interest; medical schools; novel; protein structure; response; statistics; structural genomics

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：该项目的目标是在30-90%序列同一性范围和10-30%范围内提高比较建模的准确性。这将由一个由生物物理学、数学、统计学和计算机科学领域的六名研究人员组成的多学科团队完成。基于使用图形模型（Jordan）和非参数贝叶斯方法（Jordan, Dunbrack）对同源蛋白结构对的新统计分析，Tompa将设计一个基于回溯和分支定界算法的粗抽样程序，旨在从Baker或Dunbrack组产生的起始模型中搜索同源结构的空间。Tseng和Baker将开发准牛顿优化方法的扩展，专门针对蒙特卡洛最小化轨迹。这些方法将利用早期在景观邻近区域的轨迹中进行的局部优化所获得的信息。有了大量的局部最小结构样本，Jordan将使用响应面方法和高斯过程来拟合这些局部最小值。在这个表面上搜索，然后产生有希望的空间低能区域，可以用精细采样方法进一步搜索，包括禁忌搜索（Baker）。使用块坐标下降方法（Tseng）的进一步优化也将实现。庞德将测试他最近发展的极化多极力场，同时用一个广义的波恩表面积溶剂化模型进一步发展这个力场。Dunbrack将在项目的所有阶段对预测结构的准确性进行基准测试。预测的侧链构象将与沉积坐标以及来自实验结构因子的电子密度计算进行比较（Dunbrack）。最后，本提案中开发的方法将应用于与癌症发展有关的蛋白质，包括DNA修复，细胞凋亡和细胞生长信号，优先考虑癌症治疗的靶点。来自三个蛋白质结构倡议中心的新结构将被用作预测目标（在它们被解决之前）和预测重要生物学或临床兴趣结构的模板。