New Methods for High-Resolution Comparative Modeling

高分辨率比较建模的新方法

• 批准号: 7020915
• 负责人: ROLAND L DUNBRACK
• 金额: $ 70万
• 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/7020915
• 关键词: biochemical evolution; chemical models; computational biology; computer program /software; computer simulation; conformation; data quality /integrity; electron density; mathematical model; mathematics; model design /development; protein sequence; protein structure; protein structure function; sample collection

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）：该项目的目的是提高在30-90％的序列身份范围和10-30％范围内进行比较建模的准确性。这将由一个由六名生物物理学，数学，统计和计算机科学领域的研究人员组成的多学科团队来完成。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将基于基于回溯和分支结合的算法的粗制采样程序，设计为旨在搜索由Bakake或Dunbake dunbake搜索的同源模型的空间。 Tseng和Baker将开发出专门针对Monte Carlo最小化轨迹量身定制的准Newton优化方法的扩展。这些方法将利用景观邻近区域较早进行的局部优化中获得的信息。约旦将使用大量局部最小化结构样本，将使用响应表面方法和高斯工艺来适应这些局部最小值的表面。然后，在该表面上进行搜索会产生有希望的空间的低能区域，可以通过精细的采样方法（包括Tabu Search（Baker））进行进一步搜索。还将实施使用区块坐标下降方法（Tseng）进行进一步的优化。 Ponder将测试他最近开发的可极化的多极力场，同时通过广泛的表面区域溶剂化模型进一步开发该力场。 Dunbrack将基准在项目的所有阶段基准预测结构的准确性。将将预测的侧链构象与沉积的坐标以及实验结构因子（Dunbrack）的电子密度计算进行比较。最后，该提案中开发的方法将应用于与癌症发育有关的蛋白质，包括DNA修复，凋亡和细胞增长信号传导的蛋白质，并优先考虑癌症治疗剂的靶标。来自三个蛋白质结构倡议中心的新结构既将用作预测靶标（在解决之前），也将用作预测重要生物学或临床兴趣结构的模板。