ITR: Global Optimization in Computational Biology

ITR：计算生物学中的全局优化

• 批准号: 0082146
• 负责人: Ken Dill
• 金额: --
• 依托单位: University of California-San Francisco
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0082146&HistoricalAwards=false
• 关键词: ITR; Global; Optimization; Computational; Biology

This project is a collaboration between members of the global optimization computer science and the computational biology communities to develop new methods for protein structure prediction and ligand docking. If successful, the methods developed here will help solve two important problems in computational biology - the need for fast conformational searching to predict the structures of proteins or complexes of proteins with other proteins and ligands, and the need to improve folding and docking models. Both problems are fundamental to understanding how the molecules of life work. This project will not solve these problems itself, but will develop new computational methods that can contribute to their solution.Technically, there are two specific aims: (1) To develop efficient methods for searching conformational spaces to find globally optimal (native) conformations on energy landscapes. (2) To develop efficient methods for searching parameter spaces to find optimal parameters for the large, complex models that are common in computational biology. This work is based on Underestimator methods that do not search over the tops of energy landscapes like Monte Carlo, Simulated Annealing, and Molecular Dynamics, the current standard methods.

该项目是全球优化计算机科学和计算生物学社区成员之间的合作，旨在开发蛋白质结构预测和配体对接的新方法。如果成功，这里开发的方法将有助于解决计算生物学中的两个重要问题-需要快速构象搜索来预测蛋白质或蛋白质与其他蛋白质和配体的复合物的结构，以及需要改进折叠和对接模型。这两个问题都是理解生命分子如何工作的基础。这个项目本身不会解决这些问题，但将开发新的计算方法，可以帮助他们的解决方案。从技术上讲，有两个具体的目标：（1）开发有效的方法来搜索构象空间，以找到全局最优的（本地）构象的能量景观。(2)开发搜索参数空间的有效方法，为计算生物学中常见的大型复杂模型找到最佳参数。这项工作是基于Underestimator方法，这些方法不会搜索能量景观的顶部，如Monte Carlo，模拟退火和分子动力学，目前的标准方法。