Research Starter Grant: New Physics-Based Approaches to Predictive Protein Modeling, with Applications to Cellular Regulation

研究启动资金：基于物理的新预测蛋白质建模方法及其在细胞调节中的应用

• 批准号: 0302445
• 负责人: Matthew Jacobson
• 金额: $ 5万
• 依托单位: University of California-San Francisco
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-02-01 至 2005-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0302445&HistoricalAwards=false
• 关键词: Research; Starter; Grant; New; Physics

The objective of this project is to develop new computational methods for protein structure prediction that are based on all-atom physics based models, with the specific goal of providing new computational tools to investigate the structural mechanisms of cellular regulation. All though there are numerous examples of all-atom physical chemical models that are being used to elucidate the function of specific proteins, such models currently play a very limited role in predictive modeling. From the standpoint of algorithmic development, the central emphasis is on the implementation of new sampling methods, which complement molecular dynamics, for efficiently exploring the energetic landscape of a protein starting from a suitable initial guess (for example, homology model), and on the validation and refinement of energetic models for protein structure prediction. Specific applications include homology modeling of kinases, the prediction of structural/dynamical changes due to phosphorylation, and prediction of the packing arrangements of multi-domain proteins.

该项目的目标是开发基于全原子物理模型的蛋白质结构预测的新计算方法，具体目标是提供新的计算工具来研究细胞调节的结构机制。尽管有许多全原子物理化学模型的例子被用来阐明特定蛋白质的功能，但这些模型目前在预测建模中发挥的作用非常有限。从算法开发的角度来看，中心重点是实现新的采样方法，它补充了分子动力学，从合适的初始猜测（例如，同源模型）开始有效地探索蛋白质的能量景观，以及验证和改进用于蛋白质结构预测的能量模型。具体应用包括激酶的同源性建模，由于磷酸化导致的结构/动态变化的预测，以及多结构域蛋白的包装排列的预测。