Novel Statistical Energy Function and Its Applications to Side-chain Modeling and Fold Recognition

新颖的统计能量函数及其在侧链建模和折叠识别中的应用

• 批准号: 0818353
• 负责人: Jianpeng Ma
• 金额: $ 93.04万
• 依托单位: Baylor College of Medicine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-15 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0818353&HistoricalAwards=false
• 关键词: Novel; Statistical; Energy; Function; Its

Computational methods to predict protein structure from sequence are becoming increasingly important and powerful, particularly in light of structural and functional annotation of genomic data. To improve the performance of computational algorithms, this project has three specific aims: (1) Development of an orientation-dependent statistical potential based on side-chain packing; (2) Development of a fast and accurate method for generating side-chain rotamer conformations; and (3) Development of structure profile for alignment and template identification of remote targets. Currently, the development of effective potential functions for side-chain modeling is still a challenging problem in the field. The new statistical potential function and its applications open a new path to improving the modeling of side-chains. Such a capability is vitally important for high-accuracy refinement of predicted structures, which is the most difficult step in structure prediction for the last few decades. As a faculty member for the last seven years at both Baylor College of Medicine and Rice University, the PI bears enormous educational responsibility and shares high enthusiasm for developing programs to expose underprivileged and minority students to modern computational techniques and tools of structural biology. Being in Houston, one of the largest populations of such students in the United States and having the inter-institutional appointment at both Baylor and Rice are two important factors that have dramatically enabled the PI to fulfill this important duty. The results of this research will be integrated into the extensive and on-going educational outreach activities at both Baylor and Rice and the computer programs will be made publicly available.

从序列预测蛋白质结构的计算方法变得越来越重要和强大，特别是在基因组数据的结构和功能注释方面。为了提高计算算法的性能，该项目有三个具体目标：(1)开发基于侧链堆积的与取向相关的统计势；(2)开发一种快速而准确的侧链旋转异构体构象生成方法；(3)开发用于远程目标的比对和模板识别的结构轮廓。目前，开发有效的势函数用于侧链建模仍然是该领域的一个具有挑战性的问题。新的统计势函数及其应用为改进侧链模型开辟了一条新的途径。这种能力对于预测结构的高精度精化至关重要，这是过去几十年来结构预测中最困难的一步。作为贝勒医学院和莱斯大学过去七年的教员，PI肩负着巨大的教育责任，并热衷于开发项目，让贫困和少数族裔学生接触现代结构生物学的计算技术和工具。身处美国此类学生人数最多的休斯敦，以及贝勒和莱斯的跨院校聘任，这两个重要因素极大地使PI能够履行这一重要职责。这项研究的结果将被纳入贝勒和莱斯正在进行的广泛的教育推广活动中，计算机程序将公开提供。