Theoretical Studies of Protein Folding

蛋白质折叠的理论研究

• 批准号: 0003722
• 负责人: Harold Scheraga
• 金额: $ 55万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-01 至 2006-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0003722&HistoricalAwards=false
• 关键词: Theoretical; Studies; Protein; Folding

Sheraga, Harold A.MCB-0003722 The intellectual goal of this project is not, primarily, to predict protein structure but, rather, to gain an understanding of how inter-residue interactions determine the three-dimensional structure of a globular protein (the protein folding problem). For such an understanding, use is made of an ab initio approach, i.e. one based solely on the global optimization of a potential energy function (including the role of the solvent), without the use of secondary-structure predictions, homology modeling, threading, etc. Such an approach requires both a reliable potential function and an efficient procedure for global optimization; one cannot implement one without the other. Therefore, the specific aims are (i) to improve the potential function, (ii) to improve procedures for global optimization, and (iii) to apply them, first, to globular proteins of known structure and, then, to proteins of unknown structure in the CASP-like blind tests. The potential function is being improved by ab initio calculations plus refinement by the same global optimization procedure that was used for ab initio predictions of crystal structures (an analog of the protein folding problem). The multiple-minima problem (due to the existence of numerous local minima in the multidimensional potential energy surface) is being surmounted by a newly-developed hierarchical method, which incorporates (among other procedures) a united-residue (UNRES) description of the polypeptide chain, a Conformational Space Annealing (CSA) method, and the Monte-Carlo-plus-energy minimization (MCM) method and its descendant [the Conformation-Family Monte Carlo (CFMC) method]; this hierarchical method for global optimization was very successful in the CASP3 blind test, and is being significantly improved for later CASP-like blind tests. With the PI's own PC-Linux cluster, and considerable allocated time on both the NT clusters of the Cornell Theory Center and the supercomputer at the San Diego Supercomputer Center, sufficient access is available to the necessary resources to carry out this project. The proposed theoretical approach will provide a basic understanding of the conformational and folding properties of proteins, and will provide training, not only for the two research associates carrying out this research, but also for several postdocs and graduate students working together with these research associates.

这个项目的智力目标主要不是预测蛋白质结构，而是了解残基之间的相互作用如何决定球状蛋白质的三维结构(蛋白质折叠问题)。为了理解这种理解，使用从头算方法，即仅基于势能函数(包括溶剂的作用)的全局优化的方法，而不使用二级结构预测、同源建模、线程等。这种方法既需要可靠的势函数，也需要有效的全局优化程序；没有其中一个就不能实现其中一个。因此，具体的目标是(I)改进势能函数，(Ii)改进全局优化的过程，以及(Iii)将它们应用于CASP类盲测中的已知结构的球形蛋白质，然后应用于结构未知的蛋白质。势能函数正在通过从头计算以及用于晶体结构从头计算预测(类似于蛋白质折叠问题)的相同全局优化程序进行改进。多极值问题(由于多维势能面上存在大量局部极小)正在被一种新开发的分层方法克服，该方法结合了多肽链的联合残基(UNRES)描述、构象空间退火法(CSA)和蒙特卡罗加能量最小化(MCM)方法及其衍生方法[构象家族蒙特卡罗(CFMC)方法]；这种全局优化的分层方法在CASP3盲测试中非常成功，并在以后的类似CASP的盲测中得到显著改进。由于PI拥有自己的PC-Linux集群，并且在康奈尔理论中心的NT集群和圣地亚哥超级计算机中心的超级计算机上都分配了相当多的时间，因此可以获得足够的必要资源来开展这一项目。建议的理论方法将提供对蛋白质构象和折叠性质的基本理解，并将提供培训，不仅为进行这项研究的两名研究助理，而且为几名博士后和研究生与这些研究助理一起工作。