Computational Prediction of Protein-Protein Docking

该提案将资助华盛顿大学生物化学系大卫贝克教授实验室的博士后研究员杰弗里·J·格雷的培训。格雷会是个博士2000年8月成为化学工程师，他是科学计算和胶体科学（包括自组装过程和静电学）方面的专家。培训期间的目标是使格雷成为基因组学和蛋白质生物物理学方面的专家。格雷的最终职业目标是领导一个独立的研究小组，在生物学和工程学的界面上进行基础研究。除了基因组学和蛋白质的兴趣，格雷的长期研究兴趣包括生物材料，药物设计和纳米技术。格雷的培训将包括课程，在大卫贝克和斯坦菲尔兹实验室的实验室工作，以及功能蛋白质基因组学的研究项目。贝克的实验室是蛋白质表征，结构测定和计算结构预测的领导者之一。斯坦·菲尔兹是实验性蛋白质基因组学的领导者之一，他是双杂交技术的先驱。该研究项目的目标是开发一种工具来预测给定分离蛋白质结构的蛋白质-蛋白质复合物的结构。这一工具将是有用的快速和廉价的蛋白质相互作用的基因组水平的研究。从长远来看，这将有助于将遗传数据映射到分子水平的功能。此外，这项工作必将提高对分子势、溶剂化现象和大分子热力学的理解。.除了这些基本的科学问题外，蛋白质-蛋白质相互作用的知识可能对蛋白质相关疾病和药物设计产生重大影响。该项目将借鉴格雷在静电学方面的专业知识，蛋白质静电学将使用有效和准确的广义玻恩模型进行建模。对于工具验证，Gray将首先搜索正确的对接结构，从扰动的结合结构或对接配置中的单个蛋白质结构开始。随后，该工具将被推广到包括侧链和主链的灵活性，以能够从未对接的蛋白质结构开始建模对接。最后，将进行基因组水平的分析，以补充蛋白质-蛋白质相互作用的实验性双杂交研究。

This proposal will fund the training of post-doctoral researcher Jeffrey J. Gray in the laboratory of Professor David Baker in the Department of Biochemistry at the University of Washington. Gray will be a Ph.D. chemical engineer in August of 2000, and he is an expert in scientific computation and colloid science including self-assembly processes and electrostatics. The goal of the training period is to make Gray an expert in genomics and protein biophysics. Gray's ultimate career goal is to lead an independent research group which will conduct fundamental research at the interface of biology and engineering. In addition to genomics and protein interests, Gray's long-term research interests include biomaterials, drug design, and nanotechnology. Gray's training will consist of courses, lab work in David Baker and Stan Fields' laboratories, and a research project in functional protein genomics. Baker's laboratory is one of the leaders in protein characterization, structure determination, and computational structure prediction. Stan Fields is one of the leaders in experimental protein genomics, having pioneered the two-hybrid technique. The goal of the research project is to develop a tool to predict the structure of protein-protein complexes given structures of the isolated proteins. This tool will be useful for rapid and inexpensive genomic level studies of protein interactions. In the long term, this will be useful in mapping genetic data to molecular level function. In addition, the work will necessarily improve understanding of molecular potentials, solvation phenomena, and macromolecular thermodynamics. . Besides these fundamental scientific issues, the knowledge of protein-protein interactions developed may have significant impact for protein-related diseases and drug design. The project will draw from Gray's expertise in electrostatics, and protein electrostatics will be modeled using the efficient and accurate generalized Born model. For tool validation, Gray will first search for correct docked structures starting with perturbed bound structures or individual protein structures in their docked configurations. Subsequently, the tool will be generalized to include side-chain and main chain flexibility to be able to model docking beginning with undocked protein structures. Finally, genomic level analyses will be made to compliment experimental two-hybrid studies of protein-protein interactions.