U.S.-France Cooperative Research: The Multiple-Minima Problem in Molecular Recognition

美法合作研究：分子识别中的多重极小问题

• 批准号: 9115638
• 负责人: Harold Scheraga
• 金额: $ 1.4万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-05-01 至 1996-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9115638&HistoricalAwards=false
• 关键词: U.S.; France; Cooperative; Research; Multiple

This three-year award supports cooperative research in biophysics between Harold Scheraga of Cornell University and Bernard Maigret at the Louis Pasteur University in Strasbourg, France. The cooperation deals with the theoretical and computational aspects of protein structure with particular emphasis on protein conformation and folding, the so-called multiple-minima problem. The objective of their research is to develop methods for computing the stable comformations of proteins and protein complexes from their amino acid sequences utilizing enhanced x-ray diffraction and Nuclear Magnetic Resonance (NMR). Methodology based on exposed surface areas of proteins will be used to screen out undesirable conformations. Dr. Scheraga and his research group bring to the collaboration expertise in NMR, energy minimization and Monte Carlo computations. This is complemented by French expertise in neural networks, surface area and Monte Carlo computations. Their ultimate goal is to resolve the multiple- minima problem and develop techniques for prediction of protein structure. Knowledge of the protein structure is central to the fundamental understanding of biological processes. The ability to predict structure will advance our understanding of protein behavior and the principles that determine these structures. In the long term, a computational model of protein structure will make it possible to utilize protein engineering in designing new enzymes, and possibly new drugs.

这个为期三年的奖项支持康奈尔大学的Harold Scheraga和法国斯特拉斯堡Louis Pasteur大学的Bernard Maigret在生物物理学方面的合作研究。合作涉及蛋白质结构的理论和计算方面，特别强调蛋白质的构象和折叠，即所谓的多重极小问题。他们的研究目标是开发利用增强型X射线衍射和核磁共振(NMR)从蛋白质和蛋白质复合体的氨基酸序列计算其稳定构象的方法。基于蛋白质暴露表面积的方法将被用来筛选出不需要的构象。Scheraga博士和他的研究小组将核磁共振、能量最小化和蒙特卡洛计算方面的专业知识带到了合作中。此外，法国在神经网络、表面积和蒙特卡洛计算方面的专业知识也起到了补充作用。他们的最终目标是解决多重极小值问题，并开发预测蛋白质结构的技术。对蛋白质结构的了解是对生物过程的基本理解的核心。预测结构的能力将促进我们对蛋白质行为和决定这些结构的原理的理解。从长远来看，蛋白质结构的计算模型将使利用蛋白质工程设计新的酶，甚至可能设计新的药物成为可能。