SIMULATION OF PROTEIN UNFOLDING AND FOLDING

该项目的长期目标是通过延伸折叠小蛋白质， 莱维特博士在过去30年里开发的模拟方法 年 他还计划利用已知蛋白质结构的信息。 拟议的研究涉及各种不同的理论和 与一位实验主义者合作的计算方法， 他和她有长期的关系。 五个具体目标是 通过肯定地回答这些问题来实现：1。能否 在水中的小的非极性溶质的聚集被量化， 疏水自由能及其对接触面积的依赖性？ 他将 使用分子动力学模拟不同浓度的非极性 分子在水中，并确定集群形成的能量。 2. b-发夹和a-螺旋折叠单元可以展开和重折叠吗？ 他的合作者伊顿博士最近的研究表明， 蛋白G的b-发夹在微秒时间尺度上重折叠。 他将 使用他非常有效的方法模拟这一点，其中包括显式 沃茨。 3.我们能否制造出非天然的蛋白质诱饵结构， 它们看起来像是真实的天然结构 博士 莱维特将借鉴他的工作与晶格和非晶格模型，侧- 链建模和能源优化， 小蛋白质序列的全原子结构。 4. CAN能源 正确地从选定的蛋白质结构集合中导出功能 区分接近天然的结构和诱饵 他将优化这些 通过在现有诱饵上进行测试， 设置，然后改进诱饵生成，使歧视更多 难 5.能不能只用氨基折叠一些小的蛋白质 酸性序列 他将联合收割机预测的二级结构与 诱饵生成和基于知识的潜力， 在实验解决之前预测折叠结构。

The long-term aim of this project is to fold small proteins by extending the methods for simulation that Dr. Levitt developed over the past 30 years. He also plans to use information on known protein structures. The proposed research involves a variety of different theoretical and computational methods in a collaboration with an experimentalist with whom he has had a longstanding relationship. Five specific aims are to be achieved by answering these questions in the affirmative: 1. Can the aggregation of small nonpolar solutes in water be quantified in terms of hydrophobic free-energy and its dependence on contact area? He will use molecular dynamics to simulate different concentrations of nonpolar molecules in water and determine the energetics of cluster formation. 2. Can b-hairpin and a-helix folding units be unfolded and refolded? Recent studies by his collaborator, Dr. Eaton, show that the C-terminal b-hairpin of protein G refolds on a microsecond time-scale. He will simulate this using his very efficient methods, which include explicit waters. 3. Can one generate nonnative decoy structures of proteins, which are so well-packed that they look like real native structure? Dr. Levitt will draw on his work with lattice and off-lattice models, side- chain modeling and energy refinement to generate tens of thousands of all-atom structures for small protein sequences. 4. Can energy functions derived from selected sets of protein structures correctly distinguish near-native structures from decoys? He will optimize these knowledge-based energy functions by testing them on his existing decoy sets and then improving decoy generation to make discrimination more difficult. 5. Can one fold some small proteins using just the amino acid sequence? He will combine predicted secondary structures with decoy generation and knowledge-based potentials in attempts to correctly predict folded structures before they are solved experimentally.