SIMULATION OF PROTEIN UNFOLDING AND FOLDING

这个项目的长期目标是通过延伸来折叠小蛋白。 莱维特博士在过去30年里开发的模拟方法 好几年了。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 与一位实验者合作的计算方法 他有一段长期的恋情。五个具体目标是 通过肯定地回答这些问题来实现：1。 小分子非极性溶质在水中的聚集可用 疏水自由能及其对接触面积的依赖？他会的 用分子动力学模拟不同浓度的非极性 并决定了团簇形成的能量学。 2.b-发夹和a-螺旋折叠单元可以展开和重新折叠吗？ 他的合作者伊顿博士最近的研究表明，C末端 蛋白G的B-发夹在微秒级的时间尺度上进行折叠。他会的 使用他非常有效的方法来模拟这一点，其中包括显式 沃特斯。3.人们能否产生蛋白质的非天然诱饵结构， 它们包装得如此之好，看起来像是真正的本土结构？Dr。 Levitt将利用他对晶格和非晶格模型的工作，Side-Side- 链建模和能量精炼，以生成数万个 小蛋白质序列的全原子结构。4.罐头能源 从选定的蛋白质结构集合中正确推导出的功能 区分接近原生的结构和诱饵吗？他将优化这些 通过在他现有的诱饵上测试基于知识的能量函数 设置，然后改进诱饵生成，使辨别能力更强 很难。5.仅用氨基可以折叠一些小蛋白质吗？ 酸序列？他将把预测的二级结构与 诱饵产生和基于知识的潜力试图正确地 在通过实验解决褶皱结构之前，对它们进行预测。

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.