FOLDING A MINI-PROTEIN (TRPCAGE) USING WEIGHTED ENSEMBLE PATH SAMPLING

这个子项目是许多利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 蛋白质如何折叠是一个具有科学意义的问题，因为折叠路径必须覆盖广泛而粗糙的构型景观，并克服大的熵障碍才能达到折叠状态。除了科学上的兴趣，还有直接和实际的原因来了解蛋白质折叠：阿尔茨海默氏症，亨廷顿病和帕金森病等疾病被认为是由蛋白质错误折叠引起的。使用计算机模拟来探测蛋白质折叠的尝试跨越了过去的50年，并且非常具有挑战性。因此，微型蛋白质的计算折叠被用来探索计算模型和方法的准确性和有效性。在这里，我们提出了折叠trpcage迷你蛋白通过加权包围路径采样方法。加权Enhancement方法先前应用于蛋白质Camodulin，以研究两态系统的跃迁。在那里，该方法证明了它的有用性，使易处理的罕见的过渡，需要系统爬上禁止的能量障碍。在这里，我们适应并应用蛋白质折叠的方法，其中蛋白质模型必须克服多种能量障碍才能达到折叠状态。我们比较了这种方法的效率，蛮力折叠通过传统的分子动力学。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Folding a mini-protein (trpcage) via the Weighted Ensemble Path Sampling Method How proteins fold is a matter of scientific interest because the folding pathway must cover a broad and rough configurational landscape, and overcome large entropic barriers to reach the folded state. Besides the scientific interest, there are direct and practical reasons to understand protein folding: Diseases like Alzheimers, Huntingtons and Parkinsons are proposed to be caused by protein mis-folding. Attempts to use computer simulations to probe protein-folding span the past 50 years, and is quite challenging. Therefore computational folding of mini-proteins is used to probe the accuracy and effectiveness of computational models and methods. Here we present folding the trpcage mini-protein via the Weighted Ensemble Path Sampling method. The Weighted Ensemble method was previously applied to the protein Camodulin, to investigate transitions of a two state system. There, the method demonstrated its usefulness to make tractable rare transitions that require the system to climb over prohibitive energy barriers. Here, we adapt and apply the method for protein-folding, where the protein model must overcome a manifold of energy barriers to reach the folded state. We compare the efficiency of this method to brute force folding via conventional molecular dynamics.