Strategies for Fast Conformational Searching

全局优化在计算分子生物学中占有重要地位。例如，在计算蛋白质折叠和与直接应用于药物设计的对接中，一个主要问题是，当前的构象搜索方法在寻找能量景观上的深层低能态方面不够有效。近年来发展起来的通用凸全局欠估计（CGU）方法提供了动态、灵活的能量格局欠拟合。它的搜索速度不受动力学陷阱的阻碍，在对接中被证明是成功的。基于全原子模型的能量格局，我们提出了一种新的构象搜索策略，该策略将一般的CGU与基于物理的折叠“拉链方法”相结合。我们的搜索方法可以有效地应用于各种各样的能量剖面。我们将在一些小的蛋白质上测试新的搜索策略，看看在全原子模型中是否以及多久能达到自然状态。

Global optimization plays an important role in computational molecular biology. In computational protein folding and docking with direct applications to drug design, for example, a principal problem is that current conformational search methods are not efficient enough at finding the deep low-energy states on the energy landscapes. The recently developed general Convex Global Underestimator (CGU) method provides dynamic and flexible under-fitting of the energy landscape. It is not hindered by kinetic traps in its search speed, and has been proved successful on docking. Based on all-atom model energy landscapes, we propose new conformational search strategies that combine the general CGU with a physics-based "zipper method" for folding. Our search methods can be efficiently applied to a wide variety of energy profiles. We will test the new search strategies on some small proteins to see if and how often the native states can be reached in all-atom models.