Strategies for Fast Conformational Searching

全局优化在计算分子生物学中发挥着重要作用。例如，在计算蛋白质折叠和对接药物设计的直接应用中，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次低能态方面不够有效。最近开发的通用凸全局低估(CGU)方法提供了动态和灵活的能源格局的欠拟合。它在搜索速度上没有受到动力学陷阱的阻碍，并在对接上被证明是成功的。基于全原子模型能量景观，我们提出了一种新的构象搜索策略，将通用CGU和基于物理的“拉链方法”相结合进行折叠。我们的搜索方法可以有效地应用于各种能量剖面。我们将在一些小蛋白质上测试新的搜索策略，看看在全原子模型中是否可以达到自然状态以及达到的频率有多高。

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.