Thomas Shawna, Song Guang, Amato Nancy M
Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA.
Phys Biol. 2005 Nov 9;2(4):S148-55. doi: 10.1088/1478-3975/2/4/S09.
We investigate a novel approach for studying protein folding that has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs). Our focus is to study issues related to the folding process, such as the formation of secondary and tertiary structures, assuming we know the native fold. A feature of our PRM-based framework is that the large sets of folding pathways in the roadmaps it produces, in just a few hours on a desktop PC, provide global information about the protein's energy landscape. This is an advantage over other simulation methods such as molecular dynamics or Monte Carlo methods which require more computation and produce only a single trajectory in each run. In our initial studies, we obtained encouraging results for several small proteins. In this paper, we investigate more sophisticated techniques for analyzing the folding pathways in our roadmaps. In addition to more formally revalidating our previous results, we present a case study showing that our technique captures known folding differences between the structurally similar proteins G and L.
我们研究了一种源自机器人运动规划技术（称为概率路图法，PRMs）的新型蛋白质折叠研究方法。我们的重点是研究与折叠过程相关的问题，比如二级和三级结构的形成，前提是我们已知天然折叠结构。我们基于PRM的框架的一个特点是，它在台式电脑上只需几个小时就能生成路图中的大量折叠路径集，从而提供有关蛋白质能量景观的全局信息。这相对于其他模拟方法（如分子动力学或蒙特卡罗方法）具有优势，后者需要更多计算，且每次运行仅产生一条轨迹。在我们的初步研究中，我们对几种小蛋白质取得了令人鼓舞的结果。在本文中，我们研究了更复杂的技术来分析我们路图中的折叠路径。除了更正式地重新验证我们之前的结果外，我们还展示了一个案例研究，表明我们的技术捕捉到了结构相似的蛋白质G和L之间已知的折叠差异。
