Department of Computer Science, Rice University, Houston, Texas, United States of America.
PLoS One. 2013 Jul 23;8(7):e68826. doi: 10.1371/journal.pone.0068826. Print 2013.
Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (sims), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. sims can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that sims is applied to and demonstrate a rapid solution for each. These include the automatic determination of "active" residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems.
蛋白质是许多生物功能的基础，它们通常通过结构的巨大变化来执行复杂的任务。然而，由于该问题在计算上的巨大要求，描述这些构象变化的确切细节仍然是计算生物学的核心挑战。这促使了各种有用的方法的发展，这些方法旨在以不同的空间、时间和能量分辨率回答特定的问题。这些方法主要分为两类：物理上准确但计算要求高的方法和快速、近似的方法。我们在这里引入了一种新的混合建模工具，即结构化直观移动选择器（sims），旨在弥合这两类方法之间的差距，同时允许这两种方法的优势无缝集成到一个单一的框架中。这是通过应用一种现代运动规划算法来实现的，该算法借鉴了机器人领域，同时结合了一个成熟的蛋白质建模库。sims 可以将精确的能量计算与近似或专门的构象采样例程相结合，快速、准确地分析蛋白质系统的大规模构象变异性。我们展示了几个关键的进展，包括通用定义的移动（构象采样方法）的抽象使用和广泛的概率构象探索。我们提出了 sims 应用于三个示例问题，并为每个问题演示了快速解决方案。这些问题包括自动确定基于铰链的系统 Cyanovirin-N 的“活性”残基，探索涉及核糖结合蛋白中非连续残基之间长程协调运动的构象变化，以及快速发现麦芽糖结合蛋白的瞬态构象状态，以前只能通过分子动力学来确定。对于所有情况，我们都使用成熟的能量场提供能量验证，证明了该框架是分析广泛的蛋白质灵活性问题的快速准确工具。
