BACKGROUND Many proteins tune their biological function by transitioning between different functional states, effectively acting as dynamic molecular machines. Detailed structural characterization of transition trajectories is central to understanding the relationship between protein dynamics and function. Computational approaches that build on the Molecular Dynamics framework are in principle able to model transition trajectories at great detail but also at considerable computational cost. Methods that delay consideration of dynamics and focus instead on elucidating energetically-credible conformational paths connecting two functionally-relevant structures provide a complementary approach. Effective sampling-based path planning methods originating in robotics have been recently proposed to produce conformational paths. These methods largely model short peptides or address large proteins by simplifying conformational space. METHODS We propose a robotics-inspired method that connects two given structures of a protein by sampling conformational paths. The method focuses on small- to medium-size proteins, efficiently modeling structural deformations through the use of the molecular fragment replacement technique. In particular, the method grows a tree in conformational space rooted at the start structure, steering the tree to a goal region defined around the goal structure. We investigate various bias schemes over a progress coordinate for balance between coverage of conformational space and progress towards the goal. A geometric projection layer promotes path diversity. A reactive temperature scheme allows sampling of rare paths that cross energy barriers. RESULTS AND CONCLUSIONS Experiments are conducted on small- to medium-size proteins of length up to 214 amino acids and with multiple known functionally-relevant states, some of which are more than 13Å apart of each-other. Analysis reveals that the method effectively obtains conformational paths connecting structural states that are significantly different. A detailed analysis on the depth and breadth of the tree suggests that a soft global bias over the progress coordinate enhances sampling and results in higher path diversity. The explicit geometric projection layer that biases the exploration away from over-sampled regions further increases coverage, often improving proximity to the goal by forcing the exploration to find new paths. The reactive temperature scheme is shown effective in increasing path diversity, particularly in difficult structural transitions with known high-energy barriers.

中文翻译：

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用机器人启发的方法阐明蛋白质系统中功能相关转变的集合。

背景许多蛋白质通过在不同功能状态之间转换来调节其生物学功能，有效地充当动态分子机器。过渡轨迹的详细结构表征对于理解蛋白质动力学与功能之间的关系至关重要。建立在分子动力学框架上的计算方法原则上能够非常详细地对过渡轨迹进行建模，但计算成本也很高。延迟考虑动力学并专注于阐明连接两个功能相关结构的能量可靠构象路径的方法提供了一种补充方法。最近提出了源自机器人技术的有效基于采样的路径规划方法来产生构象路径。这些方法主要通过简化构象空间来模拟短肽或处理大蛋白质。方法我们提出了一种受机器人启发的方法，该方法通过对构象路径进行采样来连接蛋白质的两个给定结构。该方法侧重于中小型蛋白质，通过使用分子片段替换技术有效地模拟结构变形。特别地，该方法在以起始结构为根的构象空间中生长一棵树，将树引导到围绕目标结构定义的目标区域。我们研究了进度坐标上的各种偏差方案，以在构象空间的覆盖范围和实现目标的进展之间取得平衡。几何投影层促进了路径多样性。反应温度方案允许对跨越能垒的稀有路径进行采样。结果与结论 实验是针对长度长达 214 个氨基酸并具有多种已知功能相关状态的中小型蛋白质进行的，其中一些蛋白质之间的距离超过 13Å。分析表明，该方法有效地获得了连接显着不同结构状态的构象路径。对树的深度和广度的详细分析表明，对进度坐标的软全局偏差增强了采样并导致更高的路径多样性。使探索远离过采样区域的显式几何投影层进一步增加了覆盖范围，通常通过强制探索寻找新路径来提高与目标的接近度。反应温度方案显示出在增加路径多样性方面有效，