一种受机器人启发的方法来采样连接蛋白质系统中已知功能相关结构的构象路径

Kevin Molloy, Amarda Shehu
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引用次数: 7

摘要

描述蛋白质在不同功能状态之间的过渡轨迹是计算生物学中一个重要但具有挑战性的问题。基于分子动力学的方法可以获得最详细和准确的信息,但计算成本很高。为了解决成本问题,采用机器人技术的基于采样的路径规划方法放弃了蛋白质动力学,转而寻求构象路径,并假设可以稍后将动力学纳入路径转换为过渡轨迹。现有的方法侧重于短肽或大蛋白质;对于后者,粗表示简化了搜索空间。在这里,我们提出了一种机器人启发的基于树的方法来采样连接中小型蛋白质的已知结构状态的构象路径。我们使用分子片段替换来处理搜索空间的维度,以有效地获得物理上真实的构象。该方法在给定构象的构象空间中生长一棵树,并对树的生长进行偏置,使其朝着给定的目标构象方向生长。研究了不同偏压方案的效果。对214个氨基酸长度的已知功能相关状态超过13ÅA的蛋白质进行的实验表明,该方法有效地获得了连接显著不同结构状态的构象路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A robotics-inspired method to sample conformational paths connecting known functionally-relevant structures in protein systems
Characterization of transition trajectories that take a protein between different functional states is an important yet challenging problem in computational biology. Approaches based on Molecular Dynamics can obtain the most detailed and accurate information but at considerable computational cost. To address the cost, sampling-based path planning methods adapted from robotics forego protein dynamics and seek instead conformational paths, operating under the assumption that dynamics can be incorporated later to transform paths to transition trajectories. Existing methods focus either on short peptides or large proteins; on the latter, coarse representations simplify the search space. Here we present a robotics-inspired tree-based method to sample conformational paths that connect known structural states of small- to medium- size proteins. We address the dimensionality of the search space using molecular fragment replacement to efficiently obtain physically-realistic conformations. The method grows a tree in conformational space rooted at a given conformation and biases the growth of the tree to steer it to a given goal conformation. Different bias schemes are investigated for their efficacy. Experiments on proteins up to 214 amino acids long with known functionally-relevant states more than 13ÅA apart show that the method effectively obtains conformational paths connecting significantly different structural states.
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