Al-Bluwi Ibrahim, Vaisset Marc, Siméon Thierry, Cortés Juan
BMC Struct Biol. 2013;13 Suppl 1(Suppl 1):S2. doi: 10.1186/1472-6807-13-S1-S2. Epub 2013 Nov 8.
Obtaining atomic-scale information about large-amplitude conformational transitions in proteins is a challenging problem for both experimental and computational methods. Such information is, however, important for understanding the mechanisms of interaction of many proteins.
This paper presents a computationally efficient approach, combining methods originating from robotics and computational biophysics, to model protein conformational transitions. The ability of normal mode analysis to predict directions of collective, large-amplitude motions is applied to bias the conformational exploration performed by a motion planning algorithm. To reduce the dimension of the problem, normal modes are computed for a coarse-grained elastic network model built on short fragments of three residues. Nevertheless, the validity of intermediate conformations is checked using the all-atom model, which is accurately reconstructed from the coarse-grained one using closed-form inverse kinematics.
Tests on a set of ten proteins demonstrate the ability of the method to model conformational transitions of proteins within a few hours of computing time on a single processor. These results also show that the computing time scales linearly with the protein size, independently of the protein topology. Further experiments on adenylate kinase show that main features of the transition between the open and closed conformations of this protein are well captured in the computed path.
The proposed method enables the simulation of large-amplitude conformational transitions in proteins using very few computational resources. The resulting paths are a first approximation that can directly provide important information on the molecular mechanisms involved in the conformational transition. This approximation can be subsequently refined and analyzed using state-of-the-art energy models and molecular modeling methods.
获取蛋白质中大幅度构象转变的原子尺度信息，对于实验方法和计算方法而言都是一个具有挑战性的问题。然而，此类信息对于理解许多蛋白质的相互作用机制非常重要。
本文提出了一种计算效率高的方法，将源自机器人技术和计算生物物理学的方法相结合，以模拟蛋白质的构象转变。应用正常模式分析预测集体大幅度运动方向的能力，来偏向由运动规划算法执行的构象探索。为了降低问题的维度，针对基于三个残基的短片段构建的粗粒度弹性网络模型计算正常模式。不过，使用全原子模型检查中间构象的有效性，该全原子模型是使用闭式逆运动学从粗粒度模型精确重建的。
对一组十种蛋白质的测试表明，该方法能够在单处理器上几个小时的计算时间内模拟蛋白质的构象转变。这些结果还表明，计算时间与蛋白质大小呈线性比例关系，与蛋白质拓扑结构无关。对腺苷酸激酶的进一步实验表明，该蛋白质开放和关闭构象之间转变的主要特征在计算路径中得到了很好的捕捉。
所提出的方法能够使用极少的计算资源模拟蛋白质中的大幅度构象转变。所得路径是一种初步近似，可直接提供有关构象转变所涉及分子机制的重要信息。随后可以使用最先进的能量模型和分子建模方法对这种近似进行细化和分析。
