Self-adaptive multiscaling algorithm for efficient simulations of many-protein systems in crowded conditions.
Self-adaptive multiscaling algorithm for efficient simulations of many-protein systems in crowded conditions.
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DOI：
10.1039/c8cp05517c
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发表时间：
2018-11-21
期刊：
影响因子：
--
通讯作者：
Hassan SA
中科院分区：
文献类型：
--
作者：
Hassan SA
描述了一种在拥挤环境中有效模拟多蛋白质系统的方法。它基于一种自适应的、可逆的结构粗化算法，该算法保留了蛋白质在不同尺度上的相关物理特征。水被隐含地处理，而水溶液的所有其他成分，如离子、共溶体或渗透分子，被原子细节处理。重点放在溶剂模型对不同分子分辨率水平的分析适应上，这允许在尺度之间进行连续的、即时的转换。这允许在动态过程中进行力的分析计算，并在蒙特卡罗模拟中保持详细的平衡。在包含数百种蛋白质的系统中，可以在不切断远程相互作用的情况下实现显著的计算加速。该方法可以与前面描述的自适应构型偏差采样技术相结合，旨在检测强、弱或超弱的蛋白质关联，并被证明提高了采样效率和收敛。该实现旨在模拟多聚体络合、聚集或自组装的早期阶段。该方法可以作为更通用的算法的基础，以识别蛋白质相互作用网络中的顶点、边和中心，或预测信号转导通路中的关键步骤。
A method is described for the efficient simulation of multiprotein systems in crowded environments. It is based on an adaptive, reversible structural coarsening algorithm that preserves relevant physical features of the proteins across scales. Water is treated implicitly whereas all the other components of the aqueous solution, such as ions, cosolutes, or osmolytes, are treated in atomic detail. The focus is on the analytical adaptation of the solvent model to different levels of molecular resolutions, which allows continuous, on-the-fly transitions between scales. This permits the analytical calculation of forces during dynamics and preserves detailed balance in Monte Carlo simulations. A major computational speedup can be achieved in systems containing hundreds of proteins without cutting off the long-range interactions. The method can be combined with a self-adaptive configurational-bias sampling technique described previously, designed to detect strong, weak, or ultra-weak protein associations and shown to improve sampling efficiency and convergence. The implementation aims to simulate early stages of multimeric complexation, aggregation, or self-assembly. The method can be adopted as the basis for a more general algorithm to identify vertices, edges, and hubs in protein interaction networks or to predict critical steps in signal transduction pathways.
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影响因子：
3
作者：
Cardone, Antonio;Bornstein, Aaron;Pant, Harish C.;Brady, Mary;Sriram, Ram;Hassan, Sergio A.
通讯作者：
Hassan, Sergio A.
影响因子：
3.5
作者：
Cardone, A.;Brady, M.;Sriram, R.;Pant, H. C.;Hassan, S. A.
通讯作者：
Hassan, S. A.
DOI：
10.1038/nrm3918
发表时间：
2015-02
期刊：
Nature reviews. Molecular cell biology
影响因子：
--
作者：
通讯作者：
--
影响因子：
2.8
作者：
Bruge, F;Fornili, SL;Palma, MU
通讯作者：
Palma, MU
影响因子：
3.3
作者：
Hassan, SA;Guarnieri, F;Mehler, EL
通讯作者：
Mehler, EL