Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York 10461, United States.
J Chem Theory Comput. 2020 Aug 11;16(8):5323-5333. doi: 10.1021/acs.jctc.0c00439. Epub 2020 Jul 29.
Complexes formed among diverse proteins carry out versatile functions in nearly all physiological processes. Association rates which measure how fast proteins form various complexes are of fundamental importance to characterize their functions. The association rates are not only determined by the energetic features at binding interfaces of a protein complex but also influenced by the intrinsic conformational dynamics of each protein in the complex. Unfortunately, how this conformational effect regulates protein association has never been calibrated on a systematic level. To tackle this problem, we developed a multiscale strategy to incorporate the information on protein conformational variations from Langevin dynamic simulations into a kinetic Monte Carlo algorithm of protein-protein association. By systematically testing this approach against a large-scale benchmark set, we found the association of a protein complex with a relatively rigid structure tends to be reduced by its conformational fluctuations. With specific examples, we further show that higher degrees of structural flexibility in various protein complexes can facilitate the searching and formation of intermolecular interactions and thereby accelerate their associations. In general, the integration of conformational dynamics can improve the correlation between experimentally measured association rates and computationally derived association probabilities. Finally, we analyzed the statistical distributions of different secondary structural types on protein-protein binding interfaces and their preference to the change of association rates. Our study, to the best of our knowledge, is the first computational method that systematically estimates the impacts of protein conformational dynamics on protein-protein association. It throws lights on the molecular mechanisms of how protein-protein recognition is kinetically modulated.
在几乎所有生理过程中，由多种蛋白质形成的复合物执行着多样化的功能。衡量蛋白质形成各种复合物的速度的关联速率对于表征其功能至关重要。关联速率不仅取决于蛋白质复合物结合界面的能量特征，还受到复合物中每个蛋白质固有构象动力学的影响。不幸的是，构象效应如何调节蛋白质的缔合从未在系统水平上进行校准。为了解决这个问题，我们开发了一种多尺度策略，将蛋白质构象变化的信息从朗之万动力学模拟纳入到蛋白质-蛋白质缔合的动力学蒙特卡罗算法中。通过系统地将该方法与大规模基准集进行测试，我们发现具有相对刚性结构的蛋白质复合物的缔合往往会受到构象波动的影响。通过具体的例子，我们进一步表明，各种蛋白质复合物中更高程度的结构灵活性可以促进分子间相互作用的搜索和形成，从而加速它们的缔合。总的来说，构象动力学的整合可以提高实验测量的缔合速率与计算得出的缔合概率之间的相关性。最后，我们分析了蛋白质-蛋白质结合界面上不同二级结构类型的统计分布及其对缔合速率变化的偏好。据我们所知，我们的研究是第一个系统地估计蛋白质构象动力学对蛋白质-蛋白质缔合影响的计算方法。它揭示了蛋白质-蛋白质识别如何在动力学上被调节的分子机制。
