Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
Biomolecules. 2020 Jul 15;10(7):1056. doi: 10.3390/biom10071056.
The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein-protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein-protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein-protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a "funnel-like" energy landscape. In summary, these results shed light on our understanding of how protein-protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein-protein association rates.
多功能蛋白质复合物的形成是几乎所有生物过程的基础。估计这些复合物的形成速度对揭示生物分子识别的机制具有广泛的意义。这种动力学性质传统上通过结合速率来量化，可以通过各种实验技术来测量。为了补充这些耗时且费力的方法，我们开发了一种粗粒化模拟方法来研究蛋白质-蛋白质相互作用的物理过程。我们系统地对我们的模拟方法进行了校准，以适应大规模的基准数据集。通过在模拟中结合基于物理的力场和统计衍生的势能，我们发现超过 80%的蛋白质复合物的结合速率可以在与其实验测量值相差一个数量级的范围内正确预测。我们进一步表明，源自互补来源的力场混合物能够以机械细节描述蛋白质-蛋白质相互作用的过程。例如，我们表明，蛋白质复合物的结合包含多个步骤，其中蛋白质通过反复解离和再结合不断搜索其局部结合取向并形成非天然样中间体。此外，由于在其天然构象周围观察到了一组松散结合的遭遇复合物，我们提出蛋白质-蛋白质结合的过渡态在结构水平上可能具有高度多样性。我们的研究还支持了这样的观点，即蛋白质复合物的结合是由“漏斗状”的能量景观驱动的。总之，这些结果揭示了我们对蛋白质-蛋白质识别如何在动力学上被调节的理解，并且我们的粗粒化模拟方法可以作为测量蛋白质-蛋白质结合速率的现有实验方法的有用补充。
