Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Utrecht 3584CH , The Netherlands.
Department of Pharmaceutical Sciences , University of Basel , 4056 Basel , Switzerland.
J Chem Theory Comput. 2019 Nov 12;15(11):6358-6367. doi: 10.1021/acs.jctc.9b00310. Epub 2019 Oct 10.
Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing for modeling larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modeling platform. Docking and refinement are performed at the coarse-grained level, and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ∼7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.
预测蛋白质相互作用的 3D 结构仍然是计算结构生物学领域的一个挑战。这在一定程度上是由于难以对多个相互作用的柔性多肽链的复杂能量景观进行采样。粗粒化方法通过平滑能量景观来帮助解决这个限制,从而更容易识别全局能量最小值。它们还加速了计算,允许对更大的组装体进行建模。在这里,我们将 MARTINI 粗粒化力场实现到我们的综合建模平台 HADDOCK 中。对接和细化在粗粒化水平上进行,然后通过距离约束引导的变形过程将得到的模型转换回原子分辨率。我们的方案在蛋白质对接基准 5 中最大的复合物上进行了测试,与标准全原子计算相比,整体速度提高了约 7 倍,同时保持了相似的准确性,并产生了更多的近天然解决方案。为了展示我们方法的潜力,我们进行了同时进行 7 个主体对接,以模拟 1:6 KaiC-KaiB 复合物,将来自质谱的诱变和氢/氘交换数据与对称约束相结合,并使用最近发表的冷冻电镜结构对得到的模型进行验证。