Aydin Fikret, Georgouli Konstantia, Pottier Loïc, Oppelstrup Tomas, Carpenter Timothy S, Tempkin Jeremy O B, Bremer Peer-Timo, Nissley Dwight V, Streitz Frederick H, Lightstone Felice C, Ingólfsson Helgi I
Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
J Phys Chem B. 2025 May 22;129(20):4895-4903. doi: 10.1021/acs.jpcb.4c08622. Epub 2025 May 8.
Computational techniques such as all-atom (AA) molecular dynamics (MD) simulations and coarse-grained (CG) models have been essential to study various biological problems over a wide range of scales. While AA simulations provide detailed insights, they are computationally expensive for capturing dynamics over longer length and time scales. CG approaches, particularly ultra-coarse-grained (UCG) models as considered in this study, have addressed this limitation by simplifying molecular representations, enabling the study of larger systems and longer time scales. This work focuses on the development of UCG models of proteins and their integration into the Multiscale Machine-Learned Modeling Infrastructure (MuMMI) to efficiently sample protein conformations, exemplified by the RAS-RBDCRD protein complex. By employing a combination of essential dynamics coarse graining (EDCG) and heterogeneous elastic network modeling (hENM) with anharmonic modifications, we developed UCG models based on the fluctuations observed in the higher resolution Martini CG simulations. These models allow the accurate sampling of protein configurations and long-range conformational changes. The incorporation of an implicit membrane model further enhanced the exploration of protein-membrane dynamics. Additionally, a novel machine-learning-based backmapping approach was developed to convert UCG structures to Martini CG representations, resulting in improved prediction accuracy. Finally, the integration of UCG models into MuMMI significantly enhances the exploration of protein configurations, offering critical insights into the role of protein dynamics in biological processes.
诸如全原子（AA）分子动力学（MD）模拟和粗粒度（CG）模型等计算技术，对于在广泛尺度上研究各种生物学问题至关重要。虽然AA模拟能提供详细的见解，但在捕捉更长长度和时间尺度的动力学方面计算成本高昂。CG方法，特别是本研究中所考虑的超粗粒度（UCG）模型，通过简化分子表示解决了这一局限性，从而能够研究更大的系统和更长的时间尺度。这项工作聚焦于蛋白质UCG模型的开发及其集成到多尺度机器学习建模基础设施（MuMMI）中，以高效地对蛋白质构象进行采样，以RAS-RBDCRD蛋白质复合物为例。通过结合基本动力学粗粒度（EDCG）和具有非谐修正的异质弹性网络建模（hENM），我们基于在更高分辨率的Martini CG模拟中观察到的涨落开发了UCG模型。这些模型能够准确地对蛋白质构象和长程构象变化进行采样。隐式膜模型的纳入进一步增强了对蛋白质-膜动力学的探索。此外，还开发了一种基于机器学习的新型反向映射方法，将UCG结构转换为Martini CG表示，从而提高了预测准确性。最后，将UCG模型集成到MuMMI中显著增强了对蛋白质构象的探索，为蛋白质动力学在生物过程中的作用提供了关键见解。
