Integrating NMR Restraints into Coarse-Grained Simulations: Toward Accurate Conformational Ensembles of Complex Protein Systems

Structural dynamics play critical roles for the biological activity of protein molecules. Characterizing the inherent conformational landscapes of these macromolecules remains a major experimental and computational challenge, particularly for heterogeneous and transient systems such as intrinsically disordered proteins, membrane-associated assemblies and disordered fuzzy coats of amyloid aggregates. In this context, coarse-grained (CG) molecular dynamics simulations have enabled accessing to extended time scales and large system sizes, however, their reduced resolution and simplified interaction potentials often limit the structural accuracy. Here, we introduce Martini3-NMR, an integrative framework that incorporates nuclear magnetic resonance (NMR) observables directly into CG protein force fields. Using artificial neural networks to model NMR chemical shifts at the CG level, and integrating these data with NOE restraints, we define an approach to significantly enhance the accuracy of CG simulations while maintaining their elevated sampling efficiency, thereby resulting in a substantially improved description of protein conformational ensembles. We demonstrate the broad applicability of Martini3-NMR by generating CG ensembles for a range of systems involved in diverse biological processes such as protein folding, oligomer disassembly within lipid bilayers and conformational transitions of disordered fuzzy regions decorating amyloid fibril surfaces, which were found to display condensate-like properties. By enabling an experimentally driven and computationally efficient exploration of protein conformational landscapes, Martini3-NMR provides a novel general framework for investigating dynamic, heterogeneous and multiscale biomolecular processes. This approach opens to significant new opportunities for extending CG simulations toward a more quantitative understanding of the relationship between molecular structure, dynamics and biological function.

结构动力学对于蛋白质分子的生物活性至关重要。解析这类大分子的固有构象景观仍是实验与计算领域的重大挑战，对于固有无序蛋白（intrinsically disordered proteins）、膜结合组装体以及淀粉样蛋白聚集体的无序模糊包膜这类异质性且瞬时性的体系而言尤为如此。在此背景下，粗粒度（coarse-grained, CG）分子动力学模拟已能够实现更长时间尺度与更大体系规模的模拟，但其较低的分辨率与简化的相互作用势往往会限制结构精度。本研究提出Martini3-NMR，这是一种将核磁共振（nuclear magnetic resonance, NMR）观测数据直接整合至CG蛋白质力场的集成框架。我们通过人工神经网络在CG层面建模NMR化学位移，并将此类数据与核Overhauser效应（NOE）约束相结合，构建出一种可在保留CG模拟优异采样效率的同时，显著提升模拟结构精度的方法，从而大幅优化对蛋白质构象系综的描述。我们通过为一系列参与不同生物学过程的体系生成CG构象系综，证明了Martini3-NMR的广泛适用性——这些体系包括蛋白质折叠、脂质双分子层内的寡聚体解离，以及修饰淀粉样纤维表面的无序模糊区域的构象转变，而此类区域被发现具有类凝聚体特性。借助该方法，研究人员可在实验驱动下以高效计算方式探索蛋白质构象景观，Martini3-NMR为研究动态、异质性且多尺度的生物分子过程提供了全新的通用框架。该方法为拓展CG模拟的应用范围，进而实现对分子结构、动力学与生物功能之间关系的更定量理解开辟了重要新机遇。