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

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Protein structural dynamics are crucial for biological activity, but characterizing them, especially for complex systems like intrinsically disordered proteins, is challenging.
• Coarse-grained (CG) molecular dynamics simulations offer efficiency for large systems but often lack structural accuracy due to simplified models.
• Existing methods struggle with heterogeneous and transient biological systems, limiting our understanding of protein conformational landscapes.

Purpose of the Study:

• To develop an integrative framework, Martini3-NMR, that enhances the accuracy of coarse-grained (CG) protein simulations.
• To incorporate nuclear magnetic resonance (NMR) observables directly into CG force fields for improved structural fidelity.
• To enable computationally efficient yet accurate exploration of protein conformational ensembles for dynamic biological systems.

Main Methods:

• Developed Martini3-NMR, an integrative framework combining CG molecular dynamics with NMR data.
• Utilized artificial neural networks to model NMR chemical shifts at the CG level.
• Integrated NMR chemical shifts and Nuclear Overhauser Effect (NOE) restraints into CG force fields.

Main Results:

• Martini3-NMR significantly enhances the accuracy of CG simulations while maintaining high sampling efficiency.
• The framework successfully generates improved CG ensembles for diverse systems, including protein folding and membrane-associated assemblies.
• Demonstrated improved description of protein conformational ensembles, capturing complex dynamics and heterogeneous structures.

Conclusions:

• Martini3-NMR provides a novel, experimentally driven computational framework for exploring protein conformational landscapes.
• This approach enables more quantitative investigations of dynamic, heterogeneous, and multiscale biomolecular processes.
• Offers new opportunities for understanding the structure-dynamics-function relationship in complex biological systems using efficient CG simulations.