Protein dynamics, conformational exchange, and motional modes are different names for a process that is inherently present in the proteins, allowing these nanomachines to perform various cellular functions. An accurate estimation of such motions is therefore required to understand the mechanistic details of biological processes. Currently, it is achieved by computational methods (e.g., molecular dynamics (MD) simulations) and experimental methods (e.g., fluorescence measurements and nuclear magnetic resonance (NMR) relaxation measurements). Although measuring NMR relaxation is a robust method because it gives atomic level information in solution conditions, it requires enriching the protein molecules with 15N and/or 13C isotopes. In addition, relaxation measurement requires carrying out resonance assignment of NMR signals, which is a laborious process. Most measured NMR relaxation parameters are longitudinal relaxation rate (R1) and transverse relaxation rate (R2), which are sensitive to both global rotational diffusion and internal motions in the protein. Similarly, MD simulation probes the dynamical properties of biomolecules at atomic resolution and, when combined with experimental data, it enables rigorous validation of molecular models, providing interpretation of experimental observables.
In a recent study, Peng and co-workers computed the NMR observables (R1 and R2) from raw MD trajectories. The authors called the package “MD2NMR,” a Python-based computational framework that supports multiple trajectory formats and is compatible with widely-used MD packages. The authors validated the method not only on globular proteins, but also on intrinsically disordered proteins and demonstrated good agreement with experimental data and a high degree of reproducibility.
The authors claim that MD2NMR provides a foundation for community-standardized workflows and plan to develop the package further for additional NMR observables, incorporating spectral density models and automated parameter optimization. With these enhancements, MD2NMR’s applicability will broaden to diverse biomolecular systems.