Integrating Electron Paramagnetic Resonance Spectroscopy and Computational Modeling to Measure Protein Structure and Dynamics

Electron paramagnetic resonance (EPR) has become a powerful probe of conformational heterogeneity and dynamics of biomolecules. In this Review, we discuss different computational modeling techniques that enrich the interpretation of EPR measurements of dynamics or distance restraints. A variety of spin labels are surveyed to provide a background for the discussion of modeling tools. Molecular dynamics (MD) simulations of models containing spin labels provide dynamical properties of biomolecules and their labels. These simulations can be used to predict EPR spectra, sample stable conformations and sample rotameric preferences of label sidechains. For molecular motions longer than milliseconds, enhanced sampling strategies and de novo prediction software incorporating or validated by EPR measurements are able to efficiently refine or predict protein conformations, respectively. To sample large-amplitude conformational transition, a coarse-grained or an atomistic weighted ensemble (WE) strategy can be guided with EPR insights. Looking forward, we anticipate an integrative strategy for efficient sampling of alternate conformations by de novo predictions, followed by validations by systematic EPR measurements and MD simulations. Continuous pathways between alternate states can be further sampled by WE-MD including all intermediate states. Electron paramagnetic resonance (EPR) has become a powerful spectroscopic method for probing conformational diversity and dynamics of proteins. The sparse constraints from EPR measurements greatly benefit from coupling with molecular modeling. This Review discusses the modeling tools that complement EPR to elucidate protein dynamics, structure, and conformational changes.image

Automated summary

This Review discusses computational modeling techniques that enhance the interpretation of Electron Paramagnetic Resonance (EPR) measurements of biomolecules. Molecular dynamics simulations and enhanced sampling strategies are utilized to predict EPR spectra and sample stable conformations of biomolecules and their spin labels. The integration of de novo predictions, EPR measurements, and MD simulations is proposed for efficient sampling of alternate protein conformations. The importance of coupling EPR with modeling tools to investigate protein dynamics and structure is highlighted.