Automated simulation-based membrane-protein refinement into cryo-EM data
I.The resolution revolution has increasingly enabled single-particle cryogenic electron microscopy (cryo-EM) reconstructions of previously inaccessible systems, including membrane proteins - a category that constitutes a disproportionate share of drug targets. We present a protocol for using density-guided molecular dynamics simulations to automatically refine atomistic models into membrane-protein cryo-EM maps. Using adaptive-force density-guided simulations as implemented in the GROMACS molecular dynamics package, we show how automated model refinement of a membrane protein is achieved without the need to manually tune the fitting force ad hoc. We also present selection criteria to choose the best fit model which balances stereochemistry and goodness-of-fit. The proposed protocol was used to refine models into a new cryo-EM density of the membrane protein maltoporin, either in a lipid bilayer or detergent micelle, and we found that results do not substantially differ from fitting in solution. Fitted structures satisfied classical model-quality metrics and improved the quality and the model-to-map correlation of the X-ray starting structure. Additionally, the density-guided fitting in combination with generalized orientation-dependent all-atom potential (GOAP) was used to correct the pixel-size estimation of the experimental cryo-EM density map. This work demonstrates the applicability of a straightforward automated approach to fitting membrane-protein cryo-EM densities. Such computational approaches promise to facilitate rapid refinement of proteins under different conditions or with various ligands present, including targets in the highly relevant superfamily of membrane proteins. II. STATEMENT OF SIGNIFICANCECryo-EM is an increasingly critical method of structure determination. As data collection and model generation become more efficient, iteratively fitting an experimental density can still require considerable time and expertise. Membrane proteins are particularly important targets in pharmacology and bioengineering, but can present distinctive challenges to data quality and modeling. Here, we tested a new tool to drive density fitting with molecular dynamics simulations, in context of a new structure of the membrane protein maltoporin. Fitting performed well in detergent, lipids, or solution, offering simpler options for fully automated simulation protocols. We were also able to apply fitting to adjust the microscopes pixel size. The approach described here should be applicable to rapid, accurate refinement of a variety of membrane-protein structures.