8b7v
From Proteopedia
Automated simulation-based refinement of maltoporin into a cryo-EM density
Structural highlights
FunctionA0A2X5NX10_ECOLX Involved in the transport of maltose and maltodextrins.[HAMAP-Rule:MF_01301] Publication Abstract from PubMedThe 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 that 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 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. Automated simulation-based membrane protein refinement into cryo-EM data.,Yvonnesdotter L, Rovsnik U, Blau C, Lycksell M, Howard RJ, Lindahl E Biophys J. 2023 Jun 5:S0006-3495(23)00367-3. doi: 10.1016/j.bpj.2023.05.033. PMID:37277992[1] From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine. References
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