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Automated simulation-based membrane protein refinement into cryo-EM data
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0003-2558-0497
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Number of Authors: 62023 (English)In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 122, no 13, p. 2773-2781Article in journal (Refereed) Published
Abstract [en]

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 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.

Place, publisher, year, edition, pages
2023. Vol. 122, no 13, p. 2773-2781
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Biophysics
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URN: urn:nbn:se:su:diva-225410DOI: 10.1016/j.bpj.2023.05.033ISI: 001122880200001PubMedID: 37277992Scopus ID: 2-s2.0-85162918774OAI: oai:DiVA.org:su-225410DiVA, id: diva2:1828573
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved

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Blau, ChristianLindahl, Erik

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