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Automated simulation-based membrane protein refinement into cryo-EM data
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).ORCID-id: 0000-0003-2558-0497
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Antal upphovsmän: 62023 (Engelska)Ingår i: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 122, nr 13, s. 2773-2781Artikel i tidskrift (Refereegranskat) 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.

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2023. Vol. 122, nr 13, s. 2773-2781
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Biofysik
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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
Tillgänglig från: 2024-01-17 Skapad: 2024-01-17 Senast uppdaterad: 2024-01-17Bibliografiskt granskad

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Blau, ChristianLindahl, Erik

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Blau, ChristianLindahl, Erik
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Institutionen för biokemi och biofysikScience for Life Laboratory (SciLifeLab)
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