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Wang Y, Shekhar M, Thifault D, Williams CJ, McGreevy R, Richardson J, Singharoy A, Tajkhorshid E. Constructing atomic structural models into cryo-EM densities using molecular dynamics - Pros and cons. J Struct Biol 2018; 204:319-328. [PMID: 30092279 PMCID: PMC6394829 DOI: 10.1016/j.jsb.2018.08.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 07/31/2018] [Accepted: 08/05/2018] [Indexed: 01/11/2023]
Abstract
Accurate structure determination from electron density maps at 3-5 Å resolution necessitates a balance between extensive global and local sampling of atomistic models, yet with the stereochemical correctness of backbone and sidechain geometries. Molecular Dynamics Flexible Fitting (MDFF), particularly through a resolution-exchange scheme, ReMDFF, provides a robust way of achieving this balance for hybrid structure determination. Employing two high-resolution density maps, namely that of β-galactosidase at 3.2 Å and TRPV1 at 3.4 Å, we showcase the quality of ReMDFF-generated models, comparing them against ones submitted by independent research groups for the 2015-2016 Cryo-EM Model Challenge. This comparison offers a clear evaluation of ReMDFF's strengths and shortcomings, and those of data-guided real-space refinements in general. ReMDFF results scored highly on the various metric for judging the quality-of-fit and quality-of-model. However, some systematic discrepancies are also noted employing a Molprobity analysis, that are reproducible across multiple competition entries. A space of key refinement parameters is explored within ReMDFF to observe their impact within the final model. Choice of force field parameters and initial model seem to have the most significant impact on ReMDFF model-quality. To this end, very recently developed CHARMM36m force field parameters provide now more refined ReMDFF models than the ones originally submitted to the Cryo-EM challenge. Finally, a set of good-practices is prescribed for the community to benefit from the MDFF developments.
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Affiliation(s)
- Yuhang Wang
- Center for Biophysics and Quantitative Biology, College of Medicine, Department of Biochemistry, Beckman Institute for Advanced Science and Technology, and University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Mrinal Shekhar
- Center for Biophysics and Quantitative Biology, College of Medicine, Department of Biochemistry, Beckman Institute for Advanced Science and Technology, and University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Darren Thifault
- School of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University, Tempe, AZ 85287, United States
| | | | - Ryan McGreevy
- Center for Biophysics and Quantitative Biology, College of Medicine, Department of Biochemistry, Beckman Institute for Advanced Science and Technology, and University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Jane Richardson
- Department of Biochemistry, Duke University, Durham, NC 27710, United States
| | - Abhishek Singharoy
- School of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University, Tempe, AZ 85287, United States.
| | - Emad Tajkhorshid
- NIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
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