Molecular dynamics-based refinement and validation for sub-5 Å cryo-electron microscopy maps
Abhishek Singharoy,
Ivan Teo,
Ryan McGreevy,
John E Stone,
Jianhua Zhao,
Klaus Schulten
Affiliations
Abhishek Singharoy
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States
Ivan Teo
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
Ryan McGreevy
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States
John E Stone
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States
Jianhua Zhao
Department of Biochemistry and Biophysics, University of California San Francisco School of Medicine, San Francisco, United States
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, United States; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
Two structure determination methods, based on the molecular dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or ensembles of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a radius of convergence of ~25 Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2 Å and 3.4 Å resolution, respectively. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria based on the inherent dynamics of the macromolecules studied, captured by means of local root mean square fluctuations. The MDFF tools described are available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services.