CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Mrinal Shekhar, Genki Terashi, Chitrak Gupta, Daipayan Sarkar, Gaspard Debussche, Nicholas J. Sisco, Jonathan Nguyen, Arup Mondal, John Vant, Petra Fromme, Wade D. Van Horn, Emad Tajkhorshid, Daisuke Kihara, Ken Dill, Alberto Perez, Abhishek Singharoy

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. We introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).

Original languageEnglish (US)
Pages (from-to)3195-3216
Number of pages22
JournalMatter
Volume4
Issue number10
DOIs
StatePublished - Oct 6 2021

Keywords

  • ATP synthase
  • CryoEM modeling
  • MAP3: Understanding
  • computations
  • cryoelectron microscopy
  • ensemble refinement
  • integrative modeling
  • molecular dynamics simulations
  • protein folding ensemble

ASJC Scopus subject areas

  • Materials Science(all)

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