TCB Publications - Abstract

M. Shekhar, G. Terashi, C. Gupta, D. Sarkar, J. Nguyn, N. J. Sisco, A. Mondal, J. Vant, P. Fromme, W. D. Van Horn, Emad Tajkhorshid, D. Kihara, K. Dill, A. Perez, and A. Singharoy. CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps. Matter, 4:3195-3216, 2021. Published.

SHEK2021-ET Cryo-electron microscopy (EM) requires molecular modeling to refine structural details from data. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins that cannot be resolved by cryo-EM. Here, we introduce CryoFold - a pipeline of molecular dynamics simulations that determines ensembles of protein structures directly from sequence by integrating density data of varying sparsity at 35 Åresolution with coarse- grained topological knowledge of the protein folds. We present six examples showing its broad applicability for folding proteins between 72 to 2000 residues, including large membrane and multi-domain systems, and results from two EMDB competitions. Driven by the data from a single known state, CryoFold discovers common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins, and simultaneously reflect in the quality of multiple density maps. Many of these conformations, unseen by traditional methods, 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 GUI.


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