PT - JOURNAL ARTICLE AU - Mrinal Shekhar AU - Genki Terashi AU - Chitrak Gupta AU - Gaspard Debussche AU - Nicholas J. Sisco AU - Jonathan Nguyen AU - James Zook AU - John Vant AU - Daipayan Sarkar AU - Petra Fromme AU - Wade D. Van Horn AU - Ken Dill AU - Daisuke Kihara AU - Emad Tajkhorshid AU - Alberto Perez AU - Abhishek Singharoy TI - CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics AID - 10.1101/687087 DP - 2019 Jan 01 TA - bioRxiv PG - 687087 4099 - http://biorxiv.org/content/early/2019/07/02/687087.short 4100 - http://biorxiv.org/content/early/2019/07/02/687087.full AB - Cryo-EM is a powerful method for determining biomolecular structures. But, unlike X-ray crystallography or solution-state NMR, which are data-rich, cryo-EM can be data-poor. Cryo-EM routinely gives electron density information to about 3–5 Å and the resolution often varies across the structure. So, it has been challenging to develop an automated computer algorithm that converts the experimental density maps to complete molecular structures. We address this challenge with CryoFold, a computational method that finds the chain trace from the density maps using MAINMAST, then performs molecular dynamics simulations using ReMDFF, a resolution-exchange flexible fitting protocol, accelerated by MELD, which uses low-information data to broaden the relevant conformational searching of secondary and tertiary structures. We describe four successes of structure determinations, including for membrane proteins and large molecules. CryoFold handles input data that is heterogeneous, and even sparse. The software is automated, and is available to the public via a python-based graphical user interface.