Cryo-EM structures of MERS-CoV and SARS-CoV spike glycoproteins reveal the dynamic receptor binding domains

Yuan, Yuan; Cao, Duanfang; Zhang, Yanfang; Ma, Jun; Qi, Jianxun; Wang, Qihui; Lu, Guangwen; Wu, Ying; Yan, Jinghua; Shi, Yi; Zhang, Xinzheng; Gao, George F.

Model building and refinement. For model building, the predicted model of MERS-CoV or SARS-CoV S protein from the Phyre2 web server 48 was used as the starting model. De novo building was performed manually in COOT (ref. 49 ) based on the well-defined continuous electron density of its main chain in the three-fold symmetry map, and sequence assignment was guided mainly by bulky amino-acid residues densities. For MERS-CoV S model, the NTD domain and RBD domain were generated by fitting its crystal structure into the electron density map. For SARS-CoV S model, the NTD initial model was built manually in COOT based on the electron density of its main chain in the map, and then the NTD initial model was used as template for crystal structure determination. Finally, the model of SARS-CoV S NTD domain and RBD domain were generated by fitting the crystal structures into the electron density map. The structure model was first refined in real space against the cryo-EM map using phenix.real_space_refine application in PHENIX (ref. 50) with geometry and secondary structure restraints. Refinement in reciprocal space was then performed in REFMAC (ref. 51) with stereo-chemical. Automatic real-space and reciprocal-space refinements followed by manual correction in COOT were carried out iteratively until there were no more improvements in both R factor and geometry parameters. The refinement statistics of the structural model are summarized in Supplementary Table 2 . For the reconstructions of MERS-CoV S class one, class two, S1 trimer and SARS-CoV S class one, class two, all domains of S1 and S2 model were fitted into the corresponding maps separately.