Predictions for the binding domain and potential new drug targets of 2019-nCoV

Zehua Zeng; Zhi Luo; Hongwu Du

In conclusion, two different deep learning-based models of protein structure prediction were used to show that the binding domain of 2019-nCov was highly conserved to the CTD1 region of the spike protein of SARS. In the functional prediction of protein, 28N or its molecules with similar structuresmay be used as a potential drug target to hydrolyze 2019-nCoV. Hopefully this study can help to develop new drug for the therapy of 2019-nCoV. Fig.1 Evolutionary relationships. The evolutionary history was inferred using the Neighbor-Joining method 14 The optimal tree with the sum of branch length = 0.00123858 is shown. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Maximum Composite Likelihood method and are in the units of the number of base substitutions per site. The analysis involved 33 nucleotide sequences. Codon positions included were 1st+2nd+3rd+Noncoding. All positions containing gaps and missing data were eliminated. There were a total of 29874 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 15 The diagram shows the superposition of glycoprotein structure and 5x58 structure on the surface of the predicted 2019-nCoV, which ranked first. B. The diagram shows the overlap between the surface glycoprotein structure and the 5x58 structure of the predicted 2019-nCoV, which ranks second. C. The part circled in red represents ace2 and spike trimer in 5x58. d. Put 5x58 and the prediction model in the same coordinate system, zoom in on the CTD1 binding region, and the arrow represents the direction of the protein conformation.