key: cord-328585-1rkrrx8a
authors: Lu, Shuai; Xie, Xi-xiu; Zhao, Lei; Wang, Bin; Zhu, Jie; Yang, Ting-rui; Yang, Guang-wen; Ji, Mei; Lv, Cui-ping; Xue, Jian; Dai, Er-hei; Fu, Xi-ming; Liu, Dong-qun; zhang, Lun; Hou, Sheng-jie; Yu, Xiao-lin; Wang, Yu-ling; Gao, Hui-xia; Shi, Xue-han; Ke, Chang-wen; Ke, Bi-xia; Jiang, Chun-guo; Liu, Rui-tian
title: The immunodominant and neutralization linear epitopes for SARS-CoV-2
date: 2020-08-27
journal: bioRxiv
DOI: 10.1101/2020.08.27.267716
sha: 
doc_id: 328585
cord_uid: 1rkrrx8a

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) becomes a tremendous threat to global health. Although vaccines against the virus are under development, the antigen epitopes on the virus and their immunogenicity are poorly understood. Here, we simulated the three-dimensional structures of SARS-CoV-2 proteins with high performance computer, predicted the B cell epitopes on spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins of SARS-CoV-2 using structure-based approaches, and then validated the epitope immunogenicity by immunizing mice. Almost all 33 predicted epitopes effectively induced antibody production, six of which were immunodominant epitopes in patients identified via the binding of epitopes with the sera from domestic and imported COVID-19 patients, and 23 were conserved within SARS-CoV-2, SARS-CoV and bat coronavirus RaTG13. We also found that the immunodominant epitopes of domestic SARS-CoV-2 were different from that of the imported, which may be caused by the mutations on S (G614D) and N proteins. Importantly, we validated that eight epitopes on S protein elicited neutralizing antibodies that blocked the cell entry of both D614 and G614 pseudo-virus of SARS-CoV-2, three and nine epitopes induced D614 or G614 neutralizing antibodies, respectively. Our present study shed light on the immunodominance, neutralization, and conserved epitopes on SARS-CoV-2 which are potently used for the diagnosis, virus classification and the vaccine design tackling inefficiency, virus mutation and different species of coronaviruses.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute 51 respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused unprecedented impact on global 52 health. More than 6 million cases were reported by WHO on June 1, 2020 53 unsolved, it is possible to model these protein structures based on their reported gene sequence 74 using molecular simulation and then predict their epitopes (Lu et al., 2020) . Increasing evidences 75 7 and S protein, respectively, and only S793-812(N) induced higher antibodies than that of the non-152 glycosylated epitope ( Fig. 1F and 1G) . 153

To investigate the spectrum of antibodies in COVID-19 patients, we detected the binding of the 155 early convalescent sera of 8 imported (Europe) cases which infected SARS-CoV-2 in early April, 156 2020 and 12 domestic (China) cases in early February, 2020 to various epitopes ( Table 1) K203R204/G189R203G204/R203G204/R203G204S344 in N protein, respectively (Table 1) , 172 resulting in different immunodominant epitopes of different virus sub-strains which provide the 173 bases for the differential diagnosis. 174

The predicted epitopes induce neutralization antibody production 175 SARS-CoV-2 pseudo-virus neutralization assay is a well-accepted method to detect the ability of 176 vaccine to inhibit SARS-CoV-2 infection . To assess 8 neutralization antibodies induced by S protein epitopes, we incubated the immunization sera with 178 D614 or G614 SARS-CoV-2 pseudo-viruses and then the mixture was added to ACE2-293FT 179 cells which stably expressed ACE2. The results showed that immunized sera of S92-106, S139-180 153, S439-454 and S455-469 epitopes inhibited SARS-CoV-2 pseudo-virus infection compared 181

to HBc-S control (p < 0.0001), with inhibition rates around 40%-50% (Fig. 3A) . Also, the sera of 182 S16-30, S243-257, S406-420, S475-499, S556-570, S793-812(N) and S909-923 inhibited SARS-183

CoV-2 infection with the inhibition rate from 20% to 40% (Fig. 3A) , indicating that these 11 184 epitopes induced neutralization antibody production. To detect the effect of epitope immunization 185 on the neutralizing responses of G614 SARS-CoV-2, we incubated the epitope-immunized sera 186 with the G614 SARS-CoV-2 pseudo-viruses. The results showed that sera of epitopes inhibiting 187 D614 SARS-CoV-2 also inhibited G614 SARS-CoV-2 infection, except of S16-30, S243-257 and 188 S556-570 (Fig. 3B) . However, the immunized sera of epitopes S63-85, S495-509, S675-689, 189 S703-719, S793-812, S1065-1079, S1065-1079(N), and S1106-1120 only inhibited G614 SARS-190

CoV-2 pseudo-virus infection. Interestingly, compared with its non-glycosylation epitope, S63-191 85(N), S703-719(N) and S1065-1079(N) induced less neutralizing antibodies to G614 192 pseudovirus while that of S793-812(N) increased (Fig. 3B ). We then 2-fold serial diluted the sera 193 with inhibition rate >50%, and determined the neutralizing antibody titers induced by these 194 epitopes. S63-85 induced the highest neutralizing effect with antibody titer at 1:80 (Fig. 3C ). The 195 structural analysis showed that most of these neutralizing epitopes to D614 and G614 SARS-196

CoV-2 were in or near N-terminal domain (NTD), receptor-binding domain (RBD) or S2' 197 cleavage site of S protein and were spatially clustered ( Fig. 3D-I) , except S1106-1120 and S675-198 689 which are in or near transmembrane domain and S1/S2 cleavage site at interface of S1 and S2 199 subunits of S protein, respectively. 

Vaccines are potent means to control the current pandemic of COVID-19 and to prevent future 205 outbreak, thus fully understanding the immune responses elicited by the virus epitopes is urgent. 206

As antigenic determinants, identifying and understanding epitopes would facilitate vaccine design 207 and development. Since neutralization antibodies usually recognize the surface area of the virus 208 proteins, identification of epitopes in surface area based on 3D structure of proteins may increase 209 the efficiency to find the epitopes that elicit neutralization antibodies. In this study, we in first 210 time used high-performance computer to simulate the three-dimensional structures of major 211 proteins on SARS-CoV-2 and predicted 33 surface area epitopes using the modeled protein 212 structures, which was proved to be efficient and accurate by the further mouse immunization and 213 pseudo-virus neutralization assay. Within the 33 identified epitopes, 24 were conserved with >80% 214 homology and 18 shared >90% homology among SARS-CoV-2, SARS-CoV and bat coronavirus 215

RaTG13 (Table S1), implicating that these epitopes could be used as for designing broad-216 spectrum betacoronavirus vaccines. 217

Some surface area epitopes of SARS-CoV-2 were determined to be immunodominant in 218 present study by detecting the binding of the antibodies in early convalescent sera of COVID-19 219 patient to various predicted epitopes. Consistent with previous report, S556-570 was an 220 immunodominant epitope and this epitope was able to elicit neutralization antibodies ( antibodies targeting the interaction interface between RBD and ACE2, but also the antibodies 255 binding with N-terminal domain (NTD) of S protein, such as S16-30, S92-106, S139-153 and 256 S243-257 showed neutralization effect on D614 strain. Within the neutralizing NTD epitopes, 257 S92-106 and S139-153 also showed neutralization effect on G614 strain. Antibodies induced by 258 S63-85 but not its glycosylated form inhibited the cell entry of G614 pseudovirus rather than 259 D614 pseudovirus, and the epitopes S703-719(N) and S1064-1079(N) induced less neutralizing mutation. S675-689 is at the S1/S2 cleavage site located at interface of S1 and S2 subunits of S 275 protein which is important for spike protein mediated virus-cell membrane fusion. Our results 276 suggested that the S675-689 epitope was at the vulnerability site of SARS-CoV-2 and might be 277 an ideal candidate and targeting site for vaccine development. Moreover, our results showed that 278 the neutralizing epitopes are highly spatial clustered, indicating that conformational epitopes in 279 the above regions may be used for designing an effective vaccine. 280

In conclusion, we have successfully predicted SARS-CoV-2 epitopes based on of the 3D 281 structures of S, M, N, E proteins, validated their immunogenicity, characterized the homology of 282 the epitopes among betacoronavirus, and identified the neutralization and immunodominant 283 epitopes (Table S3) . Our findings provide a wide neutralization and immunodominant epitope 284 spectrum for the design of an effective, safe vaccine, differential diagnosis and virus 285 classification. 286 

Serum samples were collected from 20 early convalescent patients with COVID-19 which were 293 confirmed by SARS-CoV-2 real-time reverse transcriptase-polymerase chain reaction (RT-PCR). Golden Bridge Biotechnology Co., Beijing, China) and chromogenic substrate TMB 367 (ThermoFisher, Waltham, MA, USA). The cut-off for seropositivity was set as the mean value 368 plus three standard deviations (3SD) in HBc-S control sera. The binding of the epitopes to the 369 sera of COVID-19 infected patients were detected by ELISA using the same procedure as 370 described above, 96-well plates were coated with 0.5 μg peptides and sera were diluted at 1:50. 371

The cut-off lines were based on the mean value + 3SD in 4-5 healthy persons. All ELISA studies 372 were performed at least twice. 373

Pooled mice sera collected at day 10 after the third immunization were diluted in DMEM 375 supplemented with 10% fetal bovine serum, mixed with 1.6×10 6 SARS-CoV-2 pseudoviruses and 376 incubated at 37 ℃ for 1 h. The mixture was then added to 1.5×10 4 ACE2-293T cells and the 377 medium was replaced after 6 h. Firefly luciferase activity was measured 72 h post-infection using 378

Bright-Glo™ Luciferase Assay System (Promega). All neutralization studies were performed at 379 least twice. Three independently mixed replicates were measured for each experiment. 380

The data presented in this study were expressed as mean ± SEM. Data were analyzed by one-way 382 (ANOVA), followed by multiple comparisons using Dunnett's test within GraphPad Prism 7.0 383 software. Student t-test was used to analyze the data of non-glycosylated and glycosylated 384 epitopes. p < 0.05 was considered to be significant. with HBc-S control; ***p < 0.001; ****p < 0.0001; one-way ANOVA followed by Dunnett's test; 554

Compared with non-glycosylated epitope; #p < 0.05; Student t-test). Anti-N antibody titers (log 10 ) **** **** *** S 1 6 -3 0 S 6 3 -8 5 S 6 3 -8 5 ( N ) S 9 2 -1 0 6 S 1 3 9 -1 5 3 S 1 7 6 -1 9 0 S 2 0 5 -2 1 9 S 2 4 3 -2 5 7 S 2 6 3 -2 7 5 S 3 6 6 -3 8 1 S 4 0 6 -4 2 0 S 4 3 9 -4 5 4 S 4 5 5 -4 6 9 S 4 7 5 -4 9 9 S 4 9 5 -5 0 9 S 5 5 6 -5 7 0 S 6 7 5 -6 8 9 S 7 0 3 -7 1 9 S 7 0 3 -7 1 9 ( N ) S 7 2 1 -7 3 3 S 7 9 3 -8 1 2 S 7 9 3 -8 1 2 ( N ) S 9 0 9 -9 2 3 S 1 0 6 5 -1 0 7 9 S 1 0 6 5 -1 0 7 9 ( N S 1 1 0 6 -1 1 2 0 S 1 2 0 5 -1 2 2 2 E 1 -1 7 E 1 2 -2 5 M 4 7 -6 2 M 1 6 0 -1 7 5 M 1 8 3 -1 9 7 N 4 3 -5 7 N 5 9 -7 3 N 7 7 -9 1 N 1 5 2 -1 7 0 N 3 5 7 -3 7 3 1 2 3 4 5 6 Anti-epitope antibody titers (log 10 ) S 1 6 -3 0 S 6 3 -8 5 S 6 3 -8 5 ( N ) S 9 2 -1 0 6 S 1 3 9 -1 5 3 S 1 7 6 -1 9 0 S 2 0 5 -2 1 9 S 2 4 3 -2 5 7 S 2 6 3 -2 7 5 S 3 6 6 -3 8 1 S 4 0 6 -4 2 0 S 4 3 9 -4 5 4 S 4 5 5 -4 6 9 S 4 7 5 -4 9 9 S 4 9 5 -5 0 9 S 5 5 6 -5 7 0 S 6 7 5 -6 8 9 S 7 0 3 -7 1 9 S 7 0 3 -7 1 9 ( N ) S 7 2 1 -7 3 3 S 7 9 3 -8 1 2 S 7 9 3 -8 1 2 ( N ) S 9 0 9 -9 2 3 S 1 0 6 5 -1 0 7 9 S 1 0 6 5 -1 0 7 9 ( N S 1 1 0 6 -1 1 2 0  S 1 2 0 5 -1 2 2 HBc-S S 1 6 -3 0 S 6 3 -8 5 S 6 3 -8 5 ( N ) S 9 2 -1 0 6 S 1 3 9 -1 5 3 S 1 7 6 -1 9 0 S 2 0 5 -2 1 9 S 2 4 3 -2 5 7 S 2 6 3 -2 7 5 S 3 6 6 -3 8 1 S 4 0 6 -4 2 0 S 4 3 9 -4 5 4 S 4 5 5 -4 6 9 S 4 7 5 -4 9 9 S 4 9 5 -5 0 9 S 5 5 6 -5 7 0 S 6 7 5 -6 8 9 S 7 0 3 -7 1 9 S 7 0 3 -7 1 9 ( N ) S 7 2 1 -7 3 3 S 7 9 3 -8 1 2 S 7 9 3 -8 1 2 ( N ) S 9 0 9 -9 2 3 S 1 0 6 5 -1 0 7 9 S 1 0 6 5 -1 0 7 9 ( N S 1 1 0 6 -1 1 2 0 S 1 2 0 5 - 

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This work was supported by grants from the National Natural Science Foundation of China 387 **** **** ****