tetano
Editor, Senior Moderator
Sci Rep
. 2025 May 9;15(1):16269.
doi: 10.1038/s41598-025-00555-9. Epitope mapping of SARS-CoV-2 Spike protein using naturally-acquired immune responses to develop monoclonal antibodies
Rubén López-Aladid[SUP] #[/SUP][SUP] 1 2 [/SUP], Leticia Bueno-Freire[SUP] #[/SUP][SUP] 3 4 [/SUP], Roc Farriol-Duran[SUP] #[/SUP][SUP] 5 6 [/SUP], Eduard Porta-Pardo[SUP] 5 6 [/SUP], Ruth Aguilar[SUP] 7 [/SUP], Marta Vidal[SUP] 7 [/SUP], Alfons Jiménez[SUP] 7 8 [/SUP], Roberto Cabrera[SUP] 3 4 [/SUP], Nil Vázquez[SUP] 3 4 [/SUP], Alexandre López-Gavín[SUP] 3 4 [/SUP], Gemma Moncunill[SUP] 7 9 [/SUP], Montserrat Carrascal[SUP] 10 [/SUP], Teresa García[SUP] 10 [/SUP], Miquel Lozano[SUP] 11 [/SUP], Alberto L García-Basteiro[SUP] 7 8 9 [/SUP], Carlota Dobaño[SUP] 7 9 [/SUP], Martalu D Pazos[SUP] 12 [/SUP], M-Carmen Estevez[SUP] 12 [/SUP], Laura M Lechuga[SUP] 12 [/SUP], Antoni Torres[SUP] 13 14 [/SUP], Laia Fernández-Barat[SUP] 15 16 [/SUP]
Affiliations
COVID-19 vaccination strategies are already available almost worldwide. However, it is also crucial to develop new therapeutic approaches, especially for vulnerable populations that may not fully respond to vaccination, such as the immunocompromised. In this project, we predicted 25 B-cell epitopes in silico in the SARS-CoV-2 Spike (S) protein and screened these against serum and plasma samples from 509 COVID-19 convalescent patients. The aim was to identify those epitopes with the highest IgG reactivity to produce monoclonal antibodies against them for COVID-19 treatment. We implemented Brewpitopes, a computational pipeline based on B-cell epitope prediction tools, such as BepiPred v2.0 and Discotope v2.0, and a series of antibody-epitope accessibility filters. We mapped the SARS-CoV-2 S protein epitopes most likely to be recognised by human neutralizing antibodies. Linear and structural epitope predictions were included and were further refined considering accessibility factors influencing their binding to antibodies like glycosylation status, localization in the viral membrane and accessibility on the 3D-surface of S. Blood samples were collected from 509 COVID-19 patients prospectively recruited 35 days after symptoms initiation, positive RT-qPCR or hospital/ICU discharge. Presence of IgG against SARS-CoV-2 was confirmed by lateral flow immunoassays. Epitopes immunogenicity was tested through the analysis of IgG levels and seropositivity in the convalescent serum and plasma samples and 126 pre-pandemic negative controls by Luminex to identify those with the highest reactivity. The seropositivity cut-offs for each epitope were calculated using a set of 126 pre-pandemic samples as negative controls (NC). Twenty-five SARS-CoV-2 S epitopes were predicted in silico as potentially the most immunogenic. These were synthesized and tested in a multiplex immunoassay against sera/plasmas from convalescent COVID-19 patients (5.7% asymptomatic, 35.6% mild, 13.8% moderate, 23% severe and 22% unknown because of anonymous donation). Among the 25 epitopes tested, 3 exhibited significantly higher IgG reactivity compared to the rest. The proportion of seropositive patients towards these 3 epitopes, based on median fluorescence intensity (MFI or Log[SUB]10[/SUB] MFI) above that from NC, ranged between 11 and 48%. Two out of the three most immunogenic epitopes were scaled up, resulting in the generation of two monoclonal antibodies (mAbs). These two mAbs exhibited comparable levels of S protein affinity to commercialized mAbs. Our data shows that the candidate S epitopes predicted in silico are recognised by IgG present in convalescent serum and plasma. This evidence suggests that our computational and experimental pipeline is able to yield immunogenic epitopes against SARS-CoV-2 S. These epitopes are suitable for the development of novel antibodies for preventive or therapeutic approaches against COVID-19.
Keywords: Antibody; Epitope mapping; IgG; Immunoassay; Luminex; Multiplex; Predictive models; Recovered patients of COVID-19; SARS-CoV-2; Serology; Spike protein.
. 2025 May 9;15(1):16269.
doi: 10.1038/s41598-025-00555-9. Epitope mapping of SARS-CoV-2 Spike protein using naturally-acquired immune responses to develop monoclonal antibodies
Rubén López-Aladid[SUP] #[/SUP][SUP] 1 2 [/SUP], Leticia Bueno-Freire[SUP] #[/SUP][SUP] 3 4 [/SUP], Roc Farriol-Duran[SUP] #[/SUP][SUP] 5 6 [/SUP], Eduard Porta-Pardo[SUP] 5 6 [/SUP], Ruth Aguilar[SUP] 7 [/SUP], Marta Vidal[SUP] 7 [/SUP], Alfons Jiménez[SUP] 7 8 [/SUP], Roberto Cabrera[SUP] 3 4 [/SUP], Nil Vázquez[SUP] 3 4 [/SUP], Alexandre López-Gavín[SUP] 3 4 [/SUP], Gemma Moncunill[SUP] 7 9 [/SUP], Montserrat Carrascal[SUP] 10 [/SUP], Teresa García[SUP] 10 [/SUP], Miquel Lozano[SUP] 11 [/SUP], Alberto L García-Basteiro[SUP] 7 8 9 [/SUP], Carlota Dobaño[SUP] 7 9 [/SUP], Martalu D Pazos[SUP] 12 [/SUP], M-Carmen Estevez[SUP] 12 [/SUP], Laura M Lechuga[SUP] 12 [/SUP], Antoni Torres[SUP] 13 14 [/SUP], Laia Fernández-Barat[SUP] 15 16 [/SUP]
Affiliations
- PMID: 40346118
- PMCID: PMC12064773
- DOI: 10.1038/s41598-025-00555-9
COVID-19 vaccination strategies are already available almost worldwide. However, it is also crucial to develop new therapeutic approaches, especially for vulnerable populations that may not fully respond to vaccination, such as the immunocompromised. In this project, we predicted 25 B-cell epitopes in silico in the SARS-CoV-2 Spike (S) protein and screened these against serum and plasma samples from 509 COVID-19 convalescent patients. The aim was to identify those epitopes with the highest IgG reactivity to produce monoclonal antibodies against them for COVID-19 treatment. We implemented Brewpitopes, a computational pipeline based on B-cell epitope prediction tools, such as BepiPred v2.0 and Discotope v2.0, and a series of antibody-epitope accessibility filters. We mapped the SARS-CoV-2 S protein epitopes most likely to be recognised by human neutralizing antibodies. Linear and structural epitope predictions were included and were further refined considering accessibility factors influencing their binding to antibodies like glycosylation status, localization in the viral membrane and accessibility on the 3D-surface of S. Blood samples were collected from 509 COVID-19 patients prospectively recruited 35 days after symptoms initiation, positive RT-qPCR or hospital/ICU discharge. Presence of IgG against SARS-CoV-2 was confirmed by lateral flow immunoassays. Epitopes immunogenicity was tested through the analysis of IgG levels and seropositivity in the convalescent serum and plasma samples and 126 pre-pandemic negative controls by Luminex to identify those with the highest reactivity. The seropositivity cut-offs for each epitope were calculated using a set of 126 pre-pandemic samples as negative controls (NC). Twenty-five SARS-CoV-2 S epitopes were predicted in silico as potentially the most immunogenic. These were synthesized and tested in a multiplex immunoassay against sera/plasmas from convalescent COVID-19 patients (5.7% asymptomatic, 35.6% mild, 13.8% moderate, 23% severe and 22% unknown because of anonymous donation). Among the 25 epitopes tested, 3 exhibited significantly higher IgG reactivity compared to the rest. The proportion of seropositive patients towards these 3 epitopes, based on median fluorescence intensity (MFI or Log[SUB]10[/SUB] MFI) above that from NC, ranged between 11 and 48%. Two out of the three most immunogenic epitopes were scaled up, resulting in the generation of two monoclonal antibodies (mAbs). These two mAbs exhibited comparable levels of S protein affinity to commercialized mAbs. Our data shows that the candidate S epitopes predicted in silico are recognised by IgG present in convalescent serum and plasma. This evidence suggests that our computational and experimental pipeline is able to yield immunogenic epitopes against SARS-CoV-2 S. These epitopes are suitable for the development of novel antibodies for preventive or therapeutic approaches against COVID-19.
Keywords: Antibody; Epitope mapping; IgG; Immunoassay; Luminex; Multiplex; Predictive models; Recovered patients of COVID-19; SARS-CoV-2; Serology; Spike protein.