Structure-based identification of SARS-derived peptides with potential to induce broad protective immunity

In 2019, a pandemic age caused by a novel strain of coronavirus (SARS-CoV-2) with a fast-growing number of confirmed cases all over the world took place. Several efforts are underway to produce a new vaccine, promoting immediate and long-term cell-mediated immunity based on T cell lymphocytes. T cell responses are particularly important for fighting viral infections because they can find and eliminate infected cells. This project will develop a computational pipeline to enable the identification of peptides that are conserved across different SARS-CoV strains, and that can potentially be used to induce broad protective cellular immunity against these viruses. The approach is based on the combined use of gold-standard sequence-based methods, and new cutting-edge methods for the structural modeling and analysis of peptides bound to different Human Leukocyte Antigen (HLA) receptors. HLAs are responsible for displaying the peptides to T-cell lymphocytes, and the proposed pipeline will enable the identification of conserved hot-spots capable of triggering T-cell responses against multiple SARS-CoV variants. In the context of this project, research will target conserved peptides from the Nucleocapsid (N) protein of SARS-CoV-2. If needed, the optimization of predicted peptides will be conducted for different prevalent HLA alleles. The proposed computational pipeline will be built using general software-engineering principles, making it also applicable to study different proteins from SARS-CoV variants, and even other pathogens.

For more information you can access NSF Covid Information Commons webpage at https://covidinfocommons.datascience.columbia.edu/awards/2033262.

This work has been supported by grant NSF DBI 2033262.

Related Publications

1. M. M. Rigo, R. Fasoulis, A. Conev, S. Hall-Swan, D. Amaral Antunes, and L. Kavraki, “SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development,” Frontiers in Immunology, vol. 13, Jul. 2022.
   
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   @article{rigo2022-sars-arena,
     title = {SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from
         SARS-related Coronaviruses for Novel Vaccine Development},
     author = {Rigo, Mauricio Menegatti and Fasoulis, Romanos and Conev, Anja and Hall-Swan, Sarah and Amaral Antunes, Dinler and Kavraki, Lydia},
     journal = {Frontiers in Immunology},
     month = jul,
     year = {2022},
     volume = {13},
     doi = {10.3389/fimmu.2022.931155},
     abstract = {The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19
         disease, has affected millions of people worldwide. There is constant search for new
         therapies to either prevent or mitigate the disease. Fortunately, we have observed the
         successful development of multiple vaccines. Most of them are focused on one viral envelope
         protein, the spike protein. However, such focused approaches may contribute for the rise of
         new variants, fueled by the constant selection pressure on envelope proteins, and the
         widespread dispersion of coronaviruses in nature. Therefore, it is important to examine
         other proteins, preferentially those that are less susceptible to selection pressure, such
         as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral
         response, peptides from its conserved regions can be presented by class I Human Leukocyte
         Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the
         increased number of protein sequences deposited in biological databases daily and the N
         protein conservation among viral strains, computational methods can be leveraged to discover
         potential new targets for SARS-CoV-2 and SARS-CoV-related viruses. Here we developed
         SARS-Arena, a user-friendly computational pipeline that can be used by practitioners of
         different levels of expertise for novel vaccine development. SARS-Arena combines
         sequence-based methods and structure-based analyses to (i) perform multiple sequence
         alignment (MSA) of SARS-CoV-related N protein sequences, (ii) recover candidate peptides of
         different lengths from conserved protein regions, and (iii) model the 3D structure of the
         conserved peptides in the context of different HLAs. We present two main Jupyter Notebook
         workflows that can help in the identification of new T-cell targets against SARS-CoV
         viruses. In fact, in a cross-reactive case study, our workflows identified a conserved N
         protein peptide (SPRWYFYYL) recognized by CD8+ T-cells in the context of HLA-B7+. SARS-Arena
         is available at https://github.com/KavrakiLab/SARS-Arena.},
     publisher = {Frontiers Media SA},
     url = {https://doi.org/10.3389/fimmu.2022.931155}
   }
   

   Abstract

   ×

   The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein sequences deposited in biological databases daily and the N protein conservation among viral strains, computational methods can be leveraged to discover potential new targets for SARS-CoV-2 and SARS-CoV-related viruses. Here we developed SARS-Arena, a user-friendly computational pipeline that can be used by practitioners of different levels of expertise for novel vaccine development. SARS-Arena combines sequence-based methods and structure-based analyses to (i) perform multiple sequence alignment (MSA) of SARS-CoV-related N protein sequences, (ii) recover candidate peptides of different lengths from conserved protein regions, and (iii) model the 3D structure of the conserved peptides in the context of different HLAs. We present two main Jupyter Notebook workflows that can help in the identification of new T-cell targets against SARS-CoV viruses. In fact, in a cross-reactive case study, our workflows identified a conserved N protein peptide (SPRWYFYYL) recognized by CD8+ T-cells in the context of HLA-B7+. SARS-Arena is available at https://github.com/KavrakiLab/SARS-Arena.
   Details
2. R. F. Tarabini, M. M. Rigo, A. Faustino Fonseca, F. Rubin, R. Bellé, L. E. Kavraki, T. C. Ferreto, D. Amaral Antunes, and A. P. D. de Souza, “Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving Peptide-Targets From BCG Vaccine and SARS-CoV-2,” Frontiers in Immunology, vol. 12, Jan. 2022.
   
   pdf publisher bibtex abstract details

   BibTeX

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   @article{tarabini2022-large-scale,
     title = {Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving
         Peptide-Targets From BCG Vaccine and SARS-CoV-2},
     author = {Tarabini, Renata Fioravanti and Rigo, Mauricio Menegatti and Faustino Fonseca, André and Rubin, Felipe and Bellé, Rafael and Kavraki, Lydia E and Ferreto, Tiago Coelho and Amaral Antunes, Dinler and de Souza, Ana Paula Duarte},
     journal = {Frontiers in Immunology},
     month = jan,
     year = {2022},
     volume = {12},
     doi = {10.3389/fimmu.2021.812176},
     abstract = {Although not being the first viral pandemic to affect humankind, we are now for the
         first time faced with a pandemic caused by a coronavirus. The Severe Acute Respiratory
         Syndrome Coronavirus 2 (SARS-CoV-2) has been responsible for the COVID-19 pandemic, which
         caused more than 4.5 million deaths worldwide. Despite unprecedented efforts, with vaccines
         being developed in a record time, SARS-CoV-2 continues to spread worldwide with new variants
         arising in different countries. Such persistent spread is in part enabled by public
         resistance to vaccination in some countries, and limited access to vaccines in other
         countries. The limited vaccination coverage, the continued risk for resistant variants, and
         the existence of natural reservoirs for coronaviruses, highlight the importance of
         developing additional therapeutic strategies against SARS-CoV-2 and other coronaviruses. At
         the beginning of the pandemic it was suggested that countries with Bacillus Calmette-Guérin
         (BCG) vaccination programs could be associated with a reduced number and/or severity of
         COVID-19 cases. Preliminary studies have provided evidence for this relationship and further
         investigation is being conducted in ongoing clinical trials. The protection against
         SARS-CoV-2 induced by BCG vaccination may be mediated by cross-reactive T cell lymphocytes,
         which recognize peptides displayed by class I Human Leukocyte Antigens (HLA-I) on the
         surface of infected cells. In order to identify potential targets of T cell
         cross-reactivity, we implemented an in silico strategy combining sequence-based and
         structure-based methods to screen over 13,5 million possible cross-reactive peptide pairs
         from BCG and SARS-CoV-2. Our study produced (i) a list of immunogenic BCG-derived peptides
         that may prime T cell cross-reactivity against SARS-CoV-2, (ii) a large dataset of modeled
         peptide-HLA structures for the screened targets, and (iii) new computational methods for
         structure-based screenings that can be used by others in future studies. Our study expands
         the list of BCG peptides potentially involved in T cell cross-reactivity with
         SARS-CoV-2-derived peptides, and identifies multiple high-density "neighborhoods" of
         cross-reactive peptides which could be driving heterologous immunity induced by BCG
         vaccination, therefore providing insights for future vaccine development efforts.},
     issn = {1664-3224},
     publisher = {Frontiers Media SA},
     url = {http://dx.doi.org/10.3389/fimmu.2021.812176}
   }
   

   Abstract

   ×

   Although not being the first viral pandemic to affect humankind, we are now for the first time faced with a pandemic caused by a coronavirus. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been responsible for the COVID-19 pandemic, which caused more than 4.5 million deaths worldwide. Despite unprecedented efforts, with vaccines being developed in a record time, SARS-CoV-2 continues to spread worldwide with new variants arising in different countries. Such persistent spread is in part enabled by public resistance to vaccination in some countries, and limited access to vaccines in other countries. The limited vaccination coverage, the continued risk for resistant variants, and the existence of natural reservoirs for coronaviruses, highlight the importance of developing additional therapeutic strategies against SARS-CoV-2 and other coronaviruses. At the beginning of the pandemic it was suggested that countries with Bacillus Calmette-Guérin (BCG) vaccination programs could be associated with a reduced number and/or severity of COVID-19 cases. Preliminary studies have provided evidence for this relationship and further investigation is being conducted in ongoing clinical trials. The protection against SARS-CoV-2 induced by BCG vaccination may be mediated by cross-reactive T cell lymphocytes, which recognize peptides displayed by class I Human Leukocyte Antigens (HLA-I) on the surface of infected cells. In order to identify potential targets of T cell cross-reactivity, we implemented an in silico strategy combining sequence-based and structure-based methods to screen over 13,5 million possible cross-reactive peptide pairs from BCG and SARS-CoV-2. Our study produced (i) a list of immunogenic BCG-derived peptides that may prime T cell cross-reactivity against SARS-CoV-2, (ii) a large dataset of modeled peptide-HLA structures for the screened targets, and (iii) new computational methods for structure-based screenings that can be used by others in future studies. Our study expands the list of BCG peptides potentially involved in T cell cross-reactivity with SARS-CoV-2-derived peptides, and identifies multiple high-density "neighborhoods" of cross-reactive peptides which could be driving heterologous immunity induced by BCG vaccination, therefore providing insights for future vaccine development efforts.
   Details
3. S. Hall-Swan, D. Devaurs, M. M. Rigo, D. A. Antunes, L. E. Kavraki, and G. Zanatta, “DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins,” Computers in Biology and Medicine, vol. 139, p. 104943, 2021.
   
   pdf publisher bibtex abstract details

   BibTeX

   ×

   @article{hall-swan2021-dinc-covid,
     title = {DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins},
     author = {Hall-Swan, Sarah and Devaurs, Didier and Rigo, Mauricio M. and Antunes, Dinler A. and Kavraki, Lydia E. and Zanatta, Geancarlo},
     journal = {Computers in Biology and Medicine},
     year = {2021},
     volume = {139},
     pages = {104943},
     doi = {https://doi.org/10.1016/j.compbiomed.2021.104943},
     abstract = {An unprecedented research effort has been undertaken in response to the ongoing
         COVID-19 pandemic. This has included the determination of hundreds of crystallographic
         structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large
         compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had
         very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason
         might be an often overlooked factor in these computational efforts: receptor flexibility. To
         address this issue we have implemented a computational tool for ensemble docking with
         SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations
         from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve
         pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for
         ensemble docking via a user-friendly webserver called DINC-COVID
         (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of
         two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding
         energies and experimentally-determined binding affinities. Some of the best results have
         been obtained on a dataset of large ligands resolved via room temperature crystallography,
         and therefore capturing alternative receptor conformations. In addition, we have shown that
         the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and
         that this diversity is useful in finding alternative binding modes of ligands. Overall, our
         work highlights the importance of accounting for receptor flexibility in docking studies,
         and provides a platform for the identification of new inhibitors against SARS-CoV-2
         proteins.},
     issn = {0010-4825},
     keyword = {COVID-19, SARS-CoV-2, Molecular docking, Ensemble docking, Receptor flexibility,
         Molecular dynamics},
     url = {https://www.sciencedirect.com/science/article/pii/S001048252100737X}
   }
   

   Abstract

   ×

   An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.
   Details