The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2
Open accessAbstract
Summary Effective countermeasures against the recent emergence and rapid expansion of the 2019 novel coronavirus (SARS-CoV-2) require the development of data and tools to understand and monitor its spread and immune responses to it. However, little information is available about the targets of immune responses to SARS-CoV-2. We used the Immune Epitope Database and Analysis Resource (IEDB) to catalog available data related to other coronaviruses. This includes SARS-CoV, which has high sequence similarity to SARS-CoV-2 and is the best-characterized coronavirus in terms of epitope responses. We identified multiple specific regions in SARS-CoV-2 that have high homology to the SARS-CoV virus. Parallel bioinformatic predictions identified a priori potential B and T cell epitopes for SARS-CoV-2. The independent identification of the same regions using two approaches reflects the high probability that these regions are promising targets for immune recognition of SARS-CoV-2. These predictions can facilitate effective vaccine design against this virus of high priority.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
SWISS-MODEL: homology modelling of protein structures and complexes
Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T. A. P., Rempfer, C., Bordoli, L., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky427 · Cited by 13,067
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020) · Science · doi:10.1126/science.abb2507 · Cited by 8,897
SARS and MERS: recent insights into emerging coronaviruses
de Wit, E., van Doremalen, N., Falzarano, D., & Munster, V. J. (2016) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2016.81 · Cited by 3,510
Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China
Wu, A., Peng, Y., Huang, B., Ding, X., Wang, X., Niu, P., Meng, J., Zhu, Z., Zhang, Z., Wang, J., et al. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.02.001 · Cited by 2,361
The Immune Epitope Database (IEDB): 2018 update
Vita, R., Mahajan, S., Overton, J. A., Dhanda, S. K., Martini, S., Cantrell, J. R., Wheeler, D. K., Sette, A., & Peters, B. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1006 · Cited by 2,186
Self-citationBepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes
Jespersen, M. C., Peters, B., Nielsen, M., & Marcatili, P. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx346 · Cited by 1,746
Self-citationYASARA View—molecular graphics for all devices—from smartphones to workstations
Krieger, E., & Vriend, G. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu426 · Cited by 1,568
Jurtz, V., Paul, S., Andreatta, M., Marcatili, P., Peters, B., & Nielsen, M. (2017) · The Journal of Immunology · doi:10.4049/jimmunol.1700893 · Cited by 1,470
Self-citationFrom SARS to MERS, Thrusting Coronaviruses into the Spotlight
Song, Z., Xu, Y., Bao, L., Zhang, L., Yu, P., Qu, Y., Zhu, H., Zhao, W., Han, Y., & Qin, C. (2019) · Viruses · doi:10.3390/v11010059 · Cited by 1,265
HLA class I supertypes: a revised and updated classification
Sidney, J., Peters, B., Frahm, N., Brander, C., & Sette, A. (2008) · BMC Immunology · doi:10.1186/1471-2172-9-1 · Cited by 775
Self-citation
Cited by
Grifoni, A., Weiskopf, D., Ramirez, S. I., Mateus, J., Dan, J. M., Moderbacher, C. R., Rawlings, S. A., Sutherland, A., Premkumar, L., Jadi, R. S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.015 · Cited by 3,595
Self-citationRydyznski Moderbacher, C., Ramirez, S. I., Dan, J. M., Grifoni, A., Hastie, K. M., Weiskopf, D., Belanger, S., Abbott, R. K., Kim, C., Choi, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.038 · Cited by 1,790
Self-citationImmunology of COVID-19: Current State of the Science
Vabret, N., Britton, G. J., Gruber, C., Hegde, S., Kim, J., Kuksin, M., Levantovsky, R., Malle, L., Moreira, A., Park, M. D., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.05.002 · Cited by 1,623
Selective and cross-reactive SARS-CoV-2 T cell epitopes in unexposed humans
Mateus, J., Grifoni, A., Tarke, A., Sidney, J., Ramirez, S. I., Dan, J. M., Burger, Z. C., Rawlings, S. A., Smith, D. M., Phillips, E., et al. (2020) · Science · doi:10.1126/science.abd3871 · Cited by 1,120
Self-citationImmune response to SARS‐CoV‐2 and mechanisms of immunopathological changes in COVID‐19
Azkur, A. K., Akdis, M., Azkur, D., Sokolowska, M., van de Veen, W., Brüggen, M., O’Mahony, L., Gao, Y., Nadeau, K., & Akdis, C. A. (2020) · Allergy · doi:10.1111/all.14364 · Cited by 1,119
Metadata
- Authors
- Grifoni, A., Sidney, J., Zhang, Y., Scheuermann, R. H., Peters, B., & Sette, A.
- Venue
- Cell Host & Microbe
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 27
- Issue
- 4
- Pages
- 671-680.e2
- ISSN
- 1931-3128 (ISSN-L)
- OpenAlex
- W3011488280
- Cited by
- 940
- References
- 34
- FWCI
- 57.74
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2. https://app.cochat.ai/papers/10.1016/j.chom.2020.03.002
- The paper
- Grifoni, A., Sidney, J., Zhang, Y., Scheuermann, R. H., Peters, B., & Sette, A. (2020). A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2. Cell Host & Microbe. https://doi.org/10.1016/j.chom.2020.03.002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
