ArticleComputational and structural biotechnology journal2021
Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 23 citations in OpenAlex.
- CD4+ T cells from COVID-19 mRNA vaccine recipients recognize a conserved epitope present in diverse coronaviruses.The Journal of clinical investigation · 2022Trial
- CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.Viruses · 2026Article
- Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation.bioRxiv : the preprint server for biology · 2023Article
- Computational Protein Design for COVID-19 Research and Emerging Therapeutics.ACS central science · 2023Review
- Investigation of the effects ofComputational & theoretical chemistry · 2023Article
- Improved prediction of MHC-peptide binding using protein language models.Frontiers in bioinformatics · 2023Article
- COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.Briefings in bioinformatics · 2022Article
- First computational design using lambda-superstrings and in vivo validation of SARS-CoV-2 vaccine.Scientific reports · 2022Article
- COVID-19 Vaccines: Current and Future Perspectives.Vaccines · 2022Review
- Article
- Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.Nucleic acids research · 2021Article
- Deep learning techniques have significantly impacted protein structure prediction and protein design.Current opinion in structural biology · 2021Review
- COVID-19 Vaccines: Current Understanding on Immunogenicity, Safety, and Further Considerations.Frontiers in immunology · 2021Review
- SARS-CoV-2 Vaccines Based on the Spike Glycoprotein and Implications of New Viral Variants.Frontiers in immunology · 2021Review
- Lessons Learned from Cutting-Edge Immunoinformatics on Next-Generation COVID-19 Vaccine Research.International journal of peptide research and therapeutics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the association of its Spike (S) protein with the human angiotensin-converting enzyme 2 (ACE2) receptor to invade host cells, we computationally redesigned the S protein sequence to improve its immunogenicity and antigenicity. Toward this purpose, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants that perturb the core protein sequence but keep the surface conformation and B cell epitopes. The T cell epitope content and similarity scores of the perturbed sequences were calculated and evaluated. Out of 22,914 designs with favorable stability energy, 301 candidates contained at least two pre-existing immunity-related epitopes and had promising immunogenic potential. The benchmark tests showed that, although the epitope restraints were not included in the scoring function of EvoDesign, the top S protein design successfully recovered 31 out of the 32 major histocompatibility complex (MHC)-II T cell promiscuous epitopes in the native S protein, where two epitopes were present in all seven human coronaviruses. Moreover, the newly designed S protein introduced nine new MHC-II T cell promiscuous epitopes that do not exist in the wildtype SARS-CoV-2. These results demonstrated a new and effective avenue to enhance a target protein's immunogenicity using rational protein design, which could be applied for new vaccine design against COVID-19 and other pathogens.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.