Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering
Year: 2020
Type: article
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.
Institution: University of Michigan
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,472
Awards: IIBR: Informatics: RAPID: Genome-wide Structure and Function Modeling of the SARS-CoV-2 Virus, III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction, G1492819423, G2117560495, G2232767831, G2464480468, G278929574, G3260064559, Advanced approaches to protein structure prediction, G4584606584, G4630214672, G4913448354, G5964975137, Ontology-based Information Network to Support Vaccine Research, G7063014021, G7637742026, G7897283973, G8151168852, High-Performance Computing Cluster for Biomedical Research, G8702286652, Structure-based functional annotation of microbial genomes
OpenAlex ID: W3115778640