SARS-CoV-2
SARS-CoV-2 analyzes spike glycoprotein epitopes, predicts viral–host protein interactions, and designs antibody Fv candidates to support vaccine, therapeutic, and epidemiological research on SARS-CoV-2.
Key Features:
- Energetics-Based Epitope Screening: Performs immuno-informatics analysis to identify energetically favorable epitopes on the SARS-CoV-2 spike glycoprotein.
- Computational Prediction of Interactome: Predicts interactions between SARS-CoV-2 proteins and human proteins to identify potential therapeutic targets.
- Artificial Intelligence for Immunogenic Landscape Prediction: Leverages AI to predict regions of the virus that are likely to be immunogenic.
- De Novo Antibody Design: Designs high-affinity antibody variable regions (Fv) targeting the SARS-CoV-2 spike protein for monoclonal antibody development.
- Patient Characteristics Identification: Analyzes serological screening data to identify patient characteristics associated with infection outcomes, including kidney transplant patients.
Scientific Applications:
- Vaccine Development: Identifies key epitopes and immunogenic regions to inform antigen selection for vaccine design.
- Therapeutic Design: Informs discovery of antiviral targets and supports development of monoclonal antibodies and other therapeutics via predicted viral–host interactions and designed Fv candidates.
- Epidemiological Studies: Analyzes serological and clinical data to characterize transmission dynamics and risk factors for severe outcomes, including in kidney transplant patients.
Methodology:
Applies immuno-informatics, machine learning/AI, and structural bioinformatics to analyses using datasets from genomic sequences, protein structures, and clinical studies.
Topics
Details
- License:
- CC-BY-4.0
- Added:
- 1/18/2021
- Last Updated:
- 11/24/2024
Operations
Publications
Dick K, Biggar KK, Green JR. Computational Prediction of the Comprehensive SARS-CoV-2 vs. Human Interactome to Guide the Design of Therapeutics. Unknown Journal. 2020. doi:10.1101/2020.03.29.014381.
Malone B, Simovski B, Moliné C, Cheng J, Gheorghe M, Fontenelle H, Vardaxis I, Tennøe S, Malmberg J, Stratford R, Clancy T. Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs. Unknown Journal. 2020. doi:10.1101/2020.04.21.052084.
Docherty A, Harrison E, Green C, Hardwick H, Pius R, Norman L, Holden K, Read J, Dondelinger F, Carson G, Merson L, Lee J, Plotkin D, Sigfrid L, Halpin S, Jackson C, Gamble C, Horby P, Nguyen-Van-Tam J, Dunning J, Openshaw P, Baillie J, Semple M. Features of 16,749 hospitalised UK patients with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol. Unknown Journal. 2020. doi:10.1101/2020.04.23.20076042.
Kumar A, Faiq MA, Pareek V, Raza K, Narayan RK, Prasoon P, Kumar P, Kulandhasamy M, Kumari C, Kant K, Singh HN, Qadri R, Pandey SN, Kumar S. Relevance of SARS-CoV-2 related factors ACE2 and TMPRSS2 expressions in gastrointestinal tissue with pathogenesis of digestive symptoms, diabetes-associated mortality, and disease recurrence in COVID-19 patients. Unknown Journal. 2020. doi:10.1101/2020.04.14.040204.
Banerjee A, Santra D, Maiti S. Energetics based epitope screening in SARS CoV-2 (COVID 19) spike glycoprotein by Immuno-informatic analysis aiming to a suitable vaccine development. Unknown Journal. 2020. doi:10.1101/2020.04.02.021725.
Qian X, Ren R, Wang Y, Guo Y, Fang J, Wu Z, Liu P, Han T. Fighting against the common enemy of COVID-19: a practice of building a community with a shared future for mankind. Infectious Diseases of Poverty. 2020;9(1). doi:10.1186/s40249-020-00650-1. PMID:32264957. PMCID:PMC7137400.
Boorla VS, Chowdhury R, Maranas CD. <i>De novo</i> design of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike protein. Unknown Journal. 2020. doi:10.1101/2020.04.09.034868.