Potential neutralizing antibodies discovered for novel corona virus using machine learning
Open accessPublished version of a preprint
Abstract
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.
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The workflow using ML would enable rapid screening of potential antibodies that support COVID-19 treatment with high confidence.
“This workflow using ML would enable the rapid screening of potential antibodies that support the treatment of COVID-19 with high confidence.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [115]
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- Authors
- Magar, R., Yadav, P., & Barati Farimani, A.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Scientific Reports
- Published
- 2021-03-04
- Volume
- 11
- Issue
- 1
- Pages
- 5261-5261
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7970853
- OpenAlex
- W3012111442
- Cited by
- 114
- References
- 6
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 16 · 2022: 25 · 2021: 30 · 2020: 25
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Center of Machine Learning in Health (47247.1.5007162)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- SARS-CoV-2, neutralizing antibodies, COVID-19 pandemic, synthetic antibodies, antibody stability, molecular dynamics simulation, machine learning prediction
- MeSH terms
- Machine Learning, SARS-CoV-2, Antibodies, Neutralizing, High-Throughput Screening Assays
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