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Detection of SARS-CoV-2 Neutralizing Antibodies using High-Throughput Fluorescent Imaging of Pseudovirus Infection
Published on: June 5, 2021
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Potential neutralizing antibodies discovered for novel corona virus using machine learning
Rishikesh Magar1, Prakarsh Yadav2, Amir Barati Farimani3,4,5
1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Scientific Reports
|March 5, 2021
Summary
Machine learning models identified nine stable synthetic antibodies capable of inhibiting SARS-CoV-2. This high-throughput approach accelerates the discovery of neutralizing antibodies to combat viral threats like COVID-19.
Area of Science:
- Immunology
- Computational Biology
- Virology
Background:
- Rapid virus mutations, like those in SARS-CoV-2, pose significant threats due to delayed immune responses.
- Developing effective neutralizing antibodies is crucial for controlling viral outbreaks and saving lives.
Purpose of the Study:
- To develop a high-throughput method for predicting neutralizing antibodies against SARS-CoV-2.
- To identify stable, synthetic antibody sequences with potential inhibitory effects on the virus.
Main Methods:
- Utilized machine learning (ML) models, including XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine, and Logistic Regression.
- Employed graph featurization on a dataset of 1933 virus-antibody sequences and clinical neutralization response data.
- Integrated bioinformatics, structural biology, and Molecular Dynamics (MD) simulations for candidate antibody validation.
Main Results:
- Screened thousands of hypothetical antibody sequences using various ML methods.
- Identified nine stable antibody sequences demonstrating potential to inhibit SARS-CoV-2.
- Validated the stability and inhibitory potential of candidate antibodies through multi-disciplinary approaches.
Conclusions:
- Machine learning offers a powerful tool for high-throughput prediction of neutralizing antibodies.
- The identified synthetic antibodies represent promising candidates for therapeutic development against SARS-CoV-2.
- Combining computational prediction with experimental validation accelerates the discovery of antiviral therapies.
