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Evaluation of an Affinity-Enhanced Anti-SARS-CoV2 Nanobody Design Workflow Using Machine Learning and Molecular
Zsolt Fazekas1,2, Dóra Nagy-Fazekas1,2, Boglárka Mária Shilling-Tóth3
1Hevesy György PhD School of Chemistry, Institute of Chemistry, Eötvös Loránd University, Budapest, Pázmány Péter sétány. 1/A, Budapest H-1117, Hungary.
Journal of Chemical Information and Modeling
|October 2, 2024
Summary
This study presents a machine learning workflow for designing nanobodies targeting the SARS-CoV-2 spike protein receptor binding domain (S-RBD). The workflow successfully identified enhanced nanobody binders for both wild-type and delta variants.
Area of Science:
- * Computational biology and bioinformatics.
- * Protein engineering and drug design.
- * Infectious disease research and vaccine development.
Background:
- * In silico protein binding optimization is increasingly preferred over in vitro methods due to speed and cost-effectiveness.
- * Advances in hardware and machine learning have enhanced the accessibility of in silico strategies.
- * These computational approaches have played a role in responding to global health crises like pandemics.
Purpose of the Study:
- * To propose and evaluate a workflow for designing nanobodies against the SARS-CoV-2 spike protein receptor binding domain (S-RBD).
- * To utilize machine learning techniques combined with molecular dynamics simulations for nanobody design.
- * To assess the feasibility of the workflow through experimental validation of designed nanobodies.
Main Methods:
- * Employed a workflow integrating machine learning and molecular dynamics simulations for nanobody design.
- * Tested the workflow using three nanobodies and two S-RBD variants, covering in silico design, bacterial expression, and binding assays.
- * Validated designed nanobody mutants against wild-type and delta variant S-RBD.
Main Results:
- * Successfully designed novel nanobodies targeting the SARS-CoV-2 S-RBD.
- * Identified two designed nanobodies exhibiting stronger binding affinity compared to the wild-type nanobody.
- * Demonstrated the effectiveness of the in silico assisted design strategy through experimental validation.
Conclusions:
- * The proposed in silico workflow is a feasible and effective strategy for designing high-affinity nanobodies against viral targets like SARS-CoV-2 S-RBD.
- * The study highlights both the advantages and limitations of using computational methods in nanobody design.
- * This approach holds promise for accelerating the development of therapeutics against emerging infectious diseases.
