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RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery
Jonathan Parkinson1,2, Ryan Hard1, Young Su Ko1
1Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359.
Biorxiv : the Preprint Server for Biology
|August 12, 2024
Summary
The RESP2 pipeline discovers therapeutic antibodies with high affinity for multiple pathogen variants. This enhanced computational tool significantly outperforms generative AI in identifying potent antibody candidates.
Area of Science:
- Biotechnology
- Immunology
- Computational Biology
Background:
- Discovering therapeutic antibodies for infectious diseases is challenging due to pathogen mutation.
- Antibodies require high affinity for multiple antigen variants and optimal developability.
Purpose of the Study:
- To present RESP2, an enhanced computational pipeline for discovering therapeutic antibodies.
- To optimize antibody discovery for multiple antigens and developability properties simultaneously.
Main Methods:
- In silico evaluation using the Absolut! database.
- Testing RESP2 on the COVID-19 spike protein receptor binding domain (RBD).
Main Results:
- RESP2 identified antibody sequences with superior binding affinity to target antigens (>= 85% success rate).
- Generative AI methods achieved success rates of 1.5% or less on the same datasets.
- A novel human antibody demonstrated broad binding to at least 8 COVID-19 RBD variants.
Conclusions:
- RESP2 offers a significant advantage for discovering antibodies against challenging targets like mutating pathogens.
- The pipeline is available as an open-source Python package for broader research use.
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