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Automated Optimisation of Solubility and Conformational Stability of Antibodies and Proteins

Overview
Journal Nat Commun
Specialty Biology
Date 2023 Apr 6
PMID 37024501
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Abstract

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

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References
1.
Carter P, Lazar G . Next generation antibody drugs: pursuit of the 'high-hanging fruit'. Nat Rev Drug Discov. 2017; 17(3):197-223. DOI: 10.1038/nrd.2017.227. View

2.
Hansel T, Kropshofer H, Singer T, Mitchell J, George A . The safety and side effects of monoclonal antibodies. Nat Rev Drug Discov. 2010; 9(4):325-38. DOI: 10.1038/nrd3003. View

3.
Mader A, Tydykov L, Gluck V, Bertok M, Weidlich T, Gottwald C . Omicron's binding to sotrovimab, casirivimab, imdevimab, CR3022, and sera from previously infected or vaccinated individuals. iScience. 2022; 25(4):104076. PMC: 8920075. DOI: 10.1016/j.isci.2022.104076. View

4.
Pallares I, Ventura S . Understanding and predicting protein misfolding and aggregation: Insights from proteomics. Proteomics. 2016; 16(19):2570-2581. DOI: 10.1002/pmic.201500529. View

5.
Broom A, Jacobi Z, Trainor K, Meiering E . Computational tools help improve protein stability but with a solubility tradeoff. J Biol Chem. 2017; 292(35):14349-14361. PMC: 5582830. DOI: 10.1074/jbc.M117.784165. View