Automated optimisation of solubility and conformational stability of antibodies and proteins

Angelo Rosace, Anja Bennett, Marc Oeller, Mie M. Mortensen, Laila Sakhnini, Nikolai Lorenzen, Christian Poulsen and Pietro Sormanni ()
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Angelo Rosace: University of Cambridge
Anja Bennett: University of Cambridge
Marc Oeller: University of Cambridge
Mie M. Mortensen: Global Research Technologies, Novo Nordisk A/S
Laila Sakhnini: University of Cambridge
Nikolai Lorenzen: Global Research Technologies, Novo Nordisk A/S
Christian Poulsen: Global Research Technologies, Novo Nordisk A/S
Pietro Sormanni: University of Cambridge

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37668-6

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