Automated optimisation of solubility and conformational stability of antibodies and proteins
jats:titleAbstract</jats:title>jats:pBiologics, 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.</jats:p>
Acknowledgements: P.S. is a Royal Society University Research Fellow (URF\R1\201461). This work was partly funded by a Research Grant (RGS\R1\211126) from the Royal Society, by an Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge Joint Research Grant (MBAG/624 RG89305), and by UKRI EPSRC (EP/X024733/1). Biomolecular production and some of the characterisations were funded by Novo Nordisk. M.O. is a Ph.D. student funded by AstraZeneca. A.B. and M.M.M are Ph.D. students within the Novo Nordisk R&D STAR Fellowship programme and are partially funded by Innovation Fund Denmark.
Funder: Isaac Newton Trust / Wellcome Trust ISSF / University of Cambridge Joint Research Grant (MBAG/624 RG89305)