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Automated optimisation of solubility and conformational stability of antibodies and proteins

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dc.contributor.author Rosace, Angelo
dc.contributor.author Bennett, Anja
dc.contributor.author Oeller, Marc
dc.contributor.author Mortensen, Mie M.
dc.contributor.author Sakhnini, Laila
dc.contributor.author Lorenzen, Nikolai
dc.contributor.author Poulsen, Christian
dc.contributor.author Sormanni, Pietro
dc.date.accessioned 2023-09-21T07:01:45Z
dc.date.available 2023-09-21T07:01:45Z
dc.date.issued 2023
dc.identifier.citation Rosace A, Bennett A, Oeller M, Mortensen MM, Sakhnini L, Lorenzen N, Poulsen C, Sormanni P. Automated optimisation of solubility and conformational stability of antibodies and proteins. Nat Commun. 2023;14:1937. DOI: 10.1038/s41467-023-37668-6
dc.identifier.issn 2041-1723
dc.identifier.uri http://hdl.handle.net/10230/57934
dc.description.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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Nature Research
dc.relation.ispartof Nat Commun. 2023;14:1937
dc.rights © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Automated optimisation of solubility and conformational stability of antibodies and proteins
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1038/s41467-023-37668-6
dc.subject.keyword Biophysical chemistry
dc.subject.keyword Computational science
dc.subject.keyword Drug development
dc.subject.keyword Protein design
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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