Boosting antibody developability through rational sequence optimization
Autor: | Patrick Schulz, Joey Studts, Anne R. Karow, Stefan Hoerer, Tobias Litzenburger, Patrick Garidel, Barbara Enenkel, Daniel Seeliger, Julia Spitz, Michaela Blech |
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Rok vydání: | 2015 |
Předmět: |
Boosting (machine learning)
biology Sequence analysis Chemistry medicine.drug_class Protein Stability Immunology Sequence optimization Stability (learning theory) Antibodies Monoclonal Nanotechnology Computational biology Protein engineering Monoclonal antibody Protein Engineering medicine biology.protein Immunology and Allergy Computational design Humans Amino Acid Sequence Antibody Reports |
Zdroj: | mAbs. 7(3) |
ISSN: | 1942-0870 |
Popis: | The application of monoclonal antibodies as commercial therapeutics poses substantial demands on stability and properties of an antibody. Therapeutic molecules that exhibit favorable properties increase the success rate in development. However, it is not yet fully understood how the protein sequences of an antibody translates into favorable in vitro molecule properties. In this work, computational design strategies based on heuristic sequence analysis were used to systematically modify an antibody that exhibited a tendency to precipitation in vitro. The resulting series of closely related antibodies showed improved stability as assessed by biophysical methods and long-term stability experiments. As a notable observation, expression levels also improved in comparison with the wild-type candidate. The methods employed to optimize the protein sequences, as well as the biophysical data used to determine the effect on stability under conditions commonly used in the formulation of therapeutic proteins, are described. Together, the experimental and computational data led to consistent conclusions regarding the effect of the introduced mutations. Our approach exemplifies how computational methods can be used to guide antibody optimization for increased stability. |
Databáze: | OpenAIRE |
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