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and monitor the transition from the folded to the unfolded state triggered by temperature or a denaturant. Transition midpoints (such as “melting temperatures”) are derived and provide a relative measure for the conformational stability. Due to low material consumption and a good signal-to-noise ratio spectroscopic or calorimetric means are frequently used to probe the transition.


Temperature stress is also exerted to monitor the transition of the biologic from the native to the aggregated state. This transition displays the macroscopic net-effect of consecutive microscopic steps. Thus, it also provides valuable information on aggregation resistance.


For antibodies to aggregate also their self-association tendency is crucial. Self-association (also in the folded state) is mediated by hydrophobic patches and surface charges.24, 25


Exposed tryptophans


contribute to the surface hydrophobicity of antibodies and the degree of exposure can be estimated from the tryptophan fluorescence spectrum. The net-effect of antibody self-association can be analyzed with methods borrowed from colloid science. In contrast to the specific binding events for enzyme-substrate complexes or antibody-antigen complexes, the self-association of biologics is a weak attraction. Therefore, the classical binding assays in biochemistry widely fail to quantify self-association events. Instead, light scattering experiments, analytical ultracentrifugation and self-interaction chromatography are used to determine interaction parameters such as the kD and the osmotic second virial coefficient.


The tendency for chemical degradation can be studied in accelerated stability studies. Here degradation is accelerated by choosing temperatures higher than the intended storage temperature and degradation products are analyzed typically after a few days. Mass spectrometry is most powerful in identifying the specific amino acids prone to chemical degradation. However, the method requires substantial effort and investment. Chromatographic methods allow for a simplified analysis that however does not provide specific information about the degrading residues. For both methods data analysis and the interpretation of results is facilitated when the residues prone to degradation have already been pre-selected by computational tools.


Conclusions


The synergistic use of computational and experimental methods working with small of amounts of material is a powerful way to support candidate selection and to provide valuable insights for formulation development. Moreover, computational methods can be used to map undesired properties to distinct sequence patterns which can be used to guide protein engineering for improved long-term stability and to identify developability risks.


Acknowledgements


We would like to thank Julia Kasper for fruitful discussions and careful reading of the manuscript.


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