Proteins are biological macromolecules. For use in therapeutics, diagnostics or the food industry, proteins need to be purified from complex matrices consisting of thousands of molecules including other proteins. Due to numerous options in chromatography, modeling such purification can substantially reduce experimental efforts during process development and thus safe costs.

Scalar descriptors are one way to capture complex object properties, such as the diameter, overall charge etc. of proteins. However, current descriptors fall short of capturing the complex features of proteins like the uneven surface charge distribution due to the anisotropic distribution of these properties and the non-spherical shape of proteins,

There were several priorities to this project: Protein surfaces were exported along with the corresponding charge distribution in a format amenable for further automated processing. Furthermore, a Laplace-Beltrami spectral analysis was implemented, which can capture complex object features. Additionally, an algorithm to calculate the corresponding local and global descriptors was developed. Finally, these new descriptors were applied in chromatographic model building. Using these new descriptors, the model predictions of steric mass action (SMA) model isotherm parameters were improved.

The descriptor approach used here holds potential for standardization, because a uniform approach for the description of complex objects has been established that can be used across domains, for example for protein as well as for turbines and sieves. For this, the applicability to other complex objects would have to be further investigated.