A multiscale investigation of protein affinity and displacer efficacy in chromatographic systems using quantitative structure-property relationship modeling

Methods development for a given protein separation often entails the initial screening of various classes of stationary phase materials and mobile phase conditions in order to identify chromatographic conditions with sufficient selectivity. In this thesis, high throughput experimentation is employed in concert with state-of-the-art structure-property modeling approaches to address several challenges in bioseparations methods development.; High throughput screening (HTS) can enable the rapid identification of displacers for the purification of protein mixtures by displacement chromatography. In the present work, HTS is employed to screen: (1) displacer libraries to identify selective and high-affinity displacers for protein mixtures in ion-exchange systems, and (2) a library of novel aminoglycoside polyamines for their ability to bind to DNA for nucleic acid purification applications. In addition, a novel multi-dimensional high-throughput screening (MD-HTS) approach is developed for examining the influence of various operating parameters on displacer selectivity and for the identification of potential displacers and conditions for ion-exchange displacement separations of biological mixtures. The HTS data are also used to generate quantitative structure-property relationship (QSPR) models to predict displacer efficacy or DNA-binding affinity of untested molecules. Finally, model interpretation is employed to understand the physicochemical basis of displacer selectivity and to identify the characteristics of efficacious DNA-compaction agents.; Structure-property modeling is also employed for the a priori prediction of protein retention in ion-exchange and hydrophobic interaction chromatography (HIC). These models are used to gain insight into the influence of the mobile phase salt counterion on the binding affinity of proteins in cation exchange chromatography and the role of the stationary phase resin in influencing protein retention in HIC. In addition, a novel "multiscale" modeling strategy is developed, which combines the generation of predictive QSPR models for isotherm parameters with traditional chromatographic transport models to predict ion-exchange column performance directly from protein structure data. The synergistic use of these molecular and macroscopic modeling techniques provides a unique opportunity to develop powerful predictive tools and methods for gaining insight into the fundamental physics of the protein adsorption process in different chromatographic modes.