In the present work, the effect of stationary phase resin chemistry and protein physicochemical properties on protein binding affinity in hydrophobic interaction chromatography (HIC) was investigated using linear gradient chromatography and quantitative structure-retention relationship (QSRR) modeling. Linear gradient experiments were carried out for a set of model proteins on four different HIC resins having different backbone and ligand chemistry. The retention data exhibited significant differences in protein binding affinity, not only across the phenyl and butyl ligand chemistries, but also for the different backbone chemistries found in the Sepharose (cross-linked agarose) and the Toyopearl 650 M (polymethacrylate) series of resins. QSRR models based on a Support Vector Machine (SVM) approach were developed for the linear retention data using molecular descriptors based on protein crystal structure and primary sequence information as well as a set of new hydrophobicity descriptors based on the solvent accessible protein surface area. The results indicate that the QSRR models were successfully able to capture and selectivity predict the changes observed in these systems. Furthermore, the new descriptors resulted in physically interpretable models of protein retention and provided insights into the factors influencing protein affinity in these different HIC systems. The approach put forth in this study provides a framework for developing predictive tools and for gaining insight into protein selectivity in hydrophobic interaction chromatography.
The a priori prediction of protein adsorption behavior has been a long-standing goal in several fields. In the present work, propertymodeling techniques have been used for the prediction of protein adsorption thermodynamics in ion-exchange systems directly from crystal structure. Quantitative structure-property relationship models of protein isotherm parameters and Gibbs free energy changes in ion-exchange systems were generated by using a support vector machine regression technique. The predictive ability of the models was demonstrated for two test-set proteins not included in the model training set. Molecular descriptors selected during model generation were examined to gain insights into the important physicochemical factors influencing stoichiometry, equilibrium, steric effects, and binding affinity in protein ion-exchange systems. The a priori prediction of protein isotherm parameters can have direct implications for various ion-exchange processes. As proof of concept, a multiscale modeling approach was used for predicting the chromatographic separation of a test set of proteins using the isotherm parameters obtained from the quantitative structure-property relationship models. The simulated column separation showed good agreement with the experimental data. The ability to predict chromatographic behavior of proteins directly from their crystal structures may have significant implications for a range of biotechnology processes.ion-exchange chromatography ͉ protein adsorption ͉ structure-property relationships ͉ steric mass action ͉ support vector machines P rotein adsorption on surfaces is of fundamental importance in a wide array of applications, including biomedical implants (1), tissue engineering (2), food processing (3), biosensors (4), and bioseparations (5). In particular, protein adsorption on ionexchange surfaces has direct implications for bioseparation systems ranging from preparative chromatographic and membrane separations to high-throughput chromatographic and electrophoresisbased proteomic applications (6-8).Although protein adsorption in ion-exchange systems is dominated by Coulombic interactions, other molecular interactions such as hydrophobic and van der Waals interactions can play an important role in creating multimodal interactions and unique selectivities (9-13). Because protein binding in ion-exchange chromatography has been shown to be a reversible phenomenon with no significant associated conformational changes (14), the treatment of the protein as a colloid that reversibly adsorbs and desorbs is reasonable for modeling ion-exchange chromatographic systems. Several mechanistic models have attempted to model protein binding in ion exchange. Roth and Lenhoff (9) have computed the electrostatic and van der Waals energies of interaction between a colloidal protein molecule and a planar charged surface at a fixed distance and orientation. Roth et al. (10) The steric mass action (SMA) formalism (15, 16) has been used successfully to predict complex preparative chromatographic behavior ...
In this paper, the effect of the salt counterion on protein retention in cation-exchange systems is investigated using experimental data and quantitative structure retention relationship (QSRR) models. Linear gradient experiments were carried out in the presence of three displacing counterions (sodium, ammonium, and calcium) on two cation-exchange stationary phase materials (Fast Flow Sepharose SP and Source 15S). QSRR models based on a support vector machine regression approach were developed using the experimental chromatographic data in concert with molecular descriptors computed from protein crystal structures. The resulting QSRR models were well correlated (r 2 = 0.8452−0.9599), and the predictive power of these models was demonstrated with proteins not included in the derivation of the models. The key descriptors reflected in the models were then used to evaluate the phenomena responsible for protein retention in the presence of different salt counterions. In addition to the standard competitive binding effects of the cations, the results indicated that retention also depends on the relative ability of the cations to shield charges on the protein as well as the increased importance of hydrophobic interactions in the presence of a strong kosmotrope such as Ca2+. This work provides insight into the behavior of proteins in cation-exchange systems in the presence of various salt counterions and offers an efficient tool for the a priori prediction of protein retention.
We have recently developed a novel multivalent cationic library based on the derivatization of aminoglycosides by linear polyamines. In the current study, we describe the DNA-binding activity of this library. Screening results indicated that several candidates from the library showed high DNA-binding activities with some approaching those of cationic polymers. Quantitative Structure-Activity Relationship (QSAR) models of the screening data were employed to investigate the physicochemical effects governing polyamine-DNA binding. The utility of these models for the a priori prediction of polyamine-DNA-binding affinity was also demonstrated. Molecular descriptors selected in the QSAR modeling indicated that molecular size, basicity, methylene group spacing between amine centers, and hydrogen-bond donor groups of the polyamine ligands were important contributors to their DNA-binding efficacy. The research described in this paper has led to the development of new multivalent ligands with high DNA-binding activity and improved our understanding of structure-activity relationships involved in polyamine-DNA binding. These results have implications for the discovery of novel polyamine ligands for nonviral gene delivery, plasmid DNA purification, and anticancer therapeutics.
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