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Investigation of protein retention and selectivity in HIC systems using quantitative structure retention relationship models
Author(s) -
Ladiwala Asif,
Xia Fang,
Luo Qiong,
Breneman Curt M.,
Cramer Steven M.
Publication year - 2005
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.20771
Subject(s) - chemistry , selectivity , support vector machine , hydrophilic interaction chromatography , chromatography , ligand (biochemistry) , molecular descriptor , agarose , accessible surface area , quantitative structure–activity relationship , high performance liquid chromatography , machine learning , organic chemistry , computational chemistry , stereochemistry , computer science , receptor , biochemistry , catalysis
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. © 2005 Wiley Periodicals, Inc.