Identification of Protein–Excipient Interaction Hotspots Using Computational Approaches
AbstractProtein formulation development relies on the selection of excipients that inhibit protein–protein interactions preventing aggregation. Empirical strategies involve screening many excipient and buffer combinations using force degradation studies. Such methods do not readily provide information on intermolecular interactions responsible for the protective effects of excipients. This study describes a molecular docking approach to screen and rank interactions allowing for the identification of protein–excipient hotspots to aid in the selection of excipients to be experimentally screened. Previously published work with Drosophila Su(dx) was used to develop and validate the computational methodology, which was then used to determine the formulation hotspots for Fab A33. Commonly used excipients were examined and compared to the regions in Fab A33 prone to protein–protein interactions that could lead to aggregation. This approach could provide information on a molecular level about the protective interactions of excipients in protein formulations to aid the more rational development of future formulations. View Full-Text
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Barata, T.S.; Zhang, C.; Dalby, P.A.; Brocchini, S.; Zloh, M. Identification of Protein–Excipient Interaction Hotspots Using Computational Approaches. Int. J. Mol. Sci. 2016, 17, 853.
Barata TS, Zhang C, Dalby PA, Brocchini S, Zloh M. Identification of Protein–Excipient Interaction Hotspots Using Computational Approaches. International Journal of Molecular Sciences. 2016; 17(6):853.Chicago/Turabian Style
Barata, Teresa S.; Zhang, Cheng; Dalby, Paul A.; Brocchini, Steve; Zloh, Mire. 2016. "Identification of Protein–Excipient Interaction Hotspots Using Computational Approaches." Int. J. Mol. Sci. 17, no. 6: 853.
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