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Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling
Published on: April 1, 2017
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Targeted purification development enabled by computational biophysical modeling
Francis K Insaidoo1, Michael A Rauscher, Shepard J Smithline
1Merck Research Laboratories, Merck & Co., Inc, Whitehouse Station, NJ, 08889.
Biotechnology Progress
|December 9, 2014
Summary
Computational models using atomic-level detail can predict and improve biopharmaceutical purification, specifically for insulin variants. This approach enhances understanding of protein-ligand interactions for more robust bioprocess development.
Area of Science:
- Biochemistry
- Computational Chemistry
- Chemical Engineering
Background:
- Biopharmaceutical purification relies on protein-surface interactions, influenced by protein properties and critical for efficacy.
- Existing computational methods like QSAR/QSPR have limitations in discerning subtle protein-ligand differences for purification.
- Understanding these interactions is key for developing effective purification strategies that maintain structural integrity and pharmacological activity.
Purpose of the Study:
- To develop and apply atomic-level computational models for predicting protein-ligand interactions in biopharmaceutical purification.
- To overcome limitations of previous modeling approaches by incorporating detailed atomic interactions.
- To enhance the understanding and efficiency of chromatographic and non-chromatographic separation techniques for biologics.
Main Methods:
- Utilized atomic-level detail for modeling protein-ligand interactions, extending drug target discovery principles.
- Applied computational models to the purification of different commercially available insulin variants.
- Correlated computational model predictions with empirical observations across various purification challenges.
Main Results:
- Demonstrated the ability of atomic-level computational models to correlate directionally with empirical observations for insulin purification.
- Successfully applied the methodology to resolve subtle product variants, such as amino acid misincorporations.
- Validated the predictive power of the models for different insulin systems and purification conditions.
Conclusions:
- Atomic-level computational modeling provides a powerful tool for understanding and optimizing biopharmaceutical purification processes.
- This methodology can enhance the development of robust purification platforms by accurately predicting protein-ligand interactions.
- Broader application in bioprocess development may significantly speed up the creation of efficient and effective purification strategies.
