263169 Proteomics Based Multivariate Random Forest Method for Prediction of Protein Separation Behavior During Downstream Purification

Monday, October 29, 2012: 9:42 AM
Westmoreland East (Westin )
Ryan K. Swanson1, Ruo Xu2, Dan Nettleton2 and Charles E. Glatz1, (1)Chemical and Biological Engineering, Iowa State University, Ames, IA, (2)Statistics, Iowa State University, Ames, IA

Proteomics based multivariate random forest method for prediction of protein separation behavior during downstream purification

Ryan K. Swanson1, Ruo Xu2, Dan Nettleton2 and Charles E. Glatz1

1Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011

2Department of Statistics, Iowa State University, Ames, IA 50011

Optimizing the downstream purification process remains a significant challenge when using biological expression hosts for the production of recombinant proteins. An important reason why is due to the lack of knowledge of the host cell proteins' (HCP) separation behavior during the selected purification methods. The work to be presented addresses this issue by modeling the separation behavior of a HCP extract from an arbitrary expression host (corn) during commonly used chromatographic and non-chromatographic methods. Aqueous two phase system (ATPS) partitioning followed by two dimensional electrophoresis (2DE) provided data on the three physicochemical properties most commonly exploited during downstream purification for each HCP; isoelectric point, molecular weight and surface hydrophobicity. A multivariate random forest (MVRF) statistical methodology was then applied to this database of characterized proteins creating an accurate tool for predicting the separation behavior of a mixture of proteins for three commonly selected purification methods; cation exchange chromatography (CEX), hydrophobic interaction chromatography (HIC) and ammonium sulfate precipitation (ASP). The next logical goal will be to link these three methods in series (i.e. ASP followed by HIC followed by CEX) and test the prediction accuracy of the MVRF methodology during this simulated downstream purification process. Also discussed will be the future capabilities of this work including the ability to optimize a downstream purification process without requiring any initial product or time spent screening potential methods in the lab regardless of expression host.


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