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Rensselaer Researchers Develop Approach That Predicts Protein Separation Behavior

Date:
August 24, 2005
Source:
Rensselaer Polytechnic Institute
Summary:
Applying math and computers to the drug discovery process, researchers at Rensselaer Polytechnic Institute have developed a method to predict protein separation behavior directly from protein structure. This new multi-scale protein modeling approach may reduce the time it takes to bring pharmaceuticals to market and may have significant implications for an array of biotechnology applications, including bioprocessing, drug discovery, and proteomics, the study of protein structure and function.
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A computational representation of protein 135L, with electrostatic potential encoded on its solvent accessible surface.
Credit: Image courtesy of Rensselaer Polytechnic Institute

TROY, N.Y. -- Applying math and computers to the drugdiscovery process, researchers at Rensselaer Polytechnic Institute havedeveloped a method to predict protein separation behavior directly fromprotein structure. This new multi-scale protein modeling approach mayreduce the time it takes to bring pharmaceuticals to market and mayhave significant implications for an array of biotechnologyapplications, including bioprocessing, drug discovery, and proteomics,the study of protein structure and function.

“Predictive modelingis a new approach to drug discovery that takes information from labanalysis and concentrates it in predictive models that may be evaluatedon a computer,” said Curt M. Breneman, professor of chemistry andchemical biology at Rensselaer.

“The ability to predict theseparation behavior of a particular protein directly from its structurehas considerable implications for biotechnology processes,” said StevenCramer, professor of chemical and biological engineering at Rensselaer.“The research results thus far indicate that this modeling approach canbe used to determine protein behavior for use in bioseparationapplications, such as the protein purification methods used in drugdiscovery. This could potentially reduce the development time requiredto bring biopharmaceuticals to market.”

The modeling techniqueis based on methods previously developed by Breneman’s group forrapidly predicting the efficacy and side effects of small drug-likemolecules. The newly developed model successfully predicted the amountof a protein that binds to a material under a range of conditions byusing molecular information obtained from the protein structure. Thesepredicted adsorption isotherm parameters then replicated experimentalresults by predicting the actual separation profile of proteins inchromatographic columns. Chromatography techniques are used to identifyand purify molecules, in this case, particular proteins.

“Weintend to test the model against more complicated protein structures aspart of its further development,” said Breneman. “The outcome of thiswork will yield fundamental information about the complex relationshipbetween a protein’s structural features and its chemical bindingproperties, and also aid in evaluating its potential biomedicalapplications.”

The research findings are reported in the Aug.16 issue of Proceedings of the National Academy of Sciences in a papertitled “A Priori Prediction of Adsorption Isotherm Parameters andChromatographic Behavior in Ion-Exchange Systems.”

In addition toBreneman and Cramer, the collaborative research team includes AsifLadiwala and Kaushal Rege, who both recently earned doctorates inchemical and biological engineering at Rensselaer. The work wassupported by the National Science Foundation and GE Healthcare.

Theresearchers’ computational model uses a combination of molecular-levelquantitative structure-property relationship models with macroscopicsteric mass action isotherm models and support vector machineregression computations.

Biotechnology and Interdisciplinary Studies at Rensselaer
AtRensselaer, faculty and students in diverse academic and researchdisciplines are collaborating at the intersection of the life sciencesand engineering to encourage discovery and innovation. Rensselaer’sfour biotechnology research constellations - biocatalysis and metabolicengineering, functional tissue engineering and regenerative medicine,biocomputation and bioinformatics, and integrative systems biology -engage a multidisciplinary mix of faculty and students focused on theapplication of engineering and physical and information sciences to thelife sciences. Ranked among the world’s most advanced researchfacilities, Rensselaer’s Center for Biotechnology and InterdisciplinaryStudies provides a state-of-the-art platform for collaborative researchand world-class programs and symposia.

About Rensselaer
RensselaerPolytechnic Institute, founded in 1824, is the nation’s oldesttechnological university. The school offers degrees in engineering, thesciences, information technology, architecture, management, and thehumanities and social sciences. Institute programs serveundergraduates, graduate students, and working professionals around theworld. Rensselaer faculty are known for pre-eminence in researchconducted in a wide range of research centers that are characterized bystrong industry partnerships. The Institute is especially well knownfor its success in the transfer of technology from the laboratory tothe marketplace so that new discoveries and inventions benefit humanlife, protect the environment, and strengthen economic development.


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Rensselaer Polytechnic Institute. "Rensselaer Researchers Develop Approach That Predicts Protein Separation Behavior." ScienceDaily. ScienceDaily, 24 August 2005. <www.sciencedaily.com/releases/2005/08/050821231805.htm>.
Rensselaer Polytechnic Institute. (2005, August 24). Rensselaer Researchers Develop Approach That Predicts Protein Separation Behavior. ScienceDaily. Retrieved April 26, 2015 from www.sciencedaily.com/releases/2005/08/050821231805.htm
Rensselaer Polytechnic Institute. "Rensselaer Researchers Develop Approach That Predicts Protein Separation Behavior." ScienceDaily. www.sciencedaily.com/releases/2005/08/050821231805.htm (accessed April 26, 2015).

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