Predicting protein separation

Applying maths and computers to the drug discovery process, researchers at Rensselaer Polytechnic Institute have developed a method to predict protein separation behaviour directly from protein structure.

This new multi-scale protein modelling 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.

"Predictive modelling is a new approach to drug discovery that takes information from lab analysis and concentrates it in predictive models that may be evaluated on a computer," said Curt M. Breneman, professor of chemistry and chemical biology at Rensselaer.

"The ability to predict the separation behaviour of a particular protein directly from its structure has considerable implications for biotechnology processes," said Steven Cramer, professor of chemical and biological engineering at Rensselaer. "The research results thus far indicate that this modelling approach can be used to determine protein behaviour for use in bioseparation applications, such as the protein purification methods used in drug discovery. This could potentially reduce the development time required to bring biopharmaceuticals to market."

A computational representation of protein 135L, with electrostatic potential encoded on its solvent accessible surface.

The modelling technique is based on methods previously developed by Breneman’s group for rapidly predicting the efficacy and side effects of small drug-like molecules.

The newly developed model successfully predicted the amount of a protein that binds to a material under a range of conditions by using molecular information obtained from the protein structure. These predicted adsorption isotherm parameters then replicated experimental results by predicting the actual separation profile of proteins in chromatographic columns. Chromatography techniques are used to identify and purify molecules, in this case, particular proteins.

"We intend to test the model against more complicated protein structures as part of its further development," said Breneman. "The outcome of this work will yield fundamental information about the complex relationship between a protein’s structural features and its chemical binding properties, and also aid in evaluating its potential biomedical applications."

In addition to Breneman and Cramer, the collaborative research team includes Asif Ladiwala and Kaushal Rege, who both recently earned doctorates in chemical and biological engineering at Rensselaer.

The work was supported by the US National Science Foundation and GE Healthcare.