Separating proteins by chromatography has been a bit of a dark art, requiring lots of trial and error to find the right conditions for a protein of interest. Now researchers at Rensselaer Polytechnic Institute have published a new computational method to predict protein separation behavior in ion exchange chromatography from protein structure — and industry is taking note.
The approach has potential applications in proteomics, “where you need to separate out a larger number of proteins and optimize conditions in order to do this,” says Curt Breneman, a professor in the department of chemistry and chemical biology at RPI and one of the authors of the study, which was published online in PNAS this summer.
Several pharmaceutical and biotechnology companies are interested in licensing the technology for preparative use, he says, and GE Healthcare, which supported the work, is “interested in making use of it as well.”
The method is based on earlier technology that Breneman and his collaborators developed for drug design. Instead of drug-protein interactions, they now looked at protein-chromatography resin interactions and developed a set of descriptors for the proteins that are similar to those they had previously developed for small molecules. Among them are descriptors that capture elements of the shape and the property distribution on the protein surface, which he called a novel approach.
The scientists then used these descriptors to develop a machine learning model, using 16 proteins of known chromatographic behavior. “What this means is that we are not setting out to do an a priori prediction of the protein property, but we have to have some examples,” Breneman says.
The researchers tested their model on two proteins that were not contained in the model training set, and found that their predictions came close to the experimental data.
— Julia Karow
A consortium of research institutions in the UK and Belgium — including the European Bioinformatics Institute, Flanders Interuniversity Institute for Biotechnology, and Ghent University — launched the open-source Proteomics Identifications Database. Known as PRIDE, the database includes results of the Human Proteome Organization’s Plasma Proteome Project, and a human platelet proteome set published by Ghent University.
Researchers at Applied Biosystems are developing new protein identification software called ProGroup that could result in a 20 to 50 percent increase in protein identification from mass spectra, according to an ABI researcher who gave a presentation at the International Symposium on Mass Spectrometry in the Health and Life Sciences this August. The software is currently in the research stage and no release date has been set.
Biosystems Informatics Institute and its commercial trading arm, Turbinia, gained an exclusive license to Pattern Expert software to develop and market it for protein biomarker discovery.
Nonlinear Dynamics will co-market and co-develop its respective 2D gel-analysis with Waters’ protein mass spectrometry-analysis software products, the companies announced. Nonlinear also said that Invitrogen will market and distribute several of its products, including tools for 1D and 2D electrophoresis-gel analysis.
US Patent 6,931,325. Three-dimensional protein mapping. Inventors: Daniel Wall, David Lubman, and Timothy Barder. Assignee: Regents of the University of Michigan. Issued: August 16, 2005.
This invention “relates to multi-phase protein separation methods capable of resolving and characterizing large numbers of cellular proteins, including methods for efficiently facilitating the transfer of protein samples between separation phases,” according to the abstract. It “provides systems and methods for the generation of multi-dimensional protein maps.”
US Patent 6,931,329. Likelihood-based modification of experimental crystal structure electron density maps. Inventor: Thomas Terwilliger. Assignee: The Regents of the University of California. Issued: August 16, 2005.
This patent relates “to the determination of crystal structure from the analysis of diffraction patterns, and, more particularly, to macromolecular crystallography,” according to the description. The outcome is “an improved electron density map … constructed with the maximized structure factors.”
January 1, 2007
Date at which the EBI’s Rolf Apweiler will take the helm as president of HUPO, following current head John Bergeron. Apweiler was elected to the two-year position in late August at the annual HUPO meeting.