Researchers at the Johns Hopkins Bloomberg School of Public Health have developed a new method for identifying specific proteins in whole cell extracts of microorganisms using traditional peptide mass fingerprinting (PMF). The key to the new method, according to the researchers, is a "shortcut" for preparing samples that makes PMF faster and more economical. By reducing the cost of protein identification, they believe PMF can become an economical tool for monitoring and evaluating the effectiveness of microorganisms used in environmental cleanup. The researchers used a dioxin-eating organism to demonstrate the capabilities of their methodology, which they described in an article published in the May 2005 edition of Applied and Environmental Microbiology.
PMF typically involves elaborate sample preparation. A protein mixture is spread across a gel and separated into individual proteins, which are scooped out of the gel and cut with protein scissors into predictable, small pieces called peptides. The samples are then analyzed using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), which identifies protein fragments based on the time they need to travel a defined distance when being accelerated in a vacuum.
In their study, Rolf U. Halden, PhD, PE, assistant professor in the Department of Environmental Health Sciences Bloomberg School of Public Health and his colleagues demonstrate how PMF and mass spectrometry are used to identify a unique dioxin-degrading enzyme in a soup of hundreds of cell proteins. The technique avoids elaborate conventional sample preparation steps by coaxing the cells into mass production of the protein the researchers wish to analyze.
"Finding a specific target in a mixture of hundreds of proteins can be likened to finding the proverbial needle in the haystack; this task can be performed much faster and more economically if you have more needles--and that's exactly what our methoPage: 1 2 Related biology news :1
Contact: Tim Parsons
Johns Hopkins University Bloomberg School of Public Health
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