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New, automated tool successfully classifies and relates proteins in unprecedented way

For the first time, researchers have automatically grouped fluorescently tagged proteins from high-resolution images of cells. This technical feat opens a new way to identify disease proteins and drug targets by helping to show which proteins cluster together inside a cell. The approach, developed by Carnegie Mellon University, outperforms existing visual methods to localize proteins inside cells, says Professor Robert F. Murphy, whose report, "Data Mining in Genomics and Proteomics," appears in an upcoming special issue of the Journal of Biomedicine and Biotechnology.

"Our approach really enables the new field of location proteomics, which describes and relates the location of proteins within cells," said Murphy, a professor of biological sciences, machine learning, and biomedical engineering. "This work should provide a more thorough understanding of cellular processes that underlie disease." Using this approach to spot a protein cluster could help scientists identify a common protein structure that enables those proteins to gather in one part of the cell, according to Murphy. Getting this information is critical to foil a disease like cancer, where you might want to identify and disable part of a tumor cell's machinery needed to process proteins for cancer growth. "Our tool represents a step forward because it is based on standardized features and not on features chosen by the human eye, which is unreliable. By automating the clustering of proteins inside cell images, we also can study thousands of images fast, objectively and without error," Murphy said. Murphy's tool has two key components. One is a set of subcellular location features (SLFs) that describe a protein's location in a cell image. SLFs measure both simple and complex aspects of proteins, such as shape, texture, edge qualities and contrast against background features. Like fingerprints, a protein's SLFs act as a unique set of identifiers. Using a set of established SLFs, Murphy then developed
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Source:Carnegie Mellon

Related : Journal of Biomedecine and Biotechnology article


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