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
computational strategy for automatically clustering, or grouping,
proteins based on SLF similarities and differences. For his study,
Murphy used images of randomly chosen, fluorescently labeled proteins.
These proteins were produced inside living cells using a technology
called CD tagging, which was developed by Jonathan Jarvik and Peter
Berget, both associate professors of biological sciences at Carnegie
Mellon. The computational analyses were carried out together with Xiang
Chen, a graduate student in the Merck Computational Biology and
Chen and Murphy found that the new tool outperformed existing methods
of identifying overlapping proteins within cells, such as simple visual
categorization of their locations.
"Our tool outperformed clustering based on the terms developed by the
Gene Ontology Consortium, the best previous way of describing protein
location. We found that the Gene Ontology terms were too limited to
describe the many complex location patterns we found. Of course, the
other drawback of term-based approaches is that they have to be
assigned manually by database curators, and this is often not
consistent between different curators," said Murphy.
Murphy and his colleagues are currently amassing more protein image
data using CD Tagging so that they can refine their approach further.
They are also working on ways to "train" a general system that will
work for different cell types.
Related : Journal of Biomedecine and Biotechnology article
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