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Marc Vidal, PhD, of Dana-Farber's Center for Cancer Systems Biology, is the senior author of the protein interaction studies.
For scientists who track interactions between cell proteins, a time
of reckoning has arrived. Over the past 20 years, researchers have
identified thousands of such interactions, with the ultimate goal of
inventorying all that occur within cells of various organisms — a
comprehensive catalogue known as the interactome. Such information will
be critical to understanding the basic mechanics of cellular life, and
how malfunctions in these processes contribute to cancer.
Unfortunately, the data collected by different teams of researchers
has been somewhat inconsistent. One group's "map" of protein
interactions in yeast cells, for example, may only partially overlap the
map produced by another group.
Because science depends on investigators' ability to reproduce and
build on one another's work, such variability presents a considerable
obstacle. The value of interactome maps — and the potential of further
research — will be at issue as long as the accuracy and thoroughness of
the underlying data is uncertain.
To recapture momentum, the field needs to be clear about the
strengths and weaknesses of different methods of tracking protein
interactions, researchers say, and reach a consensus on questions such
as, How reliable is the data produced by different techniques? What
portion of the interactome of different organisms has been mapped so
far? Why do existing experimental techniques fail to detect certain
interactions? What can be done to improve the quality of data collected?
In a series of four papers published in this month's issue of the journal Nature Methods,
investigators in Dana-Farber's Center for Cancer Systems Biology (CCSB)
start to answer those questions by examining the accuracy and
thoroughness of current interactome maps and the techniques by which
they are compiled.
The studies — in a special issue of the journal on the interactome —
provide a set of ground rules for future research and demonstrate the
power of such research when backed by well-proven experimental
techniques. The CCSB's director, Marc Vidal, PhD, is the senior author
of the papers.
The first study, lead-authored by the CCSB's Kavitha Venkatesan, PhD,
offers a framework for gauging the quality of current maps of the
interactome in human cells.
The maps draw on three sources of information about protein
interactions: high throughput yeast two-hybrid (HT-Y2H) procedures,
which use robotic equipment to screen thousands of proteins to see which
bind to each other (the binding switches on a "reporter" gene that can
be chemically detected); compilations of published studies on small
numbers of protein interactions; and studies that predict interactions
based on computational techniques.
While each approach is useful, it isn't clear whether small-scale
experiments provide better data than high volume screenings (as some
studies have suggested), whether the interactions detected in
experiments actually occur in living cells, and whether existing maps
depict a small- or large-sized chunk of the entire interactome.
All experimental techniques generate some false positives — in which
interactions are "detected" that haven't really taken place — and false
negatives — in which interactions that have occurred fail to be found.
To weed them out, the new framework examines experimental methods from
the standpoint of precision, sensitivity, and completeness.
"The framework approach takes as standards interactions reported in
multiple studies of high quality, and then verifies those standards
against results obtained by other techniques," says Venkatesan.
Using the framework, the Dana-Farber team found that each technique
captures only 20-30 percent of all the interactions within cells. That
led them to determine that the human interactome contains about 130,000
interactions, a small minority of which have been mapped so far.
The second study offers researchers a tool kit for determining
whether a newly discovered interaction is indeed real, and not a false
positive reading from a particular type of experiment.
The kit is a set of four high-capacity protein interaction tests that
have been weighted in relation to a common set of benchmark data. When
scientists identify two proteins as likely interactors, the pair can be
tested in the tool kit to obtain a "confidence score" about whether they
do, in fact, interact.
"This general approach will allow researchers to systematically and
objectively assign confidence scores to all individual protein-protein
interactions in cells," says lead author Pascal Braun, PhD. "Such a
universally interpretable quality standard is critical for constructing
accurate interactome maps."
The third study uses the quality control framework from the first
study to compile a new, expanded map of the interactome of the worm
Caenorhabditis elegans (C. elegans), a scientific favorite whose cells
have roughly the same number of genes as human cells do.
The previous version of the map was assembled from studies involving
about 2,000 proteins. For the new map, lead author Nicolas Simonis, PhD,
of the CCSB and his associates screened some 10,000 protein pairs,
documenting 3,864 high-quality interactions. The framework enabled the
researchers to estimate that the worm's genome includes about 116,000
interactions, meaning that 96 percent of its interactome remains
Interactome maps are constructed from a variety of sources, including
new experiments and data from earlier studies. As Michael Cusick, PhD,
and co-authors show in the fourth Nature Methods paper, the information in some of those much-used databases is not as reliable as one would hope.
The team focused on databases built from published studies that
involve just a few protein interactions — an approach sometimes thought
to be more accurate than mass-screening techniques.
Researchers typically cull information from several such studies to
draw conclusions about which proteins interact. In examining such
studies closely, however, the researchers found that the results overlap
rather infrequently. Of some 12,000 interactions that have been
identified in yeast cells, 75 percent were reported in one study only.
When Cusick and colleagues reviewed 100 of these shakily supported
interactions, they could independently substantiate only 25 percent of
The authors suggest that the lower-than-expected quality of this data
has less to do with the skill of the scientists who handle the data
than with the inherent difficulty of extracting information from long,
"Often, these studies use different reporting guidelines, which makes
it difficult to compile results in a uniform way," Cusick remarks. One
solution is the molecular interaction experiment initiative, or MIMIx,
which standardizes reporting of protein interactions in published
"Interaction mapping is a complex field," Cusick states. "By teasing
apart the process of interaction discovery and verification, we've
identified where problems are coming from and offered solutions to
minimize inconsistencies in the future. This will be critical as efforts
continue to map the entire interactome of various species, including