There’s nothing like a new pair of eyeglasses to bring fine
details into sharp relief. For scientists who study the large molecules of life
from proteins to DNA, the equivalent of new lenses have come in the form of an
advanced method for analyzing data from X-ray crystallography experiments.
The findings, published in Science, could lead to new understanding of the molecules that
drive processes in biology, medical diagnostics, nanotechnology, and other
Like dentists who use X-rays to find tooth decay, scientists
use X-rays to reveal the shape and structure of DNA, proteins, minerals, and
As X-rays pass through atoms, they reflect distinctive
patterns, which reveal what atoms are present and how atoms are bonded to each
other. However, some data are typically discarded because of concerns over
quality. In particular, data derived from edge regions of the pattern—although
very important for understanding the details of structure—are often overwhelmed
by the random errors associated with a weak signal in the midst of a lot of
Oregon State University
biophysicist Andy Karplus and his colleague Kay Diederichs at the University of Konstanz
have now proven that useful information can be gleaned from data that have
about five times the noise level that was previously considered acceptable.
“The criteria that have been used in the past are way too
conservative,” said Karplus, an expert in protein structure and stability. “These data that people have been throwing out are actually good.”
The bottom line for crystallographers is the accuracy of
their molecular models. The better the model, the better it will predict the
pattern created by X-rays passing through a molecule, and the better it will be
to develop new drugs and nanotechnologies that operate at the molecular scale.
The new method may be the most important conceptual advance
in the past 20 years in how these data are used in modeling, the scientists
said. It shows how data from “noisy” parts of the measurement can still provide
information and allows scientists to see directly where the model is limited by
noise in the data and where the model is a better estimate of molecular
structure than experimental data.
“The question is, ‘Where do we cut it off?'” said Karplus.
By adding data at incremental steps and showing how the model improved, Karplus
and Diederichs showed that scientists had been cutting off their analyses too
soon and discarding data that could sharpen their view of molecular structure.
“The big impact on the field will be that every structure
determined from here on out will be a little more accurate because people won’t
throw away data that are okay,” Karplus said. “If you have a crummy image of
the protein, it will get a little sharper. If you have a good image of the
protein, it will also get a little sharper.”
While the method will be an important step for X-ray
crystallographers, the scientists said that other physical sciences may also
find ways to benefit from this type of data quality analysis. They noted that
one branch of science has been using this type of statistical analysis for many
years. The field of psychometrics—the analysis of data from psychological
tests—has used a similar technique called the “Spearman-Brown prophecy formula”
to determine the minimum length of such tests.
“Now that we know that very noisy data are useful, this will
presumably enable still further improvements as it stimulates new software
development to do a better job of handling such weak data,” said Karplus.