Better metrics extend SAS capabilities
30 Apr 2013
Researchers from Berkeley Laboratory have improved the metrics for X-ray and neutron analysis of flexible macromolecules.
A team at Berkeley Laboratory has developed a new set of metrics for analysing data acquired via small angle scattering (SAS) experiments with X-rays (SAXS) or neutrons (SANS).
Among other advantages, they believe it will significantly cut the time required to collect data.
“SAS is the only technique that provides a complete snapshot of the thermodynamic state of macromolecules in a single image,” explained Robert Rambo, a scientist with Berkeley Lab’s Physical Biosciences Division.
With our metrics, it should be possible to collect and analyse SAS data at the theoretical limit
In SAS imaging, beams of X-rays or neutrons sent through a sample produce tiny collisions between the X-rays or neutrons and nano or sub-nano sized particles within the sample.
“Many proteins and protein complexes are not well-behaved; they can be highly flexible, creating diffuse structural states. Our new set of metrics fully extends SAS to all particle types, well-behaved and not well-behaved,” Rambo added.
The analytic metrics developed by Berkeley follow the discovery of an SAS invariant.
This invariant has been dubbed the ’volume-of-correlation’ and its value is derived from the scattered intensities of X-rays or neutrons that are specific to the structural states of particles, yet are independent of their concentrations and compositions.
“This SAS invariant applies well to compact and flexible particles, and utilises the entire dataset, which makes it more reliable than traditional SAS analytics, which utilise less than 10% of the data,” said Rambo.
Using current methods to accurately determine molecular mass has been a difficulty as previous systems required either an accurate particle concentration, the assumption of a compact near-spherical shape, or measurements on an absolute scale.
“With our metrics, it should be possible to collect and analyse SAS data at the theoretical limit,” Rambo said.
“This means we can reduce data collection times so that a 90 minute exposure time used by commercial instruments could be cut to nine minutes.”
