New algorithm enables quicker crystal lab studies

By Wai Lang Chu

- Last updated on GMT

Related tags: Electron

A new algorithm, which automatically selects molecule images for
crystallisation in Silico studies, is set to advance laboratory
studies by increasing the speed and power of methods for
determining biological structures at high resolution, based on data
from electron microscopy.

The algorithm, which is best described as "particle picking by segmentation," and is the clearest sign yet of a solution to the problem, that has seen even the best techniques yield more than 30 per cent false positives - either poor-quality images of particles or worse still debris or background noise.

Understanding structure is often the key to devising antibiotics and other therapies that can interfere with unwanted biological activity - for example, the ability of infectious bacteria to synthesize proteins can be wrecked by jamming their ribosomes, if the ribosome structure is known in detail.

Single-particle reconstruction with cryo-EM holds the promise of providing many high-resolution structures, which may be difficult or impossible to obtain otherwise.

Often, when a high-resolution structure of a large and complicated biological molecule is needed, biologists often turn to cryo-electron microscopy (cryo-EM) to perform single-particle reconstruction.

Instead of trying to coax molecules to arrange themselves in a repeating crystalline structure, as is necessary for x-ray crystallography, cryo-EM uses individual molecules frozen in random orientations.

Capturing two-dimensional images of the molecule from many different angles allows powerful computers to recreate the structure in three dimensions.

A typical micrograph may show fifteen hundred or more particles, but picking them out isn't easy. The microscope's electron beam has to be kept at low power to prevent radiation damage, so the signal­-to-noise ratio is low and the particles are barely perceptible shapes in a field of gray.

"It's hard to find good candidates even with an expert eye,"​ said Umesh Adiga, a member of Glaeser's laboratory and a staff scientist in the Physical Biosciences Division.

"Having to choose hundreds of thousands of particles is a bottleneck in the process of single-particle reconstruction,"​ he said.

Adiga and his colleagues tested the new algorithm by using it to pick images from among over 130,000 ribosome particles in 55 micrographs provided by the Wadsworth Centre of the New York State Department of Health in Albany.

Adiga separately inspected the 55 micrographs by eye and "manually" selected particles, well over 80 percent of which turned out to be the same as those picked by the program.

Fewer than 10 per cent of the images chosen by the program were false positives.

On his first pass, intending to select only particles of the highest quality - a "gold standard" - he chose roughly two-thirds of the same particles picked by the software.

When the program's additional candidates were inspected more closely, however, many turned out to be true positives of good quality; only about 10 per cent of the program's picks were false positives.

Beyond the demonstrated goal of selecting the same particles an expert would select with a low error rate, future refinement of the segmentation algorithm aims higher.

The researchers report their results in the forthcoming issue of the Journal of Structural Biology​ in an article now available to subscribers online.

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