"In theory, you need twice as many particles as the molecular weight of what you want to image," explains Umesh Adiga, a member of Glaeser's laboratory and a staff scientist in the Physical Biosciences Division. Molecular weight roughly corresponds to the number of atoms in the molecule. "So for a molecule with half a million atoms, you need a million particle images -- thousands for each orientation."
These must be chosen from many millions of candidates, and each must show the whole particle and nothing but the particle. 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," says Adiga. "Having to choose hundreds of thousands of particles is a bottleneck in the process of single-particle reconstruction."
Automatic particle-picking methods have been devised to meet this challenge, but until now even the best yield more than 30 percent false positives -- either poor-quality images of particles or something else altogether, like debris or background noise. Therefore "a human still has to go through them and pick out the good ones," Adiga says.
Adiga and his colleagues decided that concentrating too much attention on the particle itself in the early stages of picking -- for example, approximating its shape and creating a template into which real images are forced to fit, a process common to all previous automatic methods -- simply added to the difficulty. "We decided that if there's noise, there's noise, so at first let's not deal with the particle but with the noise," he says. "If the parti
Source:DOE/Lawrence Berkeley National Laboratory