  In the analysis of frozen hydrated biomolecules by single-particle
cryo-electron microscopy, template-based particle picking by a target function
called fast local correlation (FLC) allows a large number of particle images to
be automatically picked from micrographs. A second, independent target function
based on maximum likelihood (ML) can be used to align the images and verify the
presence of signal in the picked particles. Although the paradigm of this
dual-target-function (DTF) evaluation of single-particle selection has been
practiced in recent years, it remains unclear how the performance of this DTF
approach is affected by the signal-to-noise ratio of the images and by the
choice of references for FLC and ML. Here we examine this problem through a
systematic study of simulated data, followed by experimental substantiation. We
quantitatively pinpoint the critical signal-to-noise ratio (SNR), at which the
DTF approach starts losing its ability to select and verify particles from
cryo-EM micrographs. A Gaussian model is shown to be as effective in picking
particles as a single projection view of the imaged molecule in the tested
cases. For both simulated micrographs and real cryo-EM data of the 173-kDa
glucose isomerase complex, we found that the use of a Gaussian model to
initialize the target functions suppressed the detrimental effect of reference
bias in template-based particle selection. Given a sufficient signal-to-noise
ratio in the images and the appropriate choice of references, the DTF approach
can expedite the automated assembly of single-particle data sets.
