Cryo-EM is a method for determing the structure of the macromolecules and molecular machines that areresponsible for cellular function. Cryo-EM single-particle reconstruction (SPR) is the difficult task ofdeducing the three-dimensional 'density' map of a macromolecular 'particle' from a set of very noisyelectron-microscope images. SPR fails in the cases that the particles are too small, when the images aretoo noisy, or when high resolution is sought. It fails because under these conditions the orientation of theparticles giving rise to the individual images cannot be determined uniquely. We will develop an SPRalgorithm that makes use of Robust Estimation theory to more reliably perform reconstructions fromchallenging datasets. Our algorithm will maximize a clearly-defined statistical quantity (related to the aposteriori probability) while being resistant to the effects of 'outlier' images. We will test the algorithm onsimulated datasets and on two experimental datasets from small particles. One of these will be thetransferrin-transferrin receptor dataset from the Walz lab which provided a successful SPR by conventionaltechniques; the other will be data acquired from solubilized Slack (hslo2.1) ion channels, for whichconventional SPR has to date not been successful.Discerning the structure (detailed shape) of the molecular machines of cells is essential to the understandingof their function, and to the development of therapeutic interventions including drugs that affect their function.Singe-particle cryo-EM is a powerful and flexible method for the determination of structures, but in manycases it fails to provide results, or the results it provides are incorrect. We seek to increase the reliability andusefulness of this method.
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