Speaker
Description
Cryo-electron microscopy is a popular method for protein structure determination. Identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require significant ad hoc post-processing, especially for unusually-shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle picking pipeline using neural networks trained with a novel positive-unlabeled (PU) learning method. This framework enables state-of-the-art particle detection models to be trained with few, sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false positive rates, is uniquely capable of picking challenging unusually-shaped proteins (e.g. small, non-globular, and asymmetric), produces more representative particle sets, and does not require post hoc curation; we demonstrate these results on two currently difficult datasets and three conventional datasets. Our PU learning method is general-purpose and outperforms existing PU learning approaches. Topaz is modular, standalone, free, and open source.
