Chemists of all fields currently publish about 50 000 crystal structures per year, the vast majority of which are X‐ray structures. We determined two molecular structures by employing electron rather than X‐ray diffraction. For this purpose, an EIGER hybrid pixel detector was fitted to a transmission electron microscope, yielding an electron diffractometer. The structure of a new methylene blue derivative was determined at 0.9 Å resolution from a crystal smaller than 1×2 μm 2 . Several thousand active pharmaceutical ingredients (APIs) are only available as submicrocrystalline powders. To illustrate the potential of electron crystallography for the pharmaceutical industry, we also determined the structure of an API from its pill. We demonstrate that electron crystallography complements X‐ray crystallography and is the technique of choice for all unsolved cases in which submicrometer‐sized crystals were the limiting factor.

The cullin-RING ubiquitin E3 ligase (CRL) family comprises over 200 members in humans. The COP9 signalosome complex (CSN) regulates CRLs by removing their ubiquitin-like activator NEDD8. The CUL4A-RBX1-DDB1-DDB2 complex (CRL4A(DDB2)) monitors the genome for ultraviolet-light-induced DNA damage. CRL4A(DBB2) is inactive in the absence of damaged DNA and requires CSN to regulate the repair process. The structural basis of CSN binding to CRL4A(DDB2) and the principles of CSN activation are poorly understood. Here we present cryo-electron microscopy structures for CSN in complex with neddylated CRL4A ligases to 6.4 Å resolution. The CSN conformers defined by cryo-electron microscopy and a novel apo-CSN crystal structure indicate an induced-fit mechanism that drives CSN activation by neddylated CRLs. We find that CSN and a substrate cannot bind simultaneously to CRL4A, favouring a deneddylated, inactive state for substrate-free CRL4 complexes. These architectural and regulatory principles appear conserved across CRL families, allowing global regulation by CSN.

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Background: The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping.

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