5p7d

Automated refinement of diffraction data obtained from an endothiapepsin crystal treated with fragment 310

spinqualitylabelsall models 5p7d, resolution 1.17Å Show:Asymmetric Unit Biological Assembly Drag the structure with the mouse to rotate   Export Animated Image   Print3D Structural highlights 5p7d is a 1 chain structure with sequence from Cryphonectria parasitica. Full crystallographic information is available from OCA. For a guided tour on the structure components use FirstGlance. Activity: Endothiapepsin, with EC number 3.4.23.22 Resources: FirstGlance, OCA, PDBe, RCSB, PDBsum, ProSAT Publication Abstract from PubMed Today the identification of lead structures for drug development often starts from small fragment-like molecules raising the chances to find compounds that successfully pass clinical trials. At the heart of the screening for fragments binding to a specific target, crystallography delivers structural information essential for subsequent drug design. While it is common to search for bound ligands in electron densities calculated directly after an initial refinement cycle, we raise the important question whether this strategy is viable for fragments characterized by low affinities. Here, we describe and provide a collection of high-quality diffraction data obtained from 364 protein crystals treated with diverse fragments. Subsequent data analysis showed that approximately 25% of all hits would have been missed without further refining the resulting structures. To enable fast and reliable hit identification, we have designed an automated refinement pipeline that will inspire the development of optimized tools facilitating the successful application of fragment-based methods. High-Throughput Crystallography: Reliable and Efficient Identification of Fragment Hits.,Schiebel J, Krimmer SG, Rower K, Knorlein A, Wang X, Park AY, Stieler M, Ehrmann FR, Fu K, Radeva N, Krug M, Huschmann FU, Glockner S, Weiss MS, Mueller U, Klebe G, Heine A Structure. 2016 Aug 2;24(8):1398-409. doi: 10.1016/j.str.2016.06.010. Epub 2016, Jul 21. PMID:27452405 From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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