5sfm | pdb_00005sfm

CRYSTAL STRUCTURE OF HUMAN PHOSPHODIESTERASE 10 IN COMPLEX WITH n3cn1c(nc(n1)c2ccccc2)cc3NC(=O)c5c(C(=O)N4CCOCC4)cnn5C, micromolar IC50=0.0012349

spinqualitylabelsall models 5sfm, resolution 2.16Å Show:Asymmetric Unit Biological Assembly Drag the structure with the mouse to rotate   Export Animated Image   Print3D Structural highlights 5sfm is a 4 chain structure with sequence from Homo sapiens. Full crystallographic information is available from OCA. For a guided tour on the structure components use FirstGlance. Method: X-ray diffraction, Resolution 2.16Å Ligands: CME, ILN, MG, ZN Resources: FirstGlance, OCA, PDBe, RCSB, PDBsum, ProSAT Function PDE10_HUMAN Plays a role in signal transduction by regulating the intracellular concentration of cyclic nucleotides. Can hydrolyze both cAMP and cGMP, but has higher affinity for cAMP and is more efficient with cAMP as substrate. Publication Abstract from PubMed We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios-which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data. A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.,Tosstorff A, Rudolph MG, Cole JC, Reutlinger M, Kramer C, Schaffhauser H, Nilly A, Flohr A, Kuhn B J Comput Aided Mol Des. 2022 Oct;36(10):753-765. doi: 10.1007/s10822-022-00478-x. , Epub 2022 Sep 25. PMID:36153472 From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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