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In recent years, three‐dimensional density maps reconstructed from single particle images obtained by electron cryo‐microscopy (cryo‐EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de‐novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo‐EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main‐chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP‐EM suite.
Pyridoxal 5′‐phosphate (PLP) is an essential cofactor for neurotransmitter metabolism. Pyridoxal phosphatase (PDXP) deficiency in mice increases PLP and γ‐aminobutyric acid levels in the brain, yet how PDXP is regulated is unclear. Here, we identify the Ca\(^{2+}\)‐ and integrin‐binding protein 1 (CIB1) as a PDXP interactor by yeast two‐hybrid screening and find a calmodulin (CaM)‐binding motif that overlaps with the PDXP‐CIB1 interaction site. Pulldown and crosslinking assays with purified proteins demonstrate that PDXP directly binds to CIB1 or CaM. CIB1 or CaM does not alter PDXP phosphatase activity. However, elevated Ca\(^{2+}\) concentrations promote CaM binding and, thereby, diminish CIB1 binding to PDXP, as both interactors bind in a mutually exclusive way. Hence, the PDXP‐CIB1 complex may functionally differ from the PDXP‐Ca\(^{2+}\)‐CaM complex.
The absence of fluorine from most biomolecules renders it an excellent probe for NMR spectroscopy to monitor inhibitor–protein interactions. However, predicting the binding mode of a fluorinated ligand from a chemical shift (or vice versa) has been challenging due to the high electron density of the fluorine atom. Nonetheless, reliable \(^{19}\)F chemical‐shift predictions to deduce ligand‐binding modes hold great potential for in silico drug design. Herein, we present a systematic QM/MM study to predict the \(^{19}\)F NMR chemical shifts of a covalently bound fluorinated inhibitor to the essential oxidoreductase tryparedoxin (Tpx) from African trypanosomes, the causative agent of African sleeping sickness. We include many protein–inhibitor conformations as well as monomeric and dimeric inhibitor–protein complexes, thus rendering it the largest computational study on chemical shifts of \(^{19}\)F nuclei in a biological context to date. Our predicted shifts agree well with those obtained experimentally and pave the way for future work in this area.