NMR assignment of aromatic rings in proteins is a prerequisite for obtaining high-quality solution structures of proteins and for studying the dynamics and folding of their molecular cores. Here we present sensitive PFG-PEP L-GFT-(TROSY) (4,3)D HCCH NMR for identification of aromatic spin systems based on four-dimensional (4D) spectral information which can be rapidly obtained with high digital resolution. The G-matrix Fourier Transform (GFT) experiment relies on newly introduced longitudinal relaxation (L-)optimization for aromatic protons and is optimally suited for both sensitivity and sampling limited data collection, making it particularly attractive for NMR-based structural genomics. Applications are presented for 21 and 13 kDa proteins HR41 and MaR11, targets of the Northeast Structural Genomics Consortium for which data collection is, respectively, sensitivity and sampling limited. Complete assignment of aromatic rings enabled high-quality NMR structure determination, and nearly complete analysis of aromatic proton line widths allowed one to assess the flipping of most rings in HR41. Specifically, the ring of Tyr90 flips very slowly on the seconds time scale, thereby proving the absence of fast larger-amplitude motional modes which could allow the ring to flip. This indicates remarkable rigidity of the substructure in which the ring is embedded. Tyr90 is conserved among ubiquitin-conjugating enzymes E2, to which HR41 belongs, and is located in spatial proximity to the interface between E2 and ubiquitin protein ligase E3. Hence, the conformational rigidity and/or the slow motional mode probed by the ring might be of functional importance.
The binding of pyridine by displacement of the methionine-80 residue from heart ferricytochrome c (cyt c) has been studied by 1 H NMR spectroscopy. Owing to the low stability of the pyridine-bound form of cytochrome c, the so-called alkaline isomer (lysine form) of cyctochrome c appears at pH > 6 in the presence of 0.45 mol dm -3 pyridine, in contrast to the native lysine form which only appears at pH > 9. A mixture of native cytochrome c and pyridine without the lysine form can only be obtained at pH < 6. The bound pyridine is replaced by a lysine as the pH value increases. Native cytochrome c, pyridine-bound cytochrome c and the lysine form can exist simultaneously at neutral pH. A pure lysine form can be obtained at pH > 8 in the presence of 1.27 mol dm -3 pyridine. Using the known resonance assignments for native cytochrome c, some hyperfine-shifted resonances arising from haem peripheral protons and two axial ligands and some side-chain resonances of the aliphatic and aromatic protons of pyridine-ligated cyt c (pcyt c) have been assigned. The experimental dipolar shifts for protons belonging to the non-co-ordinated residues have allowed the identification of the plausible orientation and magnitude of the g tensor. The sources of the asymmetric spin density distribution in the haem group of pcyt c are discussed. The substitution reaction rate constants and the equilibrium constant for pyridine binding to cyt c have been evaluated.
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Small target detection has always been a hot and difficult point in the field of target detection. The existing detection network has a good effect on conventional targets but a poor effect on small target detection. The main challenge is that small targets have few pixels and are widely distributed in the image, so it is difficult to extract effective features, especially in the deeper neural network. A novel plug-in to extract location features of the small target in the deep network was proposed. Because the deep network has a larger receptive field and richer global information, it is easier to establish global spatial context mapping. The plug-in named location feature extraction establishes the spatial context mapping in the deep network to obtain the global information of scattered small targets in the deep feature map. Additionally, the attention mechanism can be used to strengthen attention to the spatial information. The comprehensive effect of the above two can be utilized to realize location feature extraction in the deep network. In order to improve the generalization of the network, a new self-distillation algorithm was designed for pre-training that could work under self-supervision. The experiment was conducted on the public datasets (Pascal VOC and Printed Circuit Board Defect dataset) and the self-made dedicated small target detection dataset, respectively. According to the diagnosis of the false-positive error distribution, the location error was significantly reduced, which proved the effectiveness of the plug-in proposed for location feature extraction. The mAP results can prove that the detection effect of the network applying the location feature extraction strategy is much better than the original network.
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