Mycobacterium tuberculosis l-alanine dehydrogenase (l-MtAlaDH) plays an important role in catalyzing l-alanine to ammonia and pyruvate, which has been considered to be a potential target for tuberculosis treatment. In the present work, the functional domain motions encoded in the structure of l-MtAlaDH were investigated by using the Gaussian network model (GNM) and the anisotropy network model (ANM). The slowest modes for the open-apo and closed-holo structures of the enzyme show that the domain motions have a common hinge axis centered in residues Met133 and Met301. Accompanying the conformational transition, both the 1,4-dihydronicotinamide adenine dinucleotide (NAD)-binding domain (NBD) and the substrate-binding domain (SBD) move in a highly coupled way. The first three slowest modes of ANM exhibit the open-closed, rotation and twist motions of l-MtAlaDH, respectively. The calculation of the fast modes reveals the residues responsible for the stability of the protein, and some of them are involved in the interaction with the ligand. Then, the functionally-important residues relevant to the binding of the ligand were identified by using a thermodynamic method. Our computational results are consistent with the experimental data, which will help us to understand the physical mechanism for the function of l-MtAlaDH.

The transition energies and wavelengths of 1s24f-1s2ng (5 ≤ n ≤ 8) states of lithium-like ions starting from vanadium and ending with zinc are

Surface water, as the most important land resource, profoundly affects the balance of the ecosystem and the development of social economy. The rapid development of remote-sensing technology provides the possibility of dynamic monitoring and real-time automatic extraction of surface water. Based on the index of ZhuHai-1 Orbita-hyperspectral (OHS) pixels, this paper proposes a supervised learning method for semi-automatic surface water extraction. The method first uses a threshold method to segment the cosine distance between pixel spectra, realizes automatic acquisition of training set labels required for supervised classification and effectively improves work efficiency. We then combine the extraction results of the support vector machine (SVM), which only considers spectral information, and the variant full convolutional neural network (VFCN), which only considers spatial information, to obtain more accurate surface water information. In this paper, the analysis of typical waterbodies in four regions showed that the performance of the surface water extraction for all scenes when using this method in this paper is better than that when using the water index (WI), VFCN or SVM. The overall accuracy of this method is above 98.822%, and the kappa coefficients are above 0.9019.