ABSTRACT:The hydrogen bonding complexes formed between the H 2 O and OH radical have been completely investigated for the first time in this study using density functional theory (DFT). A larger basis set 6-311ϩϩG(2d,2p) has been employed in conjunction with a hybrid density functional method, namely, UB3LYP/6-311ϩϩG(2d,2p). The two degenerate components of the OH radical 2 ⌸ ground electronic state give rise to independent states upon interaction with the water molecule, with hydrogen bonding occurring between the oxygen atom of H 2 O and the hydrogen atom of the OH radical. Another hydrogen bond occurs between one of the H atoms of H 2 O and the O atom of the OH radical. The extensive calculation reveals that there is still more hydrogen bonding form found first in this investigation, in which two or three hydrogen bonds occur at the same time. The optimized geometry parameter and interaction energy for various isomers at the present level of theory was estimated. The infrared (IR) spectrum frequencies, IR intensities, and vibrational frequency shifts are reported. The estimates of the H 2 O ⅐ OH complex's vibrational modes and predicted IR spectra for these structures are also made. It should be noted that a total of 10 stationary points have been confirmed to be genuine minima and transition states on the potential energy hypersurface of the H 2 O ⅐ HO system. Among them, four genuine minima were located.

DNA-binding proteins play pivotal roles in alternative splicing, RNA editing, methylating and many other biological functions for both eukaryotic and prokaryotic proteomes. Predicting the functions of these proteins from primary amino acids sequences is becoming one of the major challenges in functional annotations of genomes. Traditional prediction methods often devote themselves to extracting physiochemical features from sequences but ignoring motif information and location information between motifs. Meanwhile, the small scale of data volumes and large noises in training data result in lower accuracy and reliability of predictions. In this paper, we propose a deep learning based method to identify DNA-binding proteins from primary sequences alone. It utilizes two stages of convolutional neutral network to detect the function domains of protein sequences, and the long short-term memory neural network to identify their long term dependencies, an binary cross entropy to evaluate the quality of the neural networks. When the proposed method is tested with a realistic DNA binding protein dataset, it achieves a prediction accuracy of 94.2% at the Matthew’s correlation coefficient of 0.961. Compared with the LibSVM on the arabidopsis and yeast datasets via independent tests, the accuracy raises by 9% and 4% respectively. Comparative experiments using different feature extraction methods show that our model performs similar accuracy with the best of others, but its values of sensitivity, specificity and AUC increase by 27.83%, 1.31% and 16.21% respectively. Those results suggest that our method is a promising tool for identifying DNA-binding proteins.

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