Screening for anti-influenza virus compounds from traditional Mongolian medicine by GFP-based reporter virus.

Introduction: Screening for effective antiviral compounds from traditional Mongolian medicine not only aids in the research of antiviral mechanisms of traditional medicines, but is also of significant importance for the development of new antiviral drugs targeting influenza A virus. Our study aimed to establish high-throughput, rapid screening methods for antiviral compounds against influenza A virus from abundant resources of Mongolian medicine. Methods: The use of GFP-based reporter viruses plays a pivotal role in antiviral drugs screening by enabling rapid and precise identification of compounds that inhibit viral replication. Herein, a GFP-based reporter influenza A virus was used to identify potent anti-influenza compounds within traditional Mongolian medicine. Results: Our study led to the discovery of three active compounds: Cardamonin, Curcumin, and Kaempferide, all of which exhibited significant antiviral properties in vitro. Subsequent analysis confirmed that their effectiveness was largely due to the stimulation of the antiviral signaling pathways of host cells, rather than direct interference with the viral components, such as the viral polymerase. Discussion: This study showcased the use of GFP-based reporter viruses in high-throughput screening to unearth antiviral agents from traditional Mongolian medicine, which contains rich antiviral compounds and deserves further exploration. Despite certain limitations, fluorescent reporter viruses present substantial potential for antiviral drug screening research due to their high throughput and efficiency.

Dinucleotide composition representation -based deep learning to predict scoliosis-associated Fibrillin-1 genotypes.

Introduction: Scoliosis is a pathological spine structure deformation, predominantly classified as "idiopathic" due to its unknown etiology. However, it has been suggested that scoliosis may be linked to polygenic backgrounds. It is crucial to identify potential Adolescent Idiopathic Scoliosis (AIS)-related genetic backgrounds before scoliosis onset. Methods: The present study was designed to intelligently parse, decompose and predict AIS-related variants in ClinVar database. Possible AIS-related variant records downloaded from ClinVar were parsed for various labels, decomposed for Dinucleotide Compositional Representation (DCR) and other traits, screened for high-risk genes with statistical analysis, and then learned intelligently with deep learning to predict high-risk AIS genotypes. Results: Results demonstrated that the present framework is composed of all technical sections of data parsing, scoliosis genotyping, genome encoding, machine learning (ML)/deep learning (DL) and scoliosis genotype predicting. 58,000 scoliosis-related records were automatically parsed and statistically analyzed for high-risk genes and genotypes, such as FBN1, LAMA2 and SPG11. All variant genes were decomposed for DCR and other traits. Unsupervised ML indicated marked inter-group separation and intra-group clustering of the DCR of FBN1, LAMA2 or SPG11 for the five types of variants (Pathogenic, Pathogeniclikely, Benign, Benignlikely and Uncertain). A FBN1 DCR-based Convolutional Neural Network (CNN) was trained for Pathogenic and Benign/ Benignlikely variants performed accurately on validation data and predicted 179 high-risk scoliosis variants. The trained predictor was interpretable for the similar distribution of variant types and variant locations within 2D structure units in the predicted 3D structure of FBN1. Discussion: In summary, scoliosis risk is predictable by deep learning based on genomic decomposed features of DCR. DCR-based classifier has predicted more scoliosis risk FBN1 variants in ClinVar database. DCR-based models would be promising for genotype-to-phenotype prediction for more disease types.

Theoretical study on novel nitrogen-rich energetic compounds of bis(amino)-azobis(azoles) with tetrazene unit.

Six stereoisomers of 5,5'-bis(amino)-1,1'-azobis(tetrazoles) and 30 other structures, including all possible bis(amino)-azobis(azoles) with an N-N=N-N unit, were designed. The molecular geometries were fully optimized at the DFT-B3LYP level with the 6-31++g (d, p) basis set. From the absence of any imaginary frequency in the infrared vibration frequency spectrum, it is predicted that all these studied structures may exist in stable forms. The results of the total energies of the stereoisomers of 5,5'-bis(amino)-1,1'-azobis(tetrazoles) indicate that the two symmetric trans-form structures are more likely to exist than the other four. The pyrolysis process, chemical stability and molecular electrostatic potential were studied via the investigation of their electronic structure. Heats of formation (HOFs) were calculated using the atomization energy method based on the results of the harmonic vibration frequencies, and a linear relationship was found between the HOF and nitrogen chain or nitrogen content. Densities of the title compounds were predicted with the Monte Carlo method. Finally, according to the results of the calculated HOFs and densities, the explosive parameters of these compounds were calculated using the Kamlet-Jacobs formula. 5,5'-Bis(amino)-1,1'-azobis(tetrazoles) and its isomer 5,5'-bis(amino)-2,2'-azobis(tetrazoles) may have potential for use as energetic compounds.