A novel entropy-based mapping method for determining the protein-protein interactions in viral genomes by using coevolution analysis

Prediction of viral-host interactions of COVID-19 by computational methods

Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.

Transcriptome and Metabolomic Analyses Reveal Regulatory Networks Controlling Maize Stomatal Development in Response to Blue Light

(1) Background: Blue light is important for the formation of maize stomata, but the signal network remains unclear. (2) Methods: We replaced red light with blue light in an experiment and provided a complementary regulatory network for the stomatal development of maize by using transcriptome and metabolomics analysis. (3) Results: Exposure to blue light led to 1296 differentially expressed genes and 419 differential metabolites. Transcriptome comparisons and correlation signaling network analysis detected 55 genes, and identified 6 genes that work in the regulation of the HY5 module and MAPK cascade, that interact with PTI1, COI1, MPK2, and MPK3, in response to the substitution of blue light in environmental adaptation and signaling transduction pathways. Metabolomics analysis showed that two genes involved in carotenoid biosynthesis and starch and sucrose metabolism participate in stomatal development. Their signaling sites located on the PHI1 and MPK2 sites of the MAPK cascade respond to blue light signaling. (4) Conclusions: Blue light remarkably changed the transcriptional signal transduction and metabolism of metabolites, and eight obtained genes worked in the HY5 module and MAPK cascade.

Transcriptome and Metabolomic Analyses Reveal Regulatory Networks Controlling Maize Stomatal Development in Response to Blue Light

(1) Background: Blue light is important for the formation of maize stomata, but the signal network remains unclear. (2) Methods: We replaced red light with blue light in an experiment and provided a complementary regulatory network for the stomatal development of maize by using transcriptome and metabolomics analysis. (3) Results: Exposure to blue light led to 1296 differentially expressed genes and 419 differential metabolites. Transcriptome comparisons and correlation signaling network analysis detected 55 genes, and identified 6 genes that work in the regulation of the HY5 module and MAPK cascade, that interact with PTI1, COI1, MPK2, and MPK3, in response to the substitution of blue light in environmental adaptation and signaling transduction pathways. Metabolomics analysis showed that two genes involved in carotenoid biosynthesis and starch and sucrose metabolism participate in stomatal development. Their signaling sites located on the PHI1 and MPK2 sites of the MAPK cascade respond to blue light signaling. (4) Conclusions: Blue light remarkably changed the transcriptional signal transduction and metabolism of metabolites, and eight obtained genes worked in the HY5 module and MAPK cascade.