Functional annotation of proteins for signaling network inference in non-model species.

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

Synthesis, Molecular Docking, and In vitro Antimycobacterial Studies on N'-arylidene-4-nitrobenzohydrazides.

BACKGROUND: Mycobacterium tuberculosis (Mtb) is an organism that causes tuberculosis (TB). In 2019, 10 million individuals worldwide contracted tuberculosis, with 1.4 million people dying from the disease each year (World Health Organization, 2021). Hydrazones- hydrazide-based drugs have been shown to be bactericidal against M. tuberculosis replication. OBJECTIVES: We herein intended to synthesize a series of acid hydrazones (3a-3l) by condensing 4-nitrobenzohydrazine with substituted aromatic acids in ethanol at room temperature. MATERIALS AND METHODS: All newly synthesized compounds were characterized by standard spectroscopic techniques. Synthesized compounds were then tested for anti-mycobacterial activity against H37Rv strains. Molecular docking analysis was performed for three crystal structures of 1ENY, 1TED and 2FUM Mycobacterium tuberculosis receptors. RESULTS: Among all tested molecules, 3i (MIC: 50 µg/mL) and 3b (MIC: 50 µg/mL) were found to be the best ligands for further development of new anti-TB drug. We found that our proposed molecules have higher docking scores, corresponding standard anti-TB agents, such as ciprofloxacin and isoniazid. Synthesized compounds were found to have druglikeness properties when tested with Lipinski's filter for drug-likeness. CONCLUSION: Our current study proposes N'-arylidene-4-nitrobenzohydrazides as anti-TB agents. Agents with such system can be developed in future for development into active lead molecules.