Pheromones are odoriferous volatile chemical cues produced by animals for communication among conspecifics so as to regulate their social behaviors. In general, the odor compounds are recognized by receptors in the nasal cavity. Odorant-binding protein (OBP), a lipocalin family protein, mediates the air-borne odor cues to nasal receptors through nasal mucus. The presence of OBP in several mammalian species is well documented but to-date there is no report of a nasal OBP in buffalo. Hence, the present study was undertaken to investigate if OBP is present in buffalo nasal mucus. Uni- and two-dimensional gel electrophoresis of the nasal mucus suggested the presence of OBP, which was confirmed using mass spectrometry. In silico homology model of the OBP was generated and its structural similarity with other mammalian OBPs was assessed. Finally, molecular-docking and -dynamics simulations analysis revealed the efficiency of buffalo nasal OBP (bunOBP) to bind with buffalo pheromones as well as other reported chemical cues. Taken together, the occurrence of nasal OBP in buffalo and its putative role in odor binding are reported for the first time. The potential association of this protein with estrus-specific volatiles could be taken to advantage for non-invasive detection of estrus in buffaloes.

: Computer-aided drug design has an important role in drug development and design. It has become a thriving area of research in the pharmaceutical industry to accelerate the drug discovery process. Deep learning, a subdivision of artificial intelligence, is widely applied to advance new drug development and design opportunities. This article reviews the recent technology that uses deep learning techniques to ameliorate the understanding of drug-target interactions in computer-aided drug discovery based on the prior knowledge acquired from various literature. In general, deep learning models can be trained to predict the binding affinity between the protein-ligand complexes and protein structures or generate protein-ligand complexes in structure-based drug discovery. In other words, artificial neural networks and deep learning algorithms, especially graph convolutional neural networks and generative adversarial networks, can be applied to drug discovery. Graph convolutional neural network effectively captures the interactions and structural information between atoms and molecules, which can be enforced to predict the binding affinity between protein and ligand. Also, the ligand molecules with the desired properties can be generated using generative adversarial networks.

Acinetobacter baumannii (A. baumannii) is one of the major representative aetiologies of recalcitrant nosocomial infections. Genotypic and phenotypic alterations in A. baumannii have resulted in a significant surge in multidrug resistance (MDR). Of all the factors responsible for the development of antimicrobial resistance (AMR), efflux protein pumps play a paramount role. In pursuit of a safe alternative for the prevention and control of A. baumannii infections, bioactive compounds from the aerial parts of the medicinal plant Artemisia pallens were studied. GC-MS analysis of the ethanol extract of A. pallens detected five major compounds: lilac alcohol A, spathulenol, lilac alcohol C, n-hexadecanoic acid, and vulgarin. In silico examinations were performed using the Schrödinger suite. Homology modelling was performed to predict the structure of the efflux protein of A. baumannii-LAC-4 strain (MDR Ab-EP). The identified bioactive compounds were analysed for their binding efficiency with MDR Ab-EP. High binding efficiency was observed with vulgarin with a glide score of −4.775 kcal/mol and stereoisomers of lilac alcohol A (−3.706 kcal/mol) and lilac alcohol C (−3.706 kcal/mol). Our molecular dynamic simulation studies unveiled the stability of the ligand–efflux protein complex. Vulgarin and lilac alcohol A possessed strong and stable binding efficiency with MDR Ab-EP. Furthermore, validation of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of the ligands strongly suggested that these compounds could serve as a lead molecule in the development of an alternate drug from A. pallens.

Background: Top five best hit compounds (ZINC59376795, ZINC60175365, ZINC36922620, ZINC39550705 and ZINC36953975) were obtained through our high throughput virtual screening (HTVS) analysis with resistant 5204-PBP2B (5204 Penicillin Binding Protein 2B) and sensitive R6-PBP2B (R6 Penicillin Binding Protein 2B) proteins of Streptococcus pneumoniae. Objective: To gain insight in molecular docking and dynamics simulations of these top five best hit compounds with both resistant 5204-PBP2B and sensitive R6-PBP2B targets. Methods: We have employed Glide XP docking and molecular dynamics simulations of these five best hit compounds with 5204-PBP2B and R6-PBP2B targets. The stability analysis has been carried out through DFT, prime-MM/GBSA binding free energy, RMSD, RMSF and Principal Component Analysis. Results: The reference drug, penicillin G forms stable complex with sensitive R6-PBP2B protein. Similar stability is observed for the mutant resistant 5204-PBP2B with the top scoring compound ZINC592376795 which implies that this compound may act as an effective potential inhibitor. The compound ZINC59376795 forms a total of five hydrogen bonds with resistant 5204-PBP2B protein of which three are with mutated residues. Similarly, the other four compounds including penicillin G also form hydrogen bonds with mutated residue. The MD simulations and stability analysis of the complexes of wild and mutant forms are evaluated for a trajectory period of 16ns and further MD simulations of ZINC59376795 with resistant 5204-PBP2B and sensitive R6-PBP2B confirmed the stability for 50 ns. Conclusion: These results suggest that the top five best hit compounds are found to be a promising gateway for the further development of anti-pneumococcal therapeutics.

Background: Mutations in Penicillin Binding Protein 2B (PBP2B) lead to resistance against commercial β-lactam antibiotics among some strains of Streptococcus pneumoniae. In this study, the molecular mechanism of resistance and the alternate strategy for controlling the Penicillin Binding Protein 2B (PBP2B) of resistant G54 and the two intermediate-resistant Hungary19A-6 and SP195 strains of Streptococcus pneumoniae are determined using an in silico approach. Methods: In this study, we considered four ligand compounds, namely ZINC59376795, ZINC36922620, ZINC39550705, and ZINC36953975 of the ZINC database, identified through high-throughput screening (HTS) study for the resistant strain (5204-PBP2B). Glide XP docking and molecular dynamic simulation studies were performed on the PBP2B of the three resistant strains chosen for this study. The stability analysis was performed with prime-MM/GBSA, and the binding free energy, RMSD, and RMSF were also evaluated. Results: Out of the four ligands, the ZINC39550705 ligand was found to form a stable complex with PBP2B of all three strains. Furthermore, this ZINC39550705 ligand was observed to form four hydrogen bonds with the PBP2B of all three strains. Molecular dynamics simulations were performed with these four selected ligands and targets. The resulting binding pattern, RMSD, RMSF, and hydrogen bond calculation analyses provide deeper insight into the molecular interactions of these four selected ligands with the PBP2B of the three resistance strains of S. pneumoniae. Conclusion: The interaction of the selected ligand molecules and PBP2B of the resistant strains of S. pneumoniae revealed that hydroxyl amino acid Thr at position 224, acidic amino acids including Glu at positions 301, 245, and 240, amidic amino acids, Asn at positions 265 and 260, and aliphatic amino acid Gly at the 302 position had a crucial role in the formation of hydrogen bonds with most of the selected ligands that contributed to the stability of the complexes. The molecular dynamics simulation further confirmed that the stability of ligand ZINC39550705–PBP2B of all mutant strains was higher compared to other ligand-protein complexes evaluated in the present study. Selected ligand molecules showed significant properties of the inhibitor to be used against the resistant strains of S. pneumoniae, and the ligand ZINC39550705, in particular, is a potential alternative to commercial β-lactam antibiotics.