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Pancreatic ductal adenocarcinoma (PDAC) is a major health issue that has been eluding efforts to identify viable therapeutic treatment options. Besides having the lowest survival rate among all types of cancer, almost all conventional methods of treatment are futile against this condition, leaving patients to succumb to this ailment faster than ever. As it is increasingly becoming difficult to come up with new compounds for the treatment of various diseases, alternative solutions are required for tackling these problems. In this study, publically available miRNA and gene expression data were used to identify common elements that were present in gemcitabine-resistant PDAC cell lines. By selecting overexpressed genes involved in pancreatic cancer and cancer pathways in general, potential drug candidates for the treatment of PDAC were identified. In this study, 21 differentially expressed miRNAs were identified from PANC-1 cell line treated with gemcitabine. Pathway analysis revealed that MET and PPARG were overexpressed in cancer-related pathways, including pancreatic cancer, and could be targeted for PDAC treatment. Using CMap, fisetin was identified a likely candidate drug for the treatment of PDAC. Docking studies indicated that fisetin was bound to c-Met and PPARG with an XP G score of -12.819 and -7.021 kcal/mol, respectively. As miRNAs have increasingly been shown to part take in important cancer-related processes and pathways, researching drug development methods based on miRNA targets could be beneficial for pharmaceutical industries. Communicated by Ramaswamy H. Sarma.

With the advancement of high throughput techniques, the discovery rate of enzyme sequences has increased significantly in the recent past. All of these raw sequences are required to be precisely mapped to their respective functional attributes, which helps in deciphering their biological role. In the recent past, various prediction models have been proposed to predict the enzyme functional class; however, all of these models were able to quantify at most six functional enzyme classes ( EC1 to EC6 ) out of existing seven functional classes, making these approaches inappropriate for handling enzymes corresponding to the seventh functional class ( EC7 ). In this study, a Deep Neural Network-based approach, DeEPn , has been proposed, which can quantify enzymes corresponding to all seven functional classes with high precision and accuracy. The proposed model was compared with two recently developed tools, ECPred and SVM-Prot . The result demonstrated that DeEPn outperformed ECPred and SVM-Prot in terms of predictive quality. The DeEPn tool has been hosted as a web-based tool at https://bioserver.iiita.ac.in/DeEPn/.Communicated by Ramaswamy H. Sarma.

Pancreatic ductal adenocarcinoma (PDAC) is a pancreatic malignancy suffering from poor prognosis; the worst among all types of cancer. Chemotherapy, which is the standard regime for treatment in most cases, is often rendered useless as drug resistance quickly sets in after prolonged exposure to the drug. The implication of PAX2 transcription factor in regulating several ATP-binding cassette (ABC) transporter proteins that are responsible for the acquisition of drug resistance in PDAC makes it a potential target for treatment purposes. In this study, the 3D structure of PAX2 protein was modeled, and the response of key amino acids to perturbation was identified. Subsequently, kappadione, a vitamin K derivative, was found to bind efficiently to PAX2 with a binding energy of -9.819 kcal/mol. The efficacy of mechanism and mode of binding was studied by docking the protein with DNA in the presence and absence of the drug. The presence of kappadione disrupted DNA binding with key effector resides, preventing the DNA from coming into contact with the binding region essential for protein translation. By occupying the DNA binding region and replacing it with a ligand, the mechanism by which DNA interacts with PAX2 could be manipulated. Inhibition of PAX2-DNA binding using kappadione and other small molecules can prove to be beneficial for combating chemoresistance in PDAC, as proposed through in silico approaches.Communicated by Ramaswamy H. Sarma.

Listeria monocytogenes, a Gram-positive opportunistic food-borne pathogen, naturally resistant to many antibiotics and acquired resistance may be a concern in the nearer future. Hence, there is a scope for screening of novel therapeutic agents and drug targets, toward the treatment of fatal listeria infections. The SecA homologs, SecA1 and SecA2 are the essential components of the general secretion (Sec) pathway, a specialised protein export system, present in L. monocytogenes. This study evaluates the use of botanicals against L. monocytogenes MTCC 1143 by considering SecA proteins as probable drug targets by high-throughput screening approaches. The 3D structure of SecA proteins with good stereochemical validity was generated by comparative modelling. The druglikeness and pharmacokinetic properties of 97 phytoligands identified through the extensive literature survey were predicted for druglikeness and ADMET properties. The inhibitory properties of best candidates were studied by molecular docking. The effect of the selected candidate molecules were further analysed in vitro well diffusion and cell aggregation assays. The antibiotic sensitivity profiling applied to L. monocytogenes MTCC 1143 using clinically relevant antibiotics showed that the bacteria became drug resistant to many tested antibiotics. The virtual screening suggested that .05 M cinnamic aldehyde from Cinnamomum camphora and 1, 2-Epoxycyclododecane from Cassia auriculata were identified as potential SecA inhibitors. The well diffusion assays suggested that the selected herbal substances have antibacterial activities. Further, preliminary validation suggested that incorporation of cinnamic aldehyde and methanolic or ethyl acetate extract of C. auriculata in broth medium shows growth reduction, misassembly and cell aggregation. This indicates the inhibition of SecA targets.