Decoding the mechanism of andrographolide to combat hepatocellular carcinoma: a network pharmacology integrated molecular docking and dynamics approach.

HepatoCellular Carcinoma, being one of the most mortally convoluted malignancy with mounting number of occurrences across the world and being classified as the third most prevalent cause of cancer-associated mortalities and sixth most prevalent neoplasia. The active phytoconstituent andrographolide, derived from Andrographis paniculata is conveyed to reconcile a number of human ailments including various oncologies. However, the molecular mechanism underlying the anti-oncogenic effects of Andrographolide on HCC remains skeptical and unclear, emerging as a budding challenge for researchers and oncologists. The present study intends to analyze the underlying pharmacological mechanism of Andrographolide over HCC, established via assimilated approach of network pharmacology. Herein, the Network pharmacology stratagem was instigated to investigate potential HCC targets. The Andrographolide targets along with HCC targets were extracted from multiple databases. A total of 162 potential overlapping targets among HCC and Andrographolide were obtained and further subjected to gene ontology and Pathway enrichment analysis by employing OmicsBox and DAVID database, respectively. Subsequently, Protein-protein interaction network construction by Cytoscape software identified the top 10 hub nodes which were validated by survival and expression analysis. Further, the results derived from molecular docking and dynamic simulations by CB-Dock2 server and Desmond module (Schrodinger software) indicate ALB, CCND1, HIF1A, TNF, and VEGFA as potential Andrographolide related targets with high binding affinity and promising complex stability. Our findings not only reveal the antioncogenic role of andrographolide but also provide novel insights illuminating the identified targets as scientific foundation for anti-oncogenic clinical application of andrographolide in HCC therapeutics.Communicated by Ramaswamy H. Sarma.

Small RNA sequencing and identification of Andrographis paniculata miRNAs with potential cross­kingdom human gene targets.

Cross-species post-transcriptional regulatory potential of plant derived small non-coding microRNAs (miRNAs) has been well documented by plenteous studies. MicroRNAs are transferred to host cells via oral ingestion wherein they play a decisive role in regulation of host genes; thus, miRNAs have evolved as the nascent bioactive molecules imparting pharmacological values to traditionally used medicinal plants. The present study aims to investigate small RNA profiling in order to uncover the potential regulatory role of miRNAs derived from Andrographis paniculata, one of the most widely used herb by tribal communities for liver disorders and document the pharmacological properties of A. paniculata miRNAs. In this study, high-throughput sequencing method was used to generate raw data, ~ 60 million sequences were generated from A. paniculata leaves. Using computational tools and bioinformatics approach, analyses of 3,480,097 clean reads resulted in identification of 3440 known and 51 putative novel miRNAs regulating 1365 and 192 human genes respectively. Remarkably, the identified plausible novel miRNAs apa-miR-5, apa-miR-1, apa-miR-26, and apa-miR-30 are projected to target significant host genes including CDK6, IKBKB, TRAF3, CHD4, MECP2, and ADIPOQ. Subsequent annotations revealed probable involvement of the target genes in various pathways for instance p38-MAPK, AKT, AMPK, NF-Kß, ERK, WNT signalling, MYD88 dependant cascade, and pathways in cancer. Various diseases such as human papilloma virus infection, Alzheimer's, Non-alcoholic Fatty Liver, Alcoholic liver diseases, HepatoCellular Carcinoma (HCC), and numerous other cancers were predominantly found to be linked with target genes. Our findings postulate novel interpretations regarding modulation of human transcripts by A. paniculata miRNAs and exhibit the regulation of human diseases by plant-derived miRNAs. Though our study elucidates miRNAs as novel therapeutic agents, however, experimental validations for assessment of therapeutic potential of these miRNAs are still warranted.

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.