Lead optimization of Allium sativum L. compounds for PTP1B inhibition in diabetes treatment: in silico molecular docking and dynamics simulation.

Protein tyrosine phosphatase 1B (PTP1B) has been identified as a promising drug target for the development of diabetes medications via an inhibition mechanism. Using a computational approach, this study investigates the binding mechanism of lead optimized natural compounds from Allium sativum against the human PTP1B. The molecular docking, induced-fit docking, and binding free energy calculations were analyzed using Schrödinger Suite 2021-2. MD simulation, and gene enrichment analysis was achieved via the Desmond module of Schrödinger to identify best compounds as inhibitors against PTP1B in diabetes management. The docking scores of the lead optimized compounds were good; 5280443_121 from apigenin had the best binding score of -9.345 kcal/mol, followed by 5280443_129 with a binding score of -9.200 kcal/mol, and 5280863_177 from kaempferol had a binding score of -8.528 kcal/mol, followed by 5280863_462 with a binding score of -8.338 kcal/mol. The top two lead optimized compounds, docked better than the standard PTP1B inhibitor (-7.155 kcal/mol), suggesting them as potent inhibitors than the standard PTP1B inhibitor. The outcomes of the induced-fit docking were consistent with the increased binding affinity used in the Glide computation of the five conformed poses between the derivatives (5280443_121, 5280443_129, 5280863_177, and 5280863_462) and the protein (PTP1B). Based on the binding fee energies (MM-GBSA), the lead optimized compounds from kaempferol exhibited more stability than those from apigenin. In the pharmacophore development, all the models exhibit good results across the different metrics. The best performing model with five of five matches on a 1.34 and 1.33 phase score was DDRRR_1, DDRRR_2, and DDDRR_1. The average BEDROC value (= 160.9) was 1, while the average EF 1% value across all models was 101. There were no substantial conformational modifications during the MD simulation process, indicating that the apigenin derivatives (5280443_121) was stable in the protein's active site in 100 ns. IGF1R, EGFR, INSR, PTPN1, SRC, JAK2, GRB2, BCAR1, and IRS1 are among the 11 potential targets found in the protein-protein interaction (PPI) of A. sativum against PTP1B that may be important in A. sativum's defense against PTP1B. Sixty-four (64) pathways were found by KEGG pathway enrichment analysis to be potentially involved in the anti-PTP1B of A. sativum. Consequently, data obtained indicates the effectiveness of the in silico studies in identifying potential lead compounds in A. sativum against PTP1B target.Communicated by Ramaswamy H. Sarma.

Identification of some bioactive compounds from Trignonella foenumgraecum as possible inhibitors of PPARϒ for diabetes treatment through molecular docking studies, pharmacophore modelling and ADMET profiling: An in-silico study.

Oral antidiabetic agents including the peroxisome proliferator-activated receptor gamma (PPARγ) agonists are available for the clinical management of diabetes mellitus (DM) but most are characterized by many adverse effects. In this study, we explore the antidiabetic properties of phytoconstituents from Trigonellafeonumgraecum (Fabaceae) as potential agonist of PPARγ; using in silico molecular docking, molecular mechanics generalized surface area (MM/GBSA)free binding energy prediction, Pharmacophore modeling experiment, and Pharmacokinetic/ toxicity analysis. One hundred and forty (140) compounds derived from Trigonellafeonumgraecum were screened by molecular docking against protein target PDB 3VI8. Results obtained from binding affinity (BA) and that of binding free energy (BFE) revealed five 5 compounds; arachidonic acid (CID_10467, BA -10.029, BFE -58.9), isoquercetin (CID_5280804, BA -9.507kcal/mol, BFE -56.33), rutin (CID_5280805, BA -9.463kcal/mol, BFE -56.33), quercetin (CID_10121947, BA -11.945kcal/mol, BFE -45.89) and (2S)-2-[[4-methoxy-3-[(pyrene-1-carbonylamino)methyl]phenyl]methyl]butanoic acid (CID_25112371, BA -10.679kcal/mol, BFE -45.73); and were superior to the standard; Rosiglitazone with a docking score of -7.672. Hydrogen bonding was notable in the protein-ligand complex interaction, with hydrophobic bond, polar bond and pipi stacking also observed. Their Pharmacokinetic/ toxicity profile showed varying druggable characteristics, but; arachidonic acid had the most favorable characteristics. These compounds are potential agonists of PPARγ and are considered as antidiabetic agents after successful experimental validation.

Computational assessment of chemicals from Morinda citrifolia as potential inhibitors of B-Raf kinase in hepatocellular carcinoma treatment.

Hepatocellular carcinoma (HCC) is a tumour pathology that lacks specific treatment and is predominantly resistant to chemotherapy. The inhibitory activity of Morinda citrifolia, an evergreen tree commonly called Noni, against various carcinomas especially HCC is widely acclaimed. This study was to assess the phytochemical constituents of the plant for inhibitory activity against B-Raf kinase (3C4C) in order to design drugs for HCC treatment. Molecular docking, pharmacophore modelling, induced-fit docking, molecular dynamics (MD) simulations and ADMET predictions were the computational techniques employed in this study to detect potential inhibitors of B-Raf kinase from 135 compounds of Morinda citrifolia. Soranjidiol, Thiamine, Lucidin, 2-Methyl-1,3,5-Trihydroxyanthraquinone and Rubiadin were the five top-scoring compounds ranging from -8.39 to -8.22 kcal/mol, however, the standard ligand, PLX4720, scored -11.26 kcal/mol. The five compounds, like PLX4720 demonstrated hydrogen bond interactions with active site amino acid residues such as GLN 530, CYS 532 and ASP 594. The main energy contributor to the interactions between the compounds and B-Raf kinase were pi-stacking, hydrogen bond, van der Waals and covalent energy. Better docking scores obtained in the induced-fit docking further validates the inhibitory potential of the Soranjidiol against the flexible protein. In MD simulations, Soranjidiol revealed good stability in the active site of the protein since significant conformational changes were not evident. These five compounds, unlike the standard compound, demonstrated adequate druglike properties and good safety profiles. Therefore, further studies should be undertaken so as to develop them into drugs against HCC.Communicated by Ramaswamy H. Sarma.

Modern drug discovery for inflammatory bowel disease: The role of computational methods.

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn's disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.