Structure-based virtual screening of dipeptidyl peptidase 4 inhibitors and their in vitro analysis

Computational biology-based study of the molecular mechanism of spermidine amelioration of acute pancreatitis

Acute pancreatitis (AP) is an acute inflammatory gastrointestinal disease, the mortality and morbility of which has been on the increase in the past years. Spermidine, a natural polyamine, has a wide range of pharmacological effects including anti-inflammation, antioxidation, anti-aging, and anti-tumorigenic. This study aimed to investigate the reliable targets and molecular mechanisms of spermidine in treating AP. By employing computational biology methods including network pharmacology, molecular docking, and molecular dynamics (MD) simulations, we explored the potential targets of spermidine in improving AP with dietary supplementation. The computational biology results revealed that spermidine had high degrees (degree: 18, betweenness: 38.91; degree: 18, betweenness: 206.41) and stable binding free energy (ΔGbind: - 12.81 ± 0.55 kcal/mol, - 15.00 ± 1.00 kcal/mol) with acetylcholinesterase (AchE) and serotonin transporter (5-HTT). Experimental validation demonstrates that spermidine treatment could reduce the necrosis and AchE activity in pancreatic acinar cells. Cellular thermal shift assay (CETSA) results revealed that spermidine could bind to and stabilize the 5-HTT protein in acinar cells. Moreover, spermidine treatment impeded the rise of the expression of 5-HTT in pancreatic tissues of caerulein induced acute pancreatitis mice. In conclusion, serotonin transporter might be a reliable target of spermidine in treating AP. This study provides new idea for the exploration of potential targets of natural compounds.

Identification of isobenzofuranone derivatives as promising antidiabetic agents: Synthesis, in vitro and in vivo inhibition of α-glucosidase and α-amylase, computational docking analysis and molecular dynamics simulations

Diabetes mellitus, one of the major health challenges of the 21st century, is associated with numerous biomedical complications including retinopathy, neuropathy, nephropathy, cardiovascular diseases and liver disorders. To control the chronic hyperglycemic condition, the development of potential inhibitors of drug targets such as α-glucosidase and α-amylase remains a promising strategy and focus of continuous efforts. Therefore, in the present work, a concise library of isobenzofuranone derivatives (3a-q) was designed and synthesized using Suzuki-Miyaura cross-coupling approach. The biological potential of these heterocyclic compounds against carbohydrate-hydrolyzing enzymes; α-glucosidase and α-amylase, was examined. In vitro inhibitory results demonstrated that the tested isobenzofuranones were considerably more effective and potent inhibitors than the standard drug, acarbose. Compound 3d having an IC50 value of 6.82 ± 0.02 μM was emerged as the lead candidate against α-glucosidase with ⁓127-folds strong inhibition than acarbose. Similarly, compound 3g demonstrated ⁓11-folds higher inhibition strength against α-amylase when compared with acarbose. Both compounds were tested in vivo and results demonstrate that the treatment of diabetic rats with α-amylase inhibitor show more pronounced histopathological normalization in kidney and liver than with α-glucosidase inhibitor. The Lineweaver-Burk plot revealed an uncompetitive mode of inhibition for 3d against α-glucosidase whereas compound 3g exhibited mixed inhibition against α-amylase. Furthermore, in silico molecular docking and dynamics simulations validated the in vitro data for these compounds whereas pharmacokinetics profile revealed the druglike properties of potent inhibitors.

Discovery of druggable potent inhibitors of serine proteases and farnesoid X receptor by ligand-based virtual screening to obstruct SARS-CoV-2

The coronavirus, a subfamily of the coronavirinae family, is an RNA virus with over 40 variations that can infect humans, non-human mammals and birds. There are seven types of human coronaviruses, including SARS-CoV-2, is responsible for the recent COVID-19 pandemic. The current study is focused on the identification of drug molecules for the treatment of COVID-19 by targeting human proteases like transmembrane serine protease 2 (TMPRSS2), furin, cathepsin B, and a nuclear receptor named farnesoid X receptor (FXR). TMPRSS2 and furin help in cleaving the spike protein of the SARS-CoV-2 virus, while cathepsin B plays a critical role in the entry and pathogenesis. FXR, on the other hand, regulates the expression of ACE2, and its inhibition can reduce SARS-CoV-2 infection. By inhibiting these four protein targets with non-toxic inhibitors, the entry of the infectious agent into host cells and its pathogenesis can be obstructed. We have used the BioSolveIT suite for pharmacophore-based computational drug designing. A total of 1611 ligands from the ligand library were docked with the target proteins to obtain potent inhibitors on the basis of pharmacophore. Following the ADMET analysis and protein ligand interactions, potent and druggable inhibitors of the target proteins were obtained. Additionally, toxic substructures and the less toxic route of administration of the most potent inhibitors in rodents were also determined computationally. Compounds namely N-(diaminomethylene)-2-((3-((1R,3R)-3-(2-(methoxy(methyl)amino)-2-oxoethyl)cyclopentyl)propyl)amino)-2-oxoethan-1-aminium (26), (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((4-propyl-1H-imidazol-2-yl)methyl)piperidin-1-ium (29) and (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((1-propyl-1H-pyrazol-4-yl)methyl)piperidin-1-ium (30) were found as the potent inhibitors of TMPRSS2, whereas, 1-(1-(1-(1H-tetrazol-1-yl)cyclopropane-1‑carbonyl)piperidin-4-yl)azepan-2-one (6), (2R)-4-methyl-1-oxo-1-((7R,11S)-4-oxo-6,7,8,9,10,11-hexahydro-4H-7,11-methanopyrido[1,2-a]azocin-9-yl)pentan-2-aminium (12), 4-((1-(3-(3,5-dimethylisoxazol-4-yl)propanoyl)piperidin-4-yl)methyl)morpholin-4-ium (13), 1-(4,6-dimethylpyrimidin-2-yl)-N-(3-oxocyclohex-1-en-1-yl)piperidine-4-carboxamide (14), 1-(4-(1,5-dimethyl-1H-1,2,4-triazol-3-yl)piperidin-1-yl)-3-(3,5-dimethylisoxazol-4-yl)propan-1-one (25) and N,N-dimethyl-4-oxo-4-((1S,5R)-8-oxo-5,6-dihydro-1H-1,5-methanopyrido[1,2-a][1,5]diazocin-3(2H,4H,8H)-yl)butanamide (31) inhibited the FXR preferentially. In case of cathepsin B, N-((5-benzoylthiophen-2-yl)methyl)-2-hydrazineyl-2-oxoacetamide (2) and N-([2,2'-bifuran]-5-ylmethyl)-2-hydrazineyl-2-oxoacetamide (7) were identified as the most druggable inhibitors whereas 1-amino-2,7-diethyl-3,8-dioxo-6-(p-tolyl)-2,3,7,8-tetrahydro-2,7-naphthyridine-4‑carbonitrile (5) and (R)-6-amino-2-(2,3-dihydroxypropyl)-1H-benzo[de]isoquinoline-1,3(2H)-dione (20) were active against furin.

Identification of potential inhibitors targeting yellow fever virus helicase through ligand and structure-based computational studies

Yellow fever is a flavivirus having plus-sensed RNA which encodes a single polyprotein. Host proteases cut this polyprotein into seven nonstructural proteins including a vital NS3 protein. The present study aims to identify the most effective inhibitor against the helicase (NS3) using different advanced ligand and structure-based computational studies. A set of 300 ligands was selected against helicase by chemical structural similarity model, which are similar to S-adenosyl-l-cysteine using infiniSee. This tool screens billions of compounds through a similarity search from in-built chemical spaces (CHEMriya, Galaxi, KnowledgeSpace and REALSpace). The pharmacophore was designed from ligands in the library that showed same features. According to the sequence of ligands, six compounds (29, 87, 99, 116, 148, and 208) were taken for pharmacophore designing against helicase protein. Subsequently, compounds from the library which showed the best pharmacophore shared-features were docked using FlexX functionality of SeeSAR and their optibrium properties were analyzed. Afterward, their ADME was improved by replacing the unfavorable fragments, which resulted in the generation of new compounds. The selected best compounds (301, 302, 303 and 304) were docked using SeeSAR and their pharmacokinetics and toxicological properties were evaluated using SwissADME. The optimal inhibitor for yellow fever helicase was 2-amino-N-(4-(dimethylamino)thiazol-2-yl)-4-methyloxazole-5-carboxamide (302), which exhibits promising potential for drug development.Communicated by Ramaswamy H. Sarma.

Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity

Schistosomiasis is a chronic parasitic disease caused by trematodes of the genus Schistosoma; it is commonly caused by Schistosoma mansoni, which is transmitted by Bioamphalaria snails. Studies show that more than 200 million people are infected and that more than 90% of them live in Africa. Treatment with praziquantel has the best cost–benefit result on the market. However, hypersensitivity, allergy, and drug resistance are frequently presented after administration. From this perspective, ligand-based and structure-based virtual screening (VS) techniques were combined to select potentially active alkaloids against S. mansoni from an internal dataset (SistematX). A set of molecules with known activity against S. mansoni was selected from the ChEMBL database to create two different models with accuracy greater than 84%, enabling ligand-based VS of the alkaloid bank. Subsequently, structure-based VS was performed through molecular docking using four targets of the parasite. Finally, five consensus hits (i.e., five alkaloids with schistosomicidal potential), were selected. In addition, in silico evaluations of the metabolism, toxicity, and drug-like profile of these five selected alkaloids were carried out. Two of them, namely, 11,12-methylethylenedioxypropoxy and methyl-3-oxo-12-methoxy-n(1)-decarbomethoxy-14,15-didehydrochanofruticosinate, had plausible toxicity, metabolomics, and toxicity profiles. These two alkaloids could serve as starting points for the development of new schistosomicidal compounds based on natural products.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.