PyRx Software Research Articles

Functional analysis, virtual screening, and molecular dynamics revealed potential novel drug targets and their inhibitors against cardiovascular disease in human

Cardiovascular disease (CVD) is a group of diseases, affecting the human heart and accounting for 30% of deaths worldwide. Major CVDs include heart failure, hypertension, stroke, etc. Various therapeutics are available against CVD, still there is a dire need to find out potential protein drug targets to reduce economic burden and mortality rate. Goal of the current study was to utilize sequential computational techniques to find the best cardiovascular drug targets and their inhibitors. Common human cardiovascular targets of both databases (GeneCards and Uniprot) were subjected to bioinformatics analyses. Purpose was to validate putative therapeutic targets employing the structure-based bioinformatics methods to determine their physiochemical properties and biological processes. Three stable proteins, that have 0 transmembrane helices, and possess biological processes were screened as potential protein-based therapeutic targets: Hemoglobin subunit beta (HBB), Gamma-enolase (ENO2), and Cholesteryl ester transfer protein (CETP). Tertiary structures of target proteins were retrieved from PDB, and molecular docking technique was utilized to evaluate a library of 5000 phytochemicals against the interacting residues of the target protein as well as their respective standard drugs through MOE and Pyrx software. Top five phytochemicals (d-Sesamin, 1,3-benzodioxole, Sativanone, Thiamine, and Cajanol) were identified based on their RMSD and docking scores as compared to their standard drugs. The docking studies were also validated by MM-GBSA binding free energy and molecular dynamics simulations. According to the study’s findings, these phytochemicals may eventually be used as drugs to treat CVD. Further in vitro testing is required to confirm their efficacy and drug potency. Communicated by Ramaswamy H. Sarma

Potential of fenugreek (Trigonella foenum-graecum L.) metabolites as dental residual ridge resorption inhibitors through estrogen receptor signaling: In silico study

Context: Edentulism is a global health burden with long-life disabilities. Dental implant procedures are frequently constrained to alveolar residual ridge resorption. The resorption is strongly correlated with an imbalance of osteoblast-osteoclast activities. Estrogen well established maintain bone homeostasis. Fenugreek (Trigonella foenum-graecum L.) is a promising drug candidate to promote estrogenic activity and resist residual ridge resorption. Aims: To evaluate the drug-likeness, pharmacokinetic, toxicity as well as molecular interaction prediction between T. foenum-graecum metabolite compounds and estrogen receptor-α (ESRα)/estrogen receptor-ß (ESRß). Methods: The drug-likeness was evaluated using Lipinski's rule of five. A web server (PkCSM) was used to predict the pharmacokinetics and toxicity. The metabolites were retrieved from the Kanaya database. The biological activity prediction was performed using PASS Online. The ligands’ structures were downloaded from PubChem National Centre for Biotechnology Information database. Proteins were obtained from Protein Data Bank. Molecular docking prediction was carried out using PyRx software and visualized using Discovery Studio Software. Results: Quercetin, naringenin, luteolin, and kaempferol displayed the highest biological activity of estrogen agonist, anti-inflammatory, and tumor necrosis factor (TNF) expression inhibitor. Four metabolites exhibited the potential to be developed as oral drugs with good absorption in gastrointestinal, moderate tissue distribution, and safe for liver tissue. Quercetin and kaempferol showed the most negative value of binding affinity. Quercetin formed three hydrogen bonds and eight hydrophobic bonds with ESRα. Luteolin had three hydrogen bonds and five hydrophobic bonds on ESRβ. Conclusions: Metabolites present in T. foenum-graecum, like quercetin, naringenin, luteolin, and kaempferol, were potential drug candidates that could act as estrogen agonists to inhibit the dental residual ridge resorption.

In Silico Protein-Ligand Interaction Study of Selected Phytochemicals Against Monoamine Oxidase-B

Parkinson’s disease (PD) is a progressive neurodegenerative disease associated with the death of dopaminergic neurons in the substantia nigra pars compacta. Monoamine oxidase B (MAO-B) inhibitors lessen the degree of PD but still present various side effects. Natural products such as morin, piceatannol, resveratrol, and gallic acid are vital phytochemicals from plants with numerous health benefits. This in silico study aimed to identify potent natural MAO-B inhibitors that could serve as better alternatives to the known MAO-B inhibitors (rasagiline and selegiline). The crystal structure of MAO-B (PDB ID: 2C65) was retrieved from the Protein Data Bank (RSCB) and prepared for molecular docking via Discovery Studio 2020 software. Molecular docking between MAO-B and morin, piceatannol, resveratrol, gallic acid, rasagiline, and selegiline utilised the PyRx software. The Discovery Studio 2020 software was used for visualization. The SwissADME server was used to study the physiochemical properties (Lipinski rule of five), pharma-cokinetic parameters, and absorption, distribution, metabolism, elimination, and toxicity (ADMET) profiles of morin. Comparing results with binding affinity indicated that morin (-10.0 kcal/mol) has a superior potency against 2C65 than piceatannol (-9.0 kcal/mol), resveratrol (-807 kcal/mol), dopamine (-6.4 kcal/mol), gallic acid (-6.3 kcal/mol), rasagiline (-8.0 kcal/mol), and selegiline (-7.4 kcal/mol). Drug candidates comply with all five of Lipinski’s drug-likeness rules with appropriate ADMET properties. Overall, the molecular docking results suggest that morin may be considered a suitable therapeutic candidate for PD treatment.

Virtual screening of the natural antifoulants: In silico approach to screen lichen metabolites against marine biofoulers

AbstractThe present study aims to explore the inhibitory action of lichen metabolites against important protein targets of marine fouling organisms using computational methods. Lichens with their unique ability to produce various secondary metabolites find versatile applications in Biotechnology and allied fields. The 3D structure of target proteins associated with fouling activity of marine organism were collected from the protein data bank (PDB) and the computational docking analysis was performed using the PyRx software with 40 lichen compounds that are collected from PubChem. The lichen compounds were docked against the following protein targets—mannose‐specific lectin CGL1 of Crassostrea gigas, Proximal thread matrix protein 1 PTMP 1 of Mytilus galloprovincialis, and G protein‐coupled receptor (GPCR) of Balanus amphitrite. As there was no experimentally determined structure available for GPCR of B. amphitrite, homology modeling was performed to predict the protein structure. The docking study showed effective binding of lichen compounds to various fouling targets with the binding energies in the range of −10.1 to −5 kcal/mol. Among the lichen compounds studied, roccanin showed the least binding energy for CGL1, PTMP1 and GPCR. The correlation assessments predict a positive relationship between binding energy, molecular weight and retention time. MD Simulation studies were performed using LARMD online tool and the RMSD and radius of gyration (Rg) values was recorded. This study proves that the compounds produced by lichens have high antifouling effect against marine foulers under in silico conditions.

Hecogenin a Plant Derived Small Molecule as an Antagonist to BACE-1: A Potential Target for Neurodegenerative Disorders.

The field of drug discovery has recognized the significance of computer-aided drug design. Recent advancements in structure identification and characterization, bio-computational science and molecular biology have significantly contributed to the development of novel treatments for various diseases. Alzheimer's disease is prevalent in over 50 million affected people, with the pathological condition of amyloidal plaque formation by the beta-amyloidal peptide that results in lesions of the patient's brain, thus making the target prediction and treatment a hurdle. In this study, we evaluated the potential of 54 bioactive compounds from Justicia adhatoda L. and Sida cordifolia L. identified through LC-MS/MS against the β-site amyloid precursor cleaving enzyme (beta-secretase) that results in the formation of amyloidal plaques. To study the drug-likeness of the phytocompounds, Lipinski's rule of five for ADME profiling and toxicity prediction was performed. Molecular docking was performed using auto-dock tool of PyRx software; molecular dynamic simulations were performed using the Schrodinger suite. Molecular docking against BACE-1 protein revealed that hecogenin, identified from S. cordifolia has a broad spectrum of pharmacological applications and a binding affinity score of -11.3 kcal/Mol. The Hecogenin-BACE-1 protein complex was found to be stable after 30 ns of MD simulation, resulting in its substantial stability. Further studies focusing on the in vivo neuroprotective activity of hecogenin against the disease will pave the way for efficient drug discovery from natural sources in a precise manner.

Cyanobacterial compound Tolyporphine K as an inhibitor of Apo-PBP (penicillin-binding protein) in A. baumannii and its ADME assessment

Antibiotic-resistant Acinetobacter baumannii, is a common pathogen found in hospital settings and has become nosocomial due to its high infection-causing tendency amongst ICU patients. The present study explores the cyanocompoundswhich were capable to inhibit the Penicillin Binding Protein of A. baumannii through molecular docking, ADMET, and molecular dynamicssimulation strategy. A database having structural and origin details was generated for 85 bioactive compounds in MS Excel. The 3-D structures weredownloaded from the PubChem database and minimized. The receptor protein was minimized and validated for structure correctness. The database was screened against the penicillin-binding protein of A. baumannii through PyRx software. The top 5 compounds including the control molecule werefurther redocked to the receptor molecule through Autodock Vina software. The molecule pose having the highest affinity was further subjected to 100ns MD- simulation and simultaneously the in-vitro activity of the methanol extract and hexane extract was checked through agar well diffusion assay.Docking studies indicate Tolyporphine K to be a lead molecule which was further assessed through Molecular dynamics and MM/PBSA. The in-silicoresults suggested that the protein-ligand complex was found to be stable over the 100 ns trajectory with a binding free energy of -8.56 Kcalmol-1. Theligand did not induce any major structural conformation in the protein moiety and was largely stabilized by hydrophobic interactions. The bioactivityscore and ADME properties of the compounds were also calculated. The in-vitro agar well diffusion assay showed a moderate zone of inhibition of12.33mm. The results indicate that the compound Tolyporphin- K could be a potential inhibitor of penicillin-binding protein in A. baumannii. Yet furtherwork needs to be done to have a more concrete basis for the pathway of inhibition. Communicated by Ramaswamy H. Sarma

In Silico Study of the 5-Hydroxytryptamine-2C Receptor Antagonist Activity of Anthocyanins as Antidepressant Therapy

This study aimed to evaluate the drug-likeness, pharmacokinetic and safety prediction of six types of anthocyanins (ANC) as well as virtual molecular interaction between ANC and 5-hydroxytryptamine-2C (5HT-2C) receptor for antagonist target of antidepressant drug development. The Lipinski rule of five was used to predict the oral drug-likeness of ANC. The pharmacokinetic and safety prediction was analyzed with a free accessible web server. The ligands of ANC were retrieved from PubChem National Centre for Biotechnology Information (NCBI) database. The protein of the 5HT-2C receptor was obtained from Protein Data Bank. Molecular docking was performed by PyRx software and visualized using Discovery Studio Software. The results showed ANC is proposed as an oral drug candidate. The pharmacokinetic prediction of ANC was demonstrated to have high absorption in the intestinal route, solubility in the aqueous phase, capability to evade hepatic first-pass metabolism and high total clearance from the kidney. Virtual toxicity prediction showed a higher threshold of chronic lethal dose than control with no toxicity on the salmonella typhimurium reverses mutation assay (AMES) test, liver, and skin. Molecular prediction found ANC type of delphinidin has the most similar interaction site with the control antagonist ligand on the 5HT-2C receptor which is facilitated with hydrogen bonds and hydrophobic bonds at amino residues of Trp324, Phe328, Ala222 and Val135. We concluded ANC particularly delphinidin is proposed as an oral drug candidate potentially used as a 5HT-2C receptor antagonist and thus, further in vitro and in vivo studies are necessary to confirm the effect on antidepressant activity.

Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis

Liver cancer is a malignant tumor that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer. A list of studies evidences the therapeutic efficacy of Bacopa monnieri against liver cancer, but the precise molecular mechanism is yet to be discovered. This study combines data mining, network pharmacology, and molecular docking analysis to potentially revolutionize liver cancer treatment by identifying effective phytochemicals. Initially, the information on active constituents of B. monnieri and target genes of both liver cancer and B. monnieri were retrieved from literature as well as from publicly available databases. Based on the matching results between B. monnieri potential targets and liver cancer targets, the protein-protein interaction (PPI) network was constructed using the STRING database and imported into Cytoscape for screening of hub genes based on their degree of connectivity. Later, the interactions network between compounds and overlapping genes was constructed using Cytoscape software to analyze the network pharmacological prospective effects of B. monnieri on liver cancer. Gene Ontology (GO) and KEGG pathway analysis of hub genes revealed that these genes are involved in the cancer-related pathway. Lastly, the expression level of core targets was analyzed using microarray data (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790). Further, the GEPIA server and PyRx software were used for survival and molecular docking analysis, respectively. In summary, we proposed that quercetin, luteolin, apigenin, catechin, epicatechin, stigmasterol, beta-sitosterol, celastrol, and betulic acid inhibit tumor growth by affecting tumor protein 53 (TP53), interleukin 6 (IL6), RAC-alpha serine/threonine protein kinases 1 (AKT1), caspase-3 (CASP3), tumor necrosis factor (TNF), jun proto-oncogene (JUN), heat shot protein 90 AA1 (HSP90AA1), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), and SRC proto-oncogene (SRC). Through, microarray data analysis, the expression level of JUN and IL6 were found to be upregulated while the expression level of HSP90AA1 was found to be downregulated. Kaplan–Meier survival analysis indicated that HSP90AA1 and JUN are promising candidate genes that can serve as diagnostic and prognostic biomarkers for liver cancer. Moreover, the molecular docking and molecular dynamic simulation of 60ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between the compound and binding pockets of HSP90AA1 and JUN. Despite that, in vivo and in vitro studies are mandatory to unveil pharmacokinetics and biosafety profiles to completely track the candidature status of B. monnieri in liver cancer.

Wnt/β-catenin signaling pathway inhibitors, glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, and rutin in medicinal plants have better binding affinities and anticancer properties: Molecular docking and ADMET study.

Wnt/β-catenin signaling pathway plays a role in cancer development, organogenesis, and embryogenesis. The abnormal activation promotes cancer stem cell renewal, proliferation, and differentiation. In the present study, molecular docking simulation and ADMET studies were carried out on selected bioactive compounds in search of β-catenin protein inhibitors for drug discovery against cancer. Blind docking simulation was performed using PyRx software on Autodock Vina. β-catenin protein (PDB ID: 1jdh) and 313 bioactive compounds (from PubChem database) with selected standard anticancer drugs were used for molecular docking. The ADMET properties of the best-performing compounds were calculated using SwissADME and pkCMS web servers. The results obtained from the molecular docking study showed that glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, and rutin had the best binding interactions with β-catenin protein based on their binding affinities. Glycyrrhizic acid and solanine had the same and lowest binding energy of -8.5 kcal/mol. This was followed by polyphyllin I with -8.4 kcal/mol, and crocin, hypericin, and tubeimoside-1 which all had a binding energy of 8.1 kcal/mol. Other top-performing compounds include diosmin and rutin with binding energy of -8.0 kcal/mol. The ADMET study revealed that the following compounds glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, rutin, and baicalin all violated Lipinski's rule of 5 which implies poor oral bioavailability. However, based on the binding energy score, it was suggested that these pharmacologically active compounds are potential molecules to be tested against cancer.

In Silico Subtractive Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pneumoniae Strain D39.

Streptococcus pneumoniae is a notorious Gram-positive pathogen present asymptomatically in the nasophayrnx of humans. According to the World Health Organization (W.H.O), pneumococcus causes approximately one million deaths yearly. Antibiotic resistance in S. pneumoniae is raising considerable concern around the world. There is an immediate need to address the major issues that have arisen as a result of persistent infections caused by S. pneumoniae. In the present study, subtractive proteomics was used in which the entire proteome of the pathogen consisting of 1947 proteins is effectively decreased to a finite number of possible targets. Various kinds of bioinformatics tools and software were applied for the discovery of novel inhibitors. The CD-HIT analysis revealed 1887 non-redundant sequences from the entire proteome. These non-redundant proteins were submitted to the BLASTp against the human proteome and 1423 proteins were screened as non-homologous. Further, databases of essential genes (DEGG) and J browser identified almost 171 essential proteins. Moreover, non-homologous, essential proteins were subjected in KEGG Pathway Database which shortlisted six unique proteins. In addition, the subcellular localization of these unique proteins was checked and cytoplasmic proteins were chosen for the druggability analysis, which resulted in three proteins, namely DNA binding response regulator (SPD_1085), UDP-N-acetylmuramate-L-alanine Ligase (SPD_1349) and RNA polymerase sigma factor (SPD_0958), which can act as a promising potent drug candidate to limit the toxicity caused by S. pneumoniae. The 3D structures of these proteins were predicted by Swiss Model, utilizing the homology modeling approach. Later, molecular docking by PyRx software 0.8 version was used to screen a library of phytochemicals retrieved from PubChem and ZINC databases and already approved drugs from DrugBank database against novel druggable targets to check their binding affinity with receptor proteins. The top two molecules from each receptor protein were selected based on the binding affinity, RMSD value, and the highest conformation. Finally, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses were carried out by utilizing the SWISS ADME and Protox tools. This research supported the discovery of cost-effective drugs against S. pneumoniae. However, more in vivo/in vitro research should be conducted on these targets to investigate their pharmacological efficacy and their function as efficient inhibitors.

NETWORK PHARMACOLOGY AND MOLECULAR DOCKING-BASED PREDICTIONS OF PHARMACOLOGICAL EFFECTS OF FERULIC ACID

Objectives: The main objective of this study is to reveal new possible pharmacological effects of ferulic acid. This is achieved by network pharmacology by discovering potential target genes for ferulic acid, along with constructing a PPI network for those targets and performing gene enrichment analysis to understand possible diseases or disorders being affected due to the target genes. The study involves the molecular docking of target genes with ferulic acid to understand the interactions between them. Methods: ADMETlab 2.0 was used for the pharmacokinetics study of ferulic acid. Using SwissTargetPrediction and STITCH database 79 target genes were retrieved which were used to construct a PPI network using the STRING database and for gene enrichment analysis using the ShinyGo tool. Analyzing the clusters generated by k-means clustering in the STRING database, three target gene proteins were further used to perform molecular docking with ferulic acid using PyRx software, and 2D and 3D visualization was done using Biovia Discovery Studio Visualizer. Results: The ADMET analysis ferulic acid showed drug-likeliness. SwissTargetPrediction and STITCH database revealed 79 potential target genes. Three proteins (RELA, ALOX15, and STAT3) were selected from the PPI network analysis using the STRING database for molecular docking and visualization. ALOX15 showed the least binding energy among all three target proteins. Gene enrichment analysis suggests the target proteins are involved in cancer, neurological disorders, psychiatric disorders, Alzheimer’s disease, etc. Conclusion: The findings of this research suggest that ferulic acid may have a wide range of pharmacological effects and gives a new perspective on its application in the field of drug discovery.

Screening and introduction of key cell cycle microRNAs deregulated in colorectal cancer by integrated bioinformatics analysis.

Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men worldwide. Impaired cell cycle regulation leads to many cancers and is also approved in CRC. Therefore, cell cycle regulation is a critical therapeutic target for CRC. Furthermore, miRNAs have been discovered as regulators in a variety of cancer-related pathways. This study is designed to investigate how miRNAs and mRNAs interact to regulate the cell cycle in CRC patients. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Expression Omnibus (GEO), and Therapeutic Target Database (TTD), cell cycle-associated genes were identified and evaluated. Seven of the 22 differentially expressed genes (DEGs) implicated in the cell cycle in three GSEs (GSE24514, GSE10950, and GSE74604) were identified as potential therapeutic targets. Then, using PyRx software, we performed docking proteins with selected drugs. The results demonstrated that these drugs are appropriate molecules for targeting cell cycle DEGs. Tarbase, miRTarbase, miRDIP, and miRCancer databases were used to find miRNAs that target the indicated genes. The ability of these six miRNAs to impact the cell cycle in colorectal cancer may be concluded. These miRNAs were found to be downregulated in SW480 cells when compared to the normal tissue. Our data imply that a precise selection of bioinformatics tools can facilitate the identification of miRNAs that impact mRNA translation at different stages of the cell cycle. The candidates can be investigated more as targets for cell cycle arrest in cancers.

Probable mode of action of Sida Cordifolia in Dyspareunia an In-Silico network Pharmacological approach

Dyspareunia, a painful sexual disorder can be correlated to Paripluta yonivyapath (disorders of the genital tract) as per Ayurveda classics, which comes under the category of Female sexual dysfunction, the less explored clinical field in Ayurveda. Bala, Sida cordifolia Linn is the single drug that is directly indicated in this disease condition. This is the era in which the whole medical world is running behind in standardizing Evidence-based medicine (EBM) in clinical application. Network pharmacology, a branch of science that combines Network biology and polypharmacology, which predicts the mode of action of therapeutic drugs on both the interactome and the diseasome level. Here is an attempt to put forth the therapeutic efficacy of Bala in Dyspareunia through network pharmacology and bonding energy was demonstrated by molecular docking. Methods: Phytochemicals of Bala were collected from the IMPPAT database and related research articles. The disease targets were obtained from databases namely the Therapeutic target database, ChEMBL, Human protein atlas, and DisGeNet. The therapeutic efficacy of these gene targets in dyspareunia is confirmed and the conclusion is obtained from the network pharmacological ligand-target interaction methodology. The ligands and targets were retrieved from the PubChem, Protein Data Bank and docked using PyRx software. Results and Conclusion: The current study identified important Phyto-constituents like Resin Acid, Malvalic Acid, 5,7-Dihydroxy-3-Isoprenyl Flavones, Peganine, Β-Phenethylamine, Ψ-( Pseudo) -Ephedrine, Coronaric Acid, Potassium Nitrate, Phenethylamine, Ephedrine, Choline which were highly modulating CYP19A1, ESR2, MAPK1, AR, MAPK3, ESR1, CYP19A1, PGR proteins related with Dyspareunia.

Exploration of bioactive compounds from Mangifera indica (Mango) as probable inhibitors of thymidylate synthase and nuclear factor kappa-B (NF-Κb) in colorectal cancer management

Abstract This research is aimed at investigating the anti-colorectal cancer activities of phytochemicals from Mangifera indica (Mango) via the inhibition of thymidylate synthase (TS) and Nuclear Factor kappa B (NF–κB) using computational chemistry tools. Ligands (141 phytochemicals previously isolated from mangoes) and reference drugs (Raltitrexed and Emetine), the drug inhibitors of TS and NF–κB, respectively) were subjected to screening via ADMET profiling, drug-likeness analysis, oral bioavailability, PASS profile, and molecular interactions. Ligands that passed the previously mentioned screening were docked in duplicate against the target receptors (TS and NF–κB) using PyRx software. The mean values were calculated to obtain suitable docking scores. The analysis showed that TS was strongly inhibited by Friedelan-3beta-Ol with its lower binding energy of −9.0 kcal/mol more than Raltitrexed with a binding energy of −8.7 kcal/mol. NF–κB was also inhibited by Friedelan-3beta-Ol and Friedelin with binding energies of −8.0 and −8.1 kcal/mol, respectively, more than Emetine with a binding energy of −6.4 kcal/mol. These two phytochemicals performed much better than the standard drugs, thus selected as the best hits compounds because of their ADMET profile, drug-likeness properties, bioactivity, oral bioavailability, PASS prediction, binding affinities, and their interactions with the amino acids in the active sites of the receptors. Therefore, further studies are necessary for the validation of these claims toward the development of new effective and safer anti-colorectal cancer drugs.

Novel 1, 3, 4-Oxadiazole-pyridine hybrids as potential DNA gyrase B inhibitors (5D7R): ADMET prediction and molecular docking study

A small molecule (ligand) is placed in the binding site of its macromolecular target (receptor) using a computational process called molecular docking, which also calculates the binding affinity of the small molecule. With the use of PyRx software, the current study tried a high-throughput in-silico screening of 16 compounds docked with the crystal structure of DNA gyrase B receptors (PDB ID: 5D7R). In the range of -8.0 and -8.1, 3 of these 16 compounds displayed very good mol dock scores. As a typical medicine, amoxicillin medications have a mol dock score of -7.1. According to the results, all of the investigated ligands occupy similar positions and directions within the putative binding site of DNA gyrase B receptors (PDB ID: 5D7R), which reveals a sizable area surrounded by a membrane binding domain that acts as a pathway for substrate entry into the active site. Additionally, any small molecule's affinity can be viewed as a special instrument in the field of drug design and provide a possibility for future study to create an antibacterial activity. Additionally, ADME evaluations must be used to confirm compounds that are candidates for oral administration. The findings demonstrated that compound 4a, 4b, 4c, 4d, 4i, 4j, and 4k absorbed from GIT and compound 4i, 4j, and 4k fulfilled the Lipinski rule.
 Keywords: 1, 3, 4-Oxadiazole, ADME Evaluation, molecular docking, antimicrobial activity

Kajian in Silico Daun Sungkai (Peronema canescens) dalam Menghambat Enzim lanosterol 14-α demethylase Jamur Candida albicans

Candida albicans (C. albicans) is a fungus that causes candidiasis. C. albicans has the enzyme Lanosterol 14- α demethylase. Lanosterol 14-α-demethylase has bioactivity in converting lanosterol to ergosterol, a special sterol found in fungal membranes, which mediates membrane permeability and fluidity. One way to treat candidiasis is to use traditional medicinal plants. Sungkai (Peronema canescens) can be used as an anti-fungal medicine. Drug development efforts can be done using the In silico method. Objectives of this study was to determine the interaction between terpenoid, flavonoid, and phenol compounds in Sungkai leaves against Lanosterol 14-α demethylase in Candida albicans. The research design used was a pre-experimental one-shot case study. Toxtree software was used to test the toxicity of compounds. Test for compound potency using the Pass Online webserver. Docking molecular using PyRx software. Visualization of docking results using Discovery Studio 2019 Software. Physicochemical test of compounds using Lipinski Test. The results showed that the compounds that had a low risk of toxicity were butanoic, catechol, guaiacol, hydroquinone, isopropanol, methanoic acid, palmitic acid, and phytol. Compounds that have anti-fungal activity based on Pharma-expert analysis are butanoic acid, genkwanin, guaiacol, hydroquinone, isopropanol, palmitic acid, and phytol. Compounds that have hydrogen bonds and binding affinity value 10 and RMSD value 2 are butanoic acid, catechol, genkwanin, guaiacol, hydroquinone, isopropanol, methanoic acid, palmitic acid, and quinic acid compounds. These compounds are thought to inhibit the Lanosterol 14-alpha demethylase enzyme in C. albicans. Compounds that comply with Lipinski's rules are anthocyanin and genkwanin compounds. The groups of compounds found in Sungkai leaves that have antifungal activity are the terpenoids, flavonoids, and phenols.

Identification of Potent Natural Inhibitor Against Papain Like Protease of SARS CoV 2 an in Silico Approach

One of the most complicated tasks the healthcare system has faced in recent years has been the development of a curative treatment to stop the progression of the SARS CoV-2 virus. No consensus has been reached on a medical cure to slow the virus spread. From this point of view, investigating existing drugs such as SARS-CoV-2 inhibitors is an appropriate technique. With critical involvement in viral replication and host-immune suppression, Papain-like protease (PL-pro) is recognized as a key enzyme target for drug development among other SARS-CoV-2 druggable targets. Phytolignans have a wide range of physiological effects, making them an appealing drug for antiviral study. We used an insilico method to target SARS CoV-2 PL-pro with phytolignans in our investigation. The chemical structures of phytolignans were obtained from PubChem, whereas the protease structure 6WX4was obtained from the Protein Data Bank website. The PyRx software was used for molecular docking.Of all the phytolignans examined, Sesamolin has the greatest binding affinity of -8.4 kcal/mol towards PL-pro.The docking results revealed that phytolignans are potent inhibitors of the SARS-CoV-2 papain-like protease and that they may be verified further in vitro and in vivo. Our findings suggest that Sesamolin might be used as a medication to block the action of SARS CoV-2 PL-pro.

Curcumin from Curcuma longa L. as Dual Inhibitors Against Indonesian SARS-CoV-2 Isolates: A Molecular Docking Study

COVID-19 has become a global pandemic since 2020. The search for promising drugs based on the abundant herbal ingredients in Indonesia is one of the breakthroughs. Curcumin is a chemical compound with various potentials such as antioxidant, anti-inflammatory and antiviral. We conducted a molecular docking analysis to determine the potential of curcumin against SARS-CoV-2 non-structural and structural proteins, such as the main protease and spike protein. This study used the compound of curcumin (PubChem CID: 969516) from Curcuma longa L. or turmeric and two Indonesian SARS-CoV-2 isolates that have been deposited in the GISAID database (hCoV-19/Indonesia/JI-PNF-217315/2021 - EPI_ ISL_12777089 or lineage B.1.617.2 and hCoV-19/Indonesia/JI-PNF-211373/2021 - EPI_ISL_6425649 or lineage B.1.470). In addition, we used molnupiravir (PubChem CID: 145996610) as a drug control. We performed molecular docking analysis with PyRx software 0.9.9 (academic license) and visualization of molecular docking results with PyMOL software 2.5.4 (academic license). The results of this study found that curcumin had good potential against main protease and spike protein compared to the drug (control). In summary, we suggested that curcumin is a potential drug candidate against SARS-CoV-2. However, there is a need for future wet laboratory-based pre-clinical research such as in vitro and in vivo.

Multi-Targeted Molecular Docking and Drug-Likeness Evaluation of some Nitrogen Heterocyclic Compounds Targeting Proteins Involved in the Development of COVID-19.

The severe acute respiratory syndrome coronavirus-2 is causing a disaster through coronavirus disease-19 (COVID-19), affecting the world population with a high mortality rate. Although numerous scientific efforts have been made, we do not have any specific drug for COVID-19 treatment. Aim of the present study was to analyse the molecular interaction of nitrogen heterocyclic based drugs (hydroxychloroquine, remdesivir and lomefloxacin) with various SARSCoV- 2 proteins (RdRp, PLPro, Mpro and spike proteins) using a molecular docking approach. We have performed docking study using PyRx software, and Discovery Studio Visualizer was used to visualise the molecular interactions. The designed nitrogen heterocyclic analogues were checked for Lipinski's rule of five, Veber's Law and Adsorption, Distribution, Metabolism, and Excretion (ADME) threshold. After obtaining the docking results of existing nitrogen heterocyclic drugs, we modified the selected drugs to get molecules with better affinity against SARS-CoV-2. Hydroxychloroquine bound to RdRp, spike protein, PLPro and Mpro at -5.2, -5.1, -6.7 and -6.0 kcal/mol, while remdesivir bound to RdRp, spike protein, PLPro, and Mpro at -6.1, -6.9, -6.4 and -6.9 kcal/mol, respectively. Lomefloxacin bound to RdRp, spike protein, PLPro and Pro at -6.4, -6.6, -7.2 and -6.9 kcal/mol. ADME studies of all these compounds indicated lipophilicity and high gastro intestine absorbability. The modified drug structures possess better binding efficacy towards at least one target than their parent compounds. The outcome reveals that the designed nitrogen heterocyclics could contribute to developing the potent inhibitory drug SARS-CoV-2 with strong multi-targeted inhibition ability and reactivity.

Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature

ObjectiveThe coronavirus disease 2019 (COVID-19) health crisis that began at the end of 2019 made researchers around the world quickly race to find effective solutions. Related literature exploded and it was inevitable that an automated approach was needed to find useful information, namely text mining, to overcome COVID-19, especially in terms of drug candidate discovery. While text mining methods for finding drug candidates mostly try to extract bioentity associations from PubMed, very few of them mine with a clustering approach. The purpose of this study was to demonstrate the effectiveness of our approach to identify drugs for the prevention of COVID-19 through literature review, cluster analysis, drug docking calculations, and clinical trial data. MethodsThis research was conducted in four main stages. First, the text mining stage was carried out by involving Bidirectional Encoder Representations from Transformers for Biomedical to obtain vector representation of each word in the sentence from texts. The next stage generated the disease-drug associations, which were obtained from the correlation between disease and drug. Next, the clustering stage grouped the rules through the similarity of diseases by utilizing Term Frequency-Inverse Document Frequency as its feature. Finally, the drug candidate extraction stage was processed through leveraging PubChem and DrugBank databases. We further used the drug docking package AUTODOCK VINA in PyRx software to verify the results. ResultsComparative analyses showed that the percentage of findings using mining with clustering outperformed mining without clustering in all experimental settings. In addition, we suggest that the top three drugs/phytochemicals by drug docking analysis may be effective in preventing COVID-19. ConclusionsThe proposed method for text mining utilizing the clustering method is quite promising in the discovery of drug candidates for the prevention of COVID-19 through the biomedical literature.