Correction to "In Silico Prediction of Fraction Unbound in Human Plasma from Chemical Fingerprint Using Automated Machine Learning".

[This corrects the article DOI: 10.1021/acsomega.0c05846.].

Exploring the mechanism of action of podophyllotoxin derivatives through molecular docking, molecular dynamics simulation and MM/PBSA studies.

The chemical structure of a compound directly affects its biological activity, as different functional groups can change a compound's activity. With this in mind, the current study aims to predict the likely mechanism of action of several podophyllotoxin derivatives whose biological activities have already been documented. The interactions of the derivatives of podophyllotoxin with tubulin (PDB ID: 6NNG) and topoisomerase II (PDB ID: 3QX3), the two recognised targets of podophyllotoxin, were examined using molecular docking experiments. According to the molecular docking result, tubulin, and the investigated variants of podophyllotoxin interact more effectively than topoisomerase. The greatest docking score of the compounds was -12.200 against tubulin and -4.511 against topoisomerase, indicating that tubulin is the target of these drugs. Further to ascertain the strength of the interaction between the best-docked derivatives and the target protein, additional molecular dynamics investigations were also incorporated. With tubulin, the derivatives engage steadily, while with topoisomerase, the ligands shift from the protein's initial binding site to its DNA binding site. MMPBSA analysis was used to examine the stability of their relationship.Communicated by Ramaswamy H. Sarma.

An Interaction Network Driven Approach for Identifying Cervical, Endometrial, Vulvar Carcinomic Biomarkers and Their Multi-targeted Inhibitory Agents from Few Widely Available Medicinal Plants.

Differently expressed genes (DEGs) across cervical (CC), endometrial (EC), and vulvar carcinoma (VC) may serve as potential biomarkers for these progressive tumor conditions. In this study, DEGs of cervical (CC), endometrial (EC), and vulvar carcinoma (VC) were identified by microarray analysis. The interaction network between the identified 124 DEGs was constructed and analyzed to identify the hub genes and genes with high stress centrality. DEGs, namely, CDK1 and MMP9, were found to show highest degree and highest stress centrality respectively from the gene interaction network of 124 nodes and 1171 edges. DEG CDK1 is found to be overlapping in both cervical and endometrial carcinomic conditions while DEG MMP9 is found in vulvar carcinomic condition. Further, as it is studied that many phytochemicals play an important role as medicinal drugs, we have identified phytochemicals from few widely available medicinal plants and performed comprehensive computational study to identify a multi-targeted phytochemical against the identified DEGs, which are crucially responsible for the progression of these carcinomic conditions. Virtual screening of the phytochemicals against the target DEG protein structures with PDB IDs 4Y72 and 1GKC resulted in identifying the multi-targeted phytochemical against both the proteins. The molecular docking and dynamics simulation studies reveal that luteolin can act as a multi-targeted agent. Thus, the interactional and structural insights of luteolin toward the DEG proteins signify that it can be further explored as a multi-targeted agent against the cervical, endometrial, and vulvar carcinoma.

Structure-guided Design and Optimization of small Molecules as Pancreatic Lipase Inhibitors using Pharmacophore, 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation Studies.

BACKGROUND: Obesity has now become a global issue due to the increase in the population of obese people. It also substantially impacts the individual's social, financial, and psychological well-being, which may contribute to depression. Being overweight induces many metabolic and chronic disorders, urging many researchers to focus on developing the drug for obesity treatment. Pancreatic lipase inhibitors and natural product/compound-derived pancreatic lipase inhibitors have recently received much attention because of their structural variety and low toxicity. OBJECTIVE: This study aimed to build pharmacophores and QSAR for analyzing the necessary structure of pancreatic lipase inhibitors and designing new molecules with the best activity. METHODS: Ligand-based pharmacophore modeling and Atom-Based 3D-QSAR were carried out using the PHASE module of Schrodinger to determine the critical structural properties necessary for pancreatic lipase (PL) inhibitory activity. A total of 157 phytoconstituents and a standard drug, orlistat, were selected for the present study. Considering the important features for pancreatic lipase inhibition, 15 new molecules were designed and subjected to molecular docking studies and molecular dynamics simulations. The activity of designed molecules was predicted using the Atom- Based QSAR tool of the PHASE module. RESULTS: The top docked score molecule is structure-7 with a docking score of -6.094 Kcal/mol, whereas the docking score of orlistat and tristin is -3.80Kcal/mol and -5.63Kcal/mol, respectively. CONCLUSION: The designed molecules have a high docking score and good stability, are in the desirable ADME range and are derived from natural products, so they might be used as lead molecules for anti-obesity drug development.

Virtual screening and invitro evaluation of cyclooxygenase inhibitors from Tinospora cordifolia using the machine learning tool.

Tinospora cordifolia has a variety of compounds, and some of these compounds may have anti-inflammatory and antioxidant properties. In the present study, we identified the compounds in the leaf extract of T. cordifolia through Gas Chromatography-Mass Spectrometry (GC-MS) analysis and found the various metabolites. The compounds are screened virtually using a machine learning model, followed by molecular docking and simulation study to identify top-hit compounds as cyclooxygenase (COX) inhibitors. The molecular docking revealed that the compound 7,9-Di-tert-butyl-1-oxaspiro (4,5) deca-6,9-diene-2,8-dione (CID:545303) exhibited the lowest binding energies of -7.1 and -6.8 kcal/mol against COX 1 and COX 2 respectively. The interactions are favored by hydrogen bonding and hydrophobic interaction inside the binding pocket. The 100 ns MD simulation study for these compounds was performed to know the stability and found the RMSD around 2 Å and around 1.0 Å with minimal fluctuations indicating a stable complex throughout the simulation of 100 ns. Based on these findings, we proposed 7,9-Di-tertbutyl- 1-oxaspiro (4,5) deca-6,9-diene-2,8-dione could be used as a dual inhibitor of COX enzymes and a drug-like molecule for treating inflammation after evaluation of their biological properties. The methanolic extract of T. cordifolia was subjected to in vitro DPPH, ABTS, nitric oxide, anti-microbial, COX, and LOX inhibition activity. The results exhibited possible positive effects against the above activities.Communicated by Ramaswamy H. Sarma.

Finding Novel AMPs Secreted from the Human Microbiome as Potent Antibacterial and Antibiofilm Agents and Studying Their Synergistic Activity with Ag NCs.

Due to the enhanced resistance of bacteria to antibiotics, researchers always try to find effective alternatives to treat drug-resistant bacterial infections. In this context, we have explored antimicrobial peptides (AMPs), which are a broad class of small peptide molecules, and investigated their efficacy as potent antibacterial and antibiofilm agents. AMPs can cause cell death either through disruption of the cell membrane or by inhibiting vital intracellular functions, by binding to RNA, DNA, or intracellular components upon transversion through the cell membrane. We attempted to find potent intracellular cationic AMPs that can demonstrate antibacterial activity through interaction with DNA. As a source of AMPs, we have utilized those that are secreted from the human microbiome with the anticipation that these will be non-toxic in nature. Out of the total 1087 AMPs, 27 were screened on the basis of amino acid length and efficacy to cross the cell membrane barrier. From the list of 27 peptides, 4 candidates were selected through the docking score of these peptides with the DNA binding domain of H2A proteins. Further, the molecular dynamics simulation analysis demonstrated that 2 AMPs, i.e., peptides 7 and 25, are having considerable membrane permeation and DNA binding ability. Further, the in vitro analysis indicated that both peptides 7 and 25 could exhibit potent antibacterial and antibiofilm activities. In order to further enhance the antibiofilm potency, the above AMPs were used as supplements to silver nanoclusters (Ag NCs) to get synergistic activity. The synergistic activity of Ag NCs was found to be significantly increased with both the above AMPs.

Antimicrobial Peptides from Human Microbiome Against Multidrug Efflux Pump of Pseudomonas aeruginosa: a Computational Study.

The excess use of antibiotics has led to the evolution of multidrug-resistant pathogenic strains causing worldwide havoc. These multidrug-resistant strains require potent inhibitors. Pseudomonas aeruginosa is a lead cause of nosocomial infections and also feature in the critical priority list of the world health organization (WHO) for the development of new antibiotics against their antimicrobial resistance. Antimicrobial peptides (AMPs) found in almost every life form from microorganisms to humans are known to defend their hosts against various pathogens. Owing to the diversity of the human microbiome, in this study, we have identified the cell-penetrating AMPs from the human microbiome and studied their inhibitory activity against the outer membrane protein OprM of the MexAB-OprM, a constitutively expressed multidrug efflux pump of the Ps. aeruginosa. Screening of the AMPs from the human microbiome resulted in the identification of 147 cell-penetrating AMPs (CPAMPs). The virtual screening of these CPAMPs against the OprM protein showed significant inhibitory results with the top docked AMP showing binding affinity exceeding -30 kcal/mol. The molecular dynamic simulation determined the interaction stabilities between the AMPs and the OprM at the binding site. Further, the residue interaction networks (RINs) are analyses to identify the inhibitory patterns. Later, these patterns were confirmed by MM-PBSA analysis suggesting that the AMPs are majorly stabilized by electrostatic interactions at the binding site. Thus, the high binding affinity and insights from the molecular interaction signify that the identified CPAMPs from the human microbiome can be further explored as inhibitory agents against multidrug-resistant Ps. aeruginosa.

Identification of hub genes in common cancers of women in India and targeting for the search of anticancer agent from Punica granatum phytoconstituent using interaction network analysis and virtual screening.

Cancer is one of the most dreadful diseases across the globe, with the advancement in this field a great advent has been achieved in treating cancer by various therapies like chemotherapy, radiation therapy, hormone therapy, gene therapy, and many more but also the most serious concern associated with the available treatments are the toxicities or the side effects linked to them, apart from this the treatment of many malignancies are still not available, because of these such issues, tremendous research is still going on in the whole world to find a better and more potent treatment option for cancer. Cancer develops due to the synergistic effects of both genetic and epigenetic factors. The mutations that change the normal functioning of the genes are responsible for cancer. Various genes are associated with cancers; many genes are commonly found to be mutated in diverse cancer types. In the present work, the genetic co-relation among the top five common cancers in Indian women has tried to be established, after that the identification of the hub gene was carried out with the use of CytoHubba module of Cytoscape. The hub gene product signaling pathway was then targeted for molecular docking with phytoconstituents of Punica granatum while the stability of the docked protein and ligand complex was validated through Molecular Dynamics Simulation.Communicated by Ramaswamy H. Sarma.

Identification of SARS-CoV-2 inhibitors through phylogenetics and drug repurposing.

The novel coronavirus that has affected the whole world is declared a pandemic by the World Health Organization. Since the emergence of this virus, researchers worldwide have searched for potential antivirals against it. Being an RNA virus, it shows a high rate of mutability and variability in its genome. In the present study, all the reported SARS-CoV-2 genomes isolated from diverse regions of the world available in the GISAID database have been considered for phylogenetic analysis. The strain identified at the root is subjected to phylogenetic analysis with genomes of other known human viruses obtained from NCBI for identifying the nearest viral neighbor. Furthermore, the phylogenetic relationship between various human viruses was used to repurpose the known antiviral drugs towards coronavirus using in silico docking approach. The phylogeny reveals the link of the COVID virus with adenovirus. The known drugs against adenovirus are considered in the present study for drug repurposing through molecular docking analysis. The reference inhibitors of the respective targets were also considered in the docking study. The protein targets, namely protease, endoribonuclease, methyltransferase, phosphatase, and spike protein, are considered for screening with the known drug of adenovirus. Ribavirin, known to treat adenoviral infection, shows the best docking score, suggesting its use as a repurposed drug to treat SARS-CoV-2. Furthermore, the potency of the ribavirin drug is analyzed using molecular dynamics studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02019-6.

In Silico Prediction of Fraction Unbound in Human Plasma from Chemical Fingerprint Using Automated Machine Learning.

Predicting the fraction unbound of a drug in plasma plays a significant role in understanding its pharmacokinetic properties during in vitro studies of drug design and discovery. Owing to the gaining reliability of machine learning in biological predictive models and development of automated machine learning techniques for the ease of nonexperts of machine learning to optimize and maximize the reliability of the model, in this experiment, we built an in silico prediction model of a fraction unbound drug in human plasma using a chemical fingerprint and a freely available AutoML framework. The predictive model was trained on one of the largest data sets ever of 5471 experimental values using four different AutoML frameworks to compare their performance on this problem and to choose the most significant one. With a coefficient of determination of 0.85 on the test data set, our best prediction model showed better performance than other previously published models, giving our model significant importance in pharmacokinetic modeling.

Computational Identification of SARS-CoV-2 Inhibitor in Tinospora cordifolia, Cinnamomum zeylanicum and Myristica fragrans.

A novel coronavirus disease (COVID-19), caused by SARS-CoV-2, has spread over more than 100 countries all over the world. The World Health Organization has recognized Coronavirus as a pandemic and finding an effective drug for this infectious disease is of high importance. In this study, we have explored the potent inhibitors of COVID-19 main protease from Tinospora cordifolia an Ayurvedic herb locally called as Amrita meaning 'immortality' and two other Ayurveda plants namely Cinnamomum zeylanicum and Myristica fragrans. Saponarin, a phytochemical present in Tinospora cordifolia showed a very promising result with the binding affinity of - 8.75 kcal/mol. Remdesivir and Favipiravir, the experimental drugs that are known to show inhibitory activity towards COVID-19 are used as a control. The Docking results were verified by the means of molecular dynamic analysis. This study suggests that Saponarin can be a potential inhibitor for the main protease of the COVID-19.

Immunoinformatic based identification of cytotoxic T lymphocyte epitopes from the Indian isolate of SARS-CoV-2.

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has turned into a pandemic with about thirty million confirmed cases worldwide as of September 2020. Being an airborne infection, it can be catastrophic to populous countries like India. This study sets to identify potential cytotoxic T lymphocyte (CTL) epitopes in the SARS-CoV-2 Indian isolate which can act as an effective vaccine epitope candidate for the majority of the Indian population. The immunogenicity and the foreignness of the epitopes towards the human body have to be studied to further confirm their candidacy. The top-scoring epitopes were subjected to molecular docking studies to study their interactions with the corresponding human leukocyte antigen (HLA) system. The CTL epitopes were observed to bind at the peptide-binding groove of the corresponding HLA system, indicating their potency as an epitope candidate. The candidacy was further analyzed using sequence conservation studies and molecular dynamics simulation. The identified epitopes can be subjected to further studies for the development of the SARS-CoV-2 vaccine.

Discovery of Novel Coumarin Analogs against the α-Glucosidase Protein Target of Diabetes Mellitus: Pharmacophore-Based QSAR, Docking, and Molecular Dynamics Simulation Studies.

Diabetes mellitus (DM) is a chronic metabolic disease, the third killer of mankind. The finding of potent drugs against diabetes remains challenging. In the present study, coumarin derivatives with known biological activity against diabetic protein have been used to predict functional groups' positions on coumarin derivatives. α-Glucosidase is a brush border membrane-bound lysosomal enzyme from the hydrolase enzyme family. It plays an important role in the metabolism of glycoproteins. Inhibitors of lysosomal α-glucosidase can reduce postprandial hyperglycemia. Due to this, lysosomal α-glucosidase is a good therapeutic target for drugs. A total of 116 coumarin derivatives with IC50 values against lysosomal α-glucosidase were selected for a CADD (computer-aided drug design) approach to identify more potent drugs. Pharmacophore modeling and atom-based 3-QSAR of 116 active compounds against lysosomal α-glucosidase were performed and identified positions and types of groups to increase activity. We performed molecular docking of 116 coumarin derivatives against the lysosomal α-glucosidase enzyme, and three compounds (isorutarine, 10_, and 36) resulted in a docking score of -7.64, -7.12, and -6.86 kcal/mol. The molecular dynamics simulation of the above three molecules and protein complex performed for 100 ns supported the interaction stability of isorutarine, 10_, and 36 with the lysosomal binding site α-glucosidase.