Molecular dynamics simulation approach for discovering potential inhibitors against SARS-CoV-2: A structural review

Examining sialic acid derivatives as potential inhibitors of SARS-CoV-2 spike protein receptor binding domain

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has been the primary reason behind the COVID-19 global pandemic which has affected millions of lives worldwide. The fundamental cause of the infection is the molecular binding of the viral spike protein receptor binding domain (SP-RBD) with the human cell angiotensin-converting enzyme 2 (ACE2) receptor. The infection can be prevented if the binding of RBD-ACE2 is resisted by utilizing certain inhibitors or drugs that demonstrate strong binding affinity towards the SP RBD. Sialic acid based glycans found widely in human cells and tissues have notable propensity of binding to viral proteins of the coronaviridae family. Recent experimental literature have used N-acetyl neuraminic acid (Sialic acid) to create diagnostic sensors for SARS-CoV-2, but a detailed interrogation of the underlying molecular mechanisms is warranted. Here, we perform all atom molecular dynamics (MD) simulations for the complexes of certain Sialic acid-based molecules with that of SP RBD of SARS CoV-2. Our results indicate that Sialic acid not only reproduces a binding affinity comparable to the RBD-ACE2 interactions, it also assumes the longest time to dissociate completely from the protein binding pocket of SP RBD. Our predictions corroborate that a combination of electrostatic and van der Waals energies as well the polar hydrogen bond interactions between the RBD residues and the inhibitors influence free energy of binding.Communicated by Ramaswamy H. Sarma.

From petals to healing: consolidated network pharmacology and molecular docking investigations of the mechanisms underpinning Rhododendron arboreum flower's anti-NAFLD effects

Rhododendron arboreum: Sm., also known as Burans is traditionally used as an anti-inflammatory, anti-diabetic, hepatoprotective, adaptogenic, and anti-oxidative agent. It has been used since ancient times in Indian traditional medicine for various liver disorders. However, the exact mechanism behind its activity against NAFLD is not known. The aim of the present study is to investigate the molecular mechanism of Rhododendron arboreum flower (RAF) in the treatment of NAFLD using network pharmacology and molecular docking methods. Bioactives were also predicted for their drug-likeness score, probable side effects and ADMET profile. Protein-protein interaction (PPI) data was obtained using the STRING platform. For the visualisation of GO analysis, a bioinformatics server was employed. Through molecular docking, the binding affinity between potential targets and active compounds were assessed. A total of five active compounds of RAF and 30 target proteins were selected. The targets with higher degrees were identified through the PPI network. GO analysis indicated that the NAFLD treatment with RAF primarily entails a response to the fatty acid biosynthetic process, lipid metabolic process, regulation of cell death, regulation of stress response, and cellular response to a chemical stimulus. Molecular docking and molecular dynamic simulation exhibited that rutin has best binding affinity among active compounds and selected targets as indicated by the binding energy, RMSD, and RMSF data. The findings comprehensively elucidated toxicity data, potential targets of bioactives and molecular mechanisms of RAF against NAFLD, providing a promising novel strategy for future research on NAFLD treatment.

An insight for the inhibition of anxiolytic and anti-convulsant effects in zebrafish using the curcumins via exploring molecular docking and molecular dynamics simulations

In the contemporary landscape, anxiety and seizures stand as major areas of concern, prompting researchers to explore potential drugs against them. While numerous drugs have shown the potential to treat these two neurological conditions, certain adverse effects emphasize the need for development of safer alternatives. This study seeks to employ an in silico approach to evaluate natural compounds, particularly curcumins, as potential inhibitors of GABA-AT to mitigate anxiety and seizures. The proposed methodology includes generating a compound library, minimizing energy, conducting molecular docking using AutoDock, molecular dynamics simulations using Amber, and MM-GBSA calculations. Remarkably, CMPD50 and CMPD88 exhibited promising binding affinities of - 9.0 kcal/mol and - 9.1 kcal/mol with chains A and C of GABA-AT, respectively. Further, MM-GBSA calculations revealed binding free energies of - 10.88 kcal/mol and - 10.72 kcal/mol in CMPD50 and CMPD88, respectively. ADME analysis showed that these compounds contain drug-likeness properties and might be considered as potential drug candidates. The findings from this study will have practical applications in the field of drug discovery for the development of safer and effective drugs for treatment of anxiety and seizures. Overall, this study will lay the groundwork for providing valuable insights into the potential therapeutic effects of curcumins in alleviating anxiety and seizures, establishing a computational framework for future experimental validation.

A novel natural Syk inhibitor suppresses IgE-mediated mast cell activation and passive cutaneous anaphylaxis

Spleen tyrosine kinase (Syk) plays a crucial role as a target for allergy treatment due to its involvement in immunoreceptor signaling. The purpose of this study was to identify natural inhibitors of Syk and assess their effects on the IgE-mediated allergic response in mast cells and ICR mice. A list of eight compounds was selected based on pharmacophore and molecular docking, showing potential inhibitory effects through virtual screening. Among these compounds, sophoraflavanone G (SFG) was found to inhibit Syk activity in an enzymatic assay, with an IC50 value of 2.2 μM. To investigate the conformational dynamics of the SYK-SFG system, we performed molecular dynamics simulations. The stability of the binding between SFG and Syk was evaluated using root mean square deviation (RMSD) and root mean square fluctuation (RMSF). In RBL-2H3 cells, SFG demonstrated a dose-dependent suppression of IgE/BSA-induced mast cell degranulation, with no significant cytotoxicity observed at concentrations below 10.0 μM within 24 h. Furthermore, SFG reduced the production of TNF-α and IL-4 in RBL-2H3 cells. Mechanistic investigations revealed that SFG inhibited downstream signaling proteins, including phospholipase Cγ1 (PLCγ1), as well as mitogen-activated protein kinases (AKT, Erk1/2, p38, and JNK), in mast cells in a dose-dependent manner. Passive cutaneous anaphylaxis (PCA) experiments demonstrated that SFG could reduce ear swelling, mast cell degranulation, and the expression of COX-2 and IL-4. Overall, our findings identify naturally occurring SFG as a direct inhibitor of Syk that effectively suppresses mast cell degranulation both in vitro and in vivo.

In Silico Design of Potential Small-Molecule Antibiotic Adjuvants against Salmonella typhimurium Ortho Acetyl Sulphydrylase Synthase to Address Antimicrobial Resistance

The inhibition of O-acetyl sulphydrylase synthase isoforms has been reported to represent a promising approach for the development of antibiotic adjuvants. This occurs via the organism developing an unpaired oxidative stress response, causing a reduction in antibiotic resistance in vegetative and swarm cell populations. This consequently increases the effectiveness of conventional antibiotics at lower doses. This study aimed to predict potential inhibitors of Salmonella typhimurium ortho acetyl sulphydrylase synthase (StOASS), which has lower binding energy than the cocrystalized ligand pyridoxal 5 phosphate (PLP), using a computer-aided drug design approach including pharmacophore modeling, virtual screening, and in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation. The screening and molecular docking of 4254 compounds obtained from the PubChem database were carried out using AutoDock vina, while a post-screening analysis was carried out using Discovery Studio. The best three hits were compounds with the PubChem IDs 118614633, 135715279, and 155773276, possessing binding affinities of -9.1, -8.9, and -8.8 kcal/mol, respectively. The in silico ADMET prediction showed that the pharmacokinetic properties of the best hits were relatively good. The optimization of the best three hits via scaffold hopping gave rise to 187 compounds, and they were docked against StOASS; this revealed that lead compound 1 had the lowest binding energy (-9.3 kcal/mol) and performed better than its parent compound 155773276. Lead compound 1, with the best binding affinity, has a hydroxyl group in its structure and a change in the core heterocycle of its parent compound to benzimidazole, and pyrimidine introduces a synergistic effect and consequently increases the binding energy. The stability of the best hit and optimized compound at the StOASS active site was determined using RMSD, RMSF, radius of gyration, and SASA plots generated from a molecular dynamics simulation. The MD simulation results were also used to monitor how the introduction of new functional groups of optimized compounds contributes to the stability of ligands at the target active site. The improved binding affinity of these compounds compared to PLP and their toxicity profile, which is predicted to be mild, highlights them as good inhibitors of StOASS, and hence, possible antimicrobial adjuvants.

Molecular Dynamics Simulation of Kir6.2 Variants Reveals Potential Association with Diabetes Mellitus

Diabetes mellitus (DM) represents a problem for the healthcare system worldwide. DM has very serious complications such as blindness, kidney failure, and cardiovascular disease. In addition to the very bad socioeconomic impacts, it influences patients and their families and communities. The global costs of DM and its complications are huge and expected to rise by the year 2030. DM is caused by genetic and environmental risk factors. Genetic testing will aid in early diagnosis and identification of susceptible individuals or populations using ATP-sensitive potassium (KATP) channels present in different tissues such as the pancreas, myocardium, myocytes, and nervous tissues. The channels respond to different concentrations of blood sugar, stimulation by hormones, or ischemic conditions. In pancreatic cells, they regulate the secretion of insulin and glucagon. Mutations in the KCNJ11 gene that encodes the Kir6.2 protein (a major constituent of KATP channels) were reported to be associated with Type 2 DM, neonatal diabetes mellitus (NDM), and maturity-onset diabetes of the young (MODY). Kir6.2 harbors binding sites for ATP and phosphatidylinositol 4,5-diphosphate (PIP2). The ATP inhibits the KATP channel, while the (PIP2) activates it. A Kir6.2 mutation at tyrosine330 (Y330) was demonstrated to reduce ATP inhibition and predisposes to NDM. In this study, we examined the effect of mutations on the Kir6.2 structure using bioinformatics tools and molecular dynamic simulations (SIFT, PolyPhen, SNAP2, PANTHER, PhD&SNP, SNP&Go, I-Mutant, MuPro, MutPred, ConSurf, HOPE, and GROMACS). Our results indicated that M199R, R201H, R206H, and Y330H mutations influence Kir6.2 structure and function and therefore may cause DM. We conclude that MD simulations are useful techniques to predict the effects of mutations on protein structure. In addition, the M199R, R201H, R206H, and Y330H variant in the Kir6.2 protein may be associated with DM. These results require further verification in protein-protein interactions, Kir6.2 function, and case-control studies.

Theoretical Investigation of the Green-Synthesized Carbon-Based Nanomaterial Potential as Inhibitors of ACE2 for Blocking SARS-CoV-2 Binding

Since the emergence of SARS-CoV-2 in 2020, the world has faced a global pandemic, emphasizing the urgent need for effective treatments to combat COVID-19. This study explores the use of green-synthesized carbon-based nanomaterials as potential inhibitors of ACE2, a critical receptor for SARS-CoV-2 entry into host cells. Specifically, the study examines four carbon-based nanomaterials, namely, CD1, CD2, CD3, and CD4 in amino, graphitic, pyridinic, and pyrrolic forms, respectively, synthesized from curcumin, to investigate their binding affinity with ACE2. Molecular docking studies revealed that CD3 (pyridinic form) exhibited the highest binding affinity with ACE2, surpassing that of the control compound, curcumin. Notably, CD3 formed hydrophobic interactions and hydrogen bonds with key ACE2 residues, suggesting its potential to block the binding of SARS-CoV-2 to human cells. Moreover, molecular dynamics simulations demonstrated the stability of these ligand-ACE2 complexes, further supporting the promise of CD3 as an inhibitor. Quantum chemical analyses, including frontier molecular orbitals, natural bond orbital analysis, and the quantum theory of atoms in molecules, unveiled valuable insights into the reactivity and interaction strengths of these ligands. CD3 exhibited desirable chemical properties, signifying its suitability for therapeutic development. The study's findings suggest that green-synthesized carbon-based nanomaterials, particularly CD3, have the potential to serve as effective inhibitors of ACE2, offering a promising avenue for the development of treatments against COVID-19. Further experimental validation is warranted to advance these findings and establish new therapies for the ongoing global pandemic.

Modulating the antibacterial effect of the existing antibiotics along with repurposing drug metformin

Owing to the extensive prevalence of resistant bacteria to numerous antibiotic classes, antimicrobial resistance (AMR) poses a well-known hazard to world health. As an alternate approach in the field of antimicrobial drug discovery, repurposing the available medications which are also called antibiotic resistance breakers has been pursued for the treatment of infections with antimicrobial resistance pathogens. In this study, we used Haloperidol, Metformin and Hydroxychloroquine as repurposing drugs in in vitro (Antibacterial Antibiotic Sensitivity Test and Minimum Inhibitory Concentration-MIC) and in vivo (Shigellosis in Swiss albino mice) tests in combination with traditional antibiotics (Oxytetracycline, Erythromycin, Doxycycline, Gentamicin, Ampicillin, Chloramphenicol, and Penicillin) against a group of AMR resistance bacteria (Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Shigella boydii). After observing the results of the conducted in vitro experiments we studied the effects of the above non antibiotic drugs in combination with the said antibiotics. As an repurposing adjuvant antibiotic drug, Metformin exhibited noteworthy activity in almost all in vitro, in vivo and in silico tests (Zone of inhibition for 30 to 43 mm for E.coli in combination with Doxycycline; MIC value decreased 50 µM to 0.781 µM with Doxycycline on S. boydii).In rodents Doxycycline and Metformin showed prominent against Shigellosis in White blood cell count (6.47 ± 0.152 thousand/mm3) and Erythrocyte sedimentation rate (10.5 ± 1.73 mm/hr). Our findings indicated that Metformin and Doxycycline combination has a crucial impact on Shigellosis. The molecular docking study was performed targeting the Acriflavine resistance protein B (AcrB) (PDB ID: 4CDI) and MexA protein (PDB ID: 6IOK) protein with Metformin (met8) drug which showed the highest binding energy with - 6.4 kcal/mol and - 5.5 kcal/mol respectively. Further, molecular dynamics simulation revealed that the docked complexes were relatively stable during the 100 ns simulation period. This study suggest Metformin and other experimented drugs can be used as adjuvants boost up antibiosis but further study is needed to find out the safety and efficacy of this non-antibiotic drug as potent antibiotic adjuvant.

Identification of novel potential cathepsin-B inhibitors through pharmacophore-based virtual screening, molecular docking, and dynamics simulation studies for the treatment of Alzheimer's disease

Cathepsin B is a cysteine protease lysosomal enzyme involved in several physiological functions. Overexpression of the enzyme enhances its proteolytic activity and causes the breakdown of amyloid precursor protein (APP) into neurotoxic amyloid β (Aβ), a characteristic hallmark of Alzheimer's disease (AD). Therefore, inhibition of the enzyme is a crucial therapeutic aspect for treating the disease. Combined structure and ligand-based drug design strategies were employed in the current study to identify the novel potential cathepsin B inhibitors. Five different pharmacophore models were developed and used for the screening of the ZINC-15 database. The obtained hits were analyzed for the presence of duplicates, interfering PAINS moieties, and structural similarities based on Tanimoto's coefficient. The molecular docking study was performed to screen hits with better target binding affinity. The top seven hits were selected and were further evaluated based on their predicted ADME properties. The resulting best hits, ZINC827855702, ZINC123282431, and ZINC95386847, were finally subjected to molecular dynamics simulation studies to determine the stability of the protein-ligand complex during the run. ZINC123282431 was obtained as the virtual lead compound for cathepsin B inhibition and may be a promising novel anti-Alzheimer agent.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

In Silico Structural Modeling and Binding Site Analysis of Cerebroside Sulfotransferase (CST): A Therapeutic Target for Developing Substrate Reduction Therapy for Metachromatic Leukodystrophy

Cerebroside sulfotransferase (CST) is emerging as an important therapeutic target to develop substrate reduction therapy (SRT) for metachromatic leukodystrophy (MLD), a rare neurodegenerative lysosomal storage disorder. MLD develops with progressive impairment and destruction of the myelin sheath as a result of accumulation of sulfatide around the nerve cells in the absence of its recycling mechanism with deficiency of arylsulfatase A (ARSA). Sulfatide is the product of the catalytic action of cerebroside sulfotransferase (CST), which needs to be regulated under pathophysiological conditions by inhibitor development. To carry out in silico-based preliminary drug screening or for designing new drug candidates, a high-quality three-dimensional (3D) structure is needed in the absence of an experimentally derived three-dimensional crystal structure. In this study, a 3D model of the protein was developed using a primary sequence with the SWISS-MODEL server by applying the top four GMEQ score-based templates belonging to the sulfotransferase family as a reference. The 3D model of CST highlights the features of the protein responsible for its catalytic action. The CST model comprises five β-strands, which are flanked by ten α-helices from both sides as well as form the upside cover of the catalytic pocket of CST. CST has two catalytic regions: PAPS (-sulfo donor) binding and galactosylceramide (-sulfo acceptor) binding. The catalytic action of CST was proposed via molecular docking and molecular dynamic (MD) simulation with PAPS, galactosylceramide (GC), PAPS-galactosylceramide, and PAP. The stability of the model and its catalytic action were confirmed using molecular dynamic simulation-based trajectory analysis. CST response against the inhibition potential of the experimentally reported competitive inhibitor of CST was confirmed via molecular docking and molecular dynamics simulation, which suggested the suitability of the CST model for future drug discovery to strengthen substrate reduction therapy for MLD.

Engineering of methionine sulfoxide reductase A with simultaneously improved stability and activity for kinetic resolution of chiral sulfoxides

Methionine sulfoxide reductase A (MsrA) has emerged as promising biocatalysts in the enantioselective kinetic resolution of racemic (rac) sulfoxides. In this study, we engineered robust MsrA variants through directed evolution, demonstrating substantial improvements of thermostability. Mechanism analysis reveals that the enhanced thermostability results from the strengthening of intracellular interactions and increase in molecular compactness. Moreover, these variants demonstrated concurrent improvements in catalytic activities, and notably, these enhancements in stability and activity collectively contributed to a significant improvement in enzyme substrate tolerance. We achieved kinetic resolution on a series of rac-sulfoxides with high enantioselectivity under initial substrate concentrations reaching up to 93.0 g/L, representing a great improvement in the aspect of the substrate concentration for biocatalytic preparation of chiral sulfoxide. Hence, the simultaneously improved thermostability, activity and substrate tolerance of MsrA represent an excellent biocatalyst for the green synthesis of optically pure sulfoxides.

Biophysical and in-silico studies on the structure-function relationship of Brugia malayi protein disulfide isomerase

Human Lymphatic filariasis is caused by parasitic nematodes Wuchereria bancrofti, Brugia malayi, and Brugia timori. Protein disulfide isomerase (PDI), a redox-active enzyme, helps to form and isomerize the disulfide bonds, thereby acting as a chaperone. Such activity is essential for activating many essential enzymes and functional proteins. Brugia malayi protein disulfide isomerase (BmPDI) is crucial for parasite survival and an important drug target. Here, we used a combination of spectroscopic and computational analysis to study the structural and functional changes in the BmPDI during unfolding. Tryptophan fluorescence data revealed two well-separated transitions during the unfolding process, suggesting that the unfolding of the BmPDI is non-cooperative. The binding of the fluorescence probe 8-anilino-1-naphthalene sulfonic acid dye (ANS) validated the results obtained by the pH unfolding. The dynamics of molecular simulation performed at different pH conditions revealed the structural basis of BmPDI unfolding. Detailed analysis suggested that under different pH, both the global structure and the conformational dynamics of the active site residues were differentially altered. Our multiparametric study reveals the differential dynamics and collective motions of BmPDI unfolding, providing insights into its structure-function relationship.Communicated by Ramaswamy H. Sarma.

Elucidation of Molecular Interactions Between Drug-Polymer in Amorphous Solid Dispersion by a Computational Approach Using Molecular Dynamics Simulations

Introduction

Amorphous drug dispersion is frequently used to enhance the solubility and dissolution of poorly water-soluble drugs, thereby improving their oral bioavailability. The dispersion of these drugs into polymer matrix can inhibit their recrystallization. The inter-molecular interactions between drug and polymer plays a role in the improvement of the dissolution rate, solubility, and physical stability of drug.

Aim

This study aims to investigate the formation and interactions of ritonavir (RTV)/poloxamer (PLX) amorphous formulation using a computational approach via molecular dynamics (MD) simulations, which mimicked solvent evaporation and melt-quenching method.

Methods

TheRoot Mean Square Deviation (RMSD) value, Root Mean Square Fluctuation (RMSF), Radial Distribution Function (RDF), Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA), and hydrogen bond interactions were analyzed to determine interaction mechanisms between RTV and PLX in amorphous solid dispersion.

Results

The pi-alkyl bonds between RTV and PLX were formed after simulations of solvent evaporation, while the hydrogen bond interactions of RTV-PLX was observed during melt method simulations. These results indicate the successful formulation of amorphous solid dispersion (ASD) from RTV and PLX. The RMSD values obtained from the solvent evaporation, melt-cooling-A, melt-cooling-B, and melt-cooling-C methods were 3.33 Å, 1.97 Å, 1.30 Å, and 1.29 Å, respectively, while the average RMSF values were 2.65 Å, 1.04 Å, 1.05 Å, and 1.07 Å, respectively. This indicates that the suppression of translational motion of RTV from the melt method can be stronger than solvent evaporation caused by the intermolecular interactions of RTV-PLX.

Conclusion

MD simulations helped in understanding the formation and interaction mechanisms of ASD formulations that were difficult to detect by experimental approaches.

Evaluation of wound healing active principles in the transdermal patch formulated with crude bio wastes and plant extracts against GSK-3 beta - an in silico study

The wound-healing process is accelerated by inhibiting proteins that decelerate the wound-healing pathway. One of the active proteins involved in enhancing healing at the nuclear level and in gene expression is catenin. Inhibition of Glycogen Synthase Kinase 3β (GSK3 β) phosphorylates and degrades catenin via the downstream Wnt signalling pathway, thereby stabilizing catenin. A medicated wound dressing transdermal patch designed with fusion of bio wastes, viz. physiologically clotted fibrin, fish scale collagen, and the ethanolic extract of Mangifera indica (L.) and spider web, was analysed against GSK3β to enhance healing. In our earlier studies, the compounds present in the transdermal patch were identified using GC-MS analysis; 12 compounds exhibiting the wound healing mechanism were analyzed using PASS software and filtered out. From these 12 compounds, 6 compounds that possessed drug-likeness were screened by SwissADME and vNN-ADMET to dock against GSK3β in the present work. The PyRx results confirmed the binding of the six ligands to the active site of the target protein. Though the remaining filtered ligands also exhibited inhibitory activity, Molecular dynamics simulation studies were carried out with 100 ns on a complex of 10,12 Tricosadiyonic acid, Nopyl acetate and 2 Methyl 4 Heptanol as they showed binding affinity of -6.2Kcal/mol, -5.7Kcal/mol and -5.1Kcal/mol respectively. The stability of the complex was validated using MD simulation parameters RMSD, RMSF, Rg, and Number of Hydrogen bonds. These results implied that the transdermal patch would be efficient in accelerating the wound healing process through the inactivation of GSK3β.Communicated by Ramaswamy H. Sarma.

Bioactivity of dihydropyrimidinone derivatives as inhibitors of cyclooxygenase-2 (COX-2): an in silico approach

Cyclooxygenase-2 (COX-2) is an enzyme involved in inflammation. The overexpression of COX-2 causes chronic inflammation, which can be prevented by COX-2 inhibitors. Generally, COX-2 inhibitors possess a carboxyl group and an aromatic ring in their molecular structure. These moieties are involved in the interaction with the active site of COX-2, thus playing a pivotal role in the inhibitory activity. Regarding the requisite molecular structure of COX-2 inhibitors, derivatives of dihydropyrimidinone (DHPM) are ideal candidates to be explored as COX-2 inhibitors, due to the ease of synthesis and their versatility to be transformed chemically. In this study, we prepared a novel small library consisting of 288 designed DHPM derivatives by varying the constituent components. The selection criteria of potential candidates for the COX-2 inhibitor of the data bank involve in silico studies via molecular docking investigations, prediction of ADMET and druglikeness, as well as molecular dynamics (MD) simulations. Molecular docking served as the initial step of selection, based on the comparison of grid score, docking pose, and interactions with those of lumiracoxib (LUR) as the original ligand of COX-2. The next criteria of selection were scores obtained from the ADMET and druglikeness by comparing the designed candidates with COX-2 inhibitors that were already marketed. Compound RDUE2 and SDT29 were the most potential candidates, which were further analyzed using the MD simulation. The results of the MD simulation indicated that RDUE2 and SDT29 interacted stably with amino acid residues on the active site of COX-2. The estimation of binding free energy indicated that SDT29 exhibited an inhibitory activity comparable to that of LUR, whereas RDUE2 showed a lower inhibitory activity than that of SDT29 and LUR.

Molecular insights and optimization strategies for the competitive binding of engineered ACE2 proteins: a multiple replica molecular dynamics study

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to spread globally, and rapid viral evolution and the emergence of new variants pose challenges to pandemic control. During infection, the spike protein of SARS-CoV-2 interacts with the human ACE2 protein via its receptor binding domain (RBD), and it is known that engineered forms of ACE2 can compete with wild-type (WT) ACE2 for binding to inhibit infection. Here, we conducted multiple replica molecular dynamics (MRMD) simulations to study the mechanisms of the engineered ACE2 variants 3N39 and 3N94 and provide directions for optimization. Our findings reveal that engineered ACE2 is notably more efficacious in systems that show weaker binding to WT ACE2 (i.e., WT and BA.1 RBD), but also faces immune escape as the virus evolves. Moreover, by modifying residue types near the binding interface, engineered ACE2 alters the electrostatic potential distribution and reconfigures the hydrogen bonding network, which results in modified binding to the RBD. However, this structural rearrangement does not occur in all RBD variants. In addition, we identified potentially engineerable beneficial residues and potentially engineerable detrimental residues in both ACE2 and RBD. Functional conservation can thus enable the optimization of these residues and improve the binding competitiveness of engineered ACE2, which therefore provides additional immune escape prevention. Finally, we conclude that these findings have implications for understanding the mechanisms responsible for engineered ACE2 and can help us to develop engineered ACE2 proteins that show superior performance.

Ehretia Species Phytoconstituents as Potential Lead Compounds against Klebsiella pneumoniae Carbapenemase: A Computational Approach

The evolution of antibiotic-resistant carbapenemase has negatively impacted the management of critical healthcare-associated infections. K. pneumoniae carbapenemase-2- (KPC-2-) expressing bacteria have developed resistance to conventional therapeutic options, including those used as a last resort for life-threatening diseases. In this study, Ehretia species phytoconstituents were screened for their potential to inhibit KPC-2 protein using in silico approaches. Molecular docking was used to identify strong KPC-2 protein binding phytoconstituents retrieved from the literature. The best-docked conformation of the ligands was selected based on their glide energy and binding interactions. To determine their binding free energies, these hit compounds were subjected to molecular mechanics with generalized born and surface area (MM-GBSA) in the PRIME module. Pharmacological assessments of the ligands were performed to evaluate their drug-likeness. Molecular dynamic (MD) simulations were used to analyze the conformational stability of the selected druglike compounds within the active site of the KPC-2 protein. Overall, a total of 69 phytoconstituents were compiled from the literature. Fourteen of these compounds exhibited a stronger binding affinity for the protein target than the reference drugs. Four of these top hit compounds, DB09, DB12, DB28, and DB66, revealed the highest efficacy in terms of drug-likeness properties. The MD simulation established that among the druglike compounds, DB66 attained stable conformations after 150 ns simulation in the active site of the protein. We concluded that DB66 from Ehretia species could play a significant role in therapeutic efforts against KPC-2-expressing bacteria.

Computer-aided identification of dengue virus NS2B/NS3 protease inhibitors: an integrated molecular modelling approach for screening of phytochemicals

Globally, dengue (DENV) fever has appeared as the most widespread vector-borne disease, affecting more than 100 million individuals annually. No approved anti-DENV therapy or preventive vaccine is available yet. DENV NS3 protein is associated with protease activity and is essential for viral replication process within the host cell. NS2B is linked with NS3 protein as a cofactor. Hence, NS3/NS2B is a potential druggable target for developing inhibitors against dengue virus. In the present study, a dataset of Beta vulgaris L.-based natural compounds was developed. Virtual ligand screening of 30 phytochemicals was carried out to find novel inhibitors against the NS2B/NS3 protein. Spatial affinity, drug-likeness, and binding behaviors of selected phytochemicals were analyzed. Post-simulation analysis, including Principal Component Analysis (PCA), MMGBSA, and Co-relation analysis, was also performed to provide deep insight for elucidating protein-ligand complexes. This computer-aided screening scrutinized four potent phytochemicals, including betavulgaroside II, vitexin xyloside, epicatechin, and isovitexin2-O-xyloside inhibitors exhibiting optimal binding with viral NS3/NS2B protein. Our study brings novel scaffolds against DENV NS2B/NS3 of serotype-2 to act as lead molecules for further biological optimization. In future, this study will prompt the exploration and development of adjuvant anti-DENV therapy based on natural compounds.Communicated by Ramaswamy H. Sarma.

Exploring the Synergistic Mechanism of AP2A2 Transcription Factor Inhibition via Molecular Modeling and Simulations as a Novel Computational Approach for Combating Breast Cancer: In Silico Interpretations

Studies have shown that transcription factor AP2A2 (activator protein-2 alpha-2) is involved in the expression of DLEC1, a tumor suppressor gene, which, when mutated, will cause breast cancer and is thus an excellent target for breast cancer studies. Therefore, in the present research, a synergistic approach toward combating breast cancer is proposed by blocking AP2A2 factor, and allowing the cancer cells to be sensitive to anti-cancer drugs. The effect of AP2A2 on breast cancer was first understood via gene analysis from cBioPortal. AP2A2 was then modeled using RaptorX and its structure was validated from Ramachandran plots. Using all ligands from MolPort database, molecular docking was performed against AP2A2, from which the top three best docked ligands were studied for toxicity in humans using Protox-II. Once the ligands passed these tests, the best complexes were simulated at 200ns in Desmond Maestro, to comprehend their stabilities, followed by the computations of free energies of binding via Molecular mechanics- Generalized Born Solvent Accessibility method (MM-GBSA). The results showed that molecules MolPort-005-945-556 (sachharolipids), MolPort-001-741-124 (flavonoids), and MolPort-005-944-667 (lignan glycosides) with AP2A2 passed toxicity evaluation and belonged to toxicity classes 6, 5, and 5, respectively, with good docking energies. 200 ns simulations revealed stable complexes with slight conformational changes. Stability of ligands was confirmed via snapshots at every 20 ns of the trajectory. Radial distribution of these molecules against the protein revealed very slight deviation from binding pocket. Additionally, the free binding energies for these complexes were found to be - 54.93 ± 12.982 kcal/mol, - 44.39 ± 14.393 kcal/mol, and - 66.51 ± 13.522 kcal/mol, respectively. A preliminary computational validation of the inability of AP2A2 to bind to DLEC1 in the presence of ligands offers beneficial insights into the potential of these ligands. Therefore, this study sheds light on the potential natural molecules that could stably block AP2A2 with least deviation and act in synergy to aid anti-cancer drugs work on breast cancer cells.

A novel robust inhibitor of papain-like protease (PLpro) as a COVID-19 drug

Regarding the significance of SARS-CoV-2, scientists have shown considerable interest in developing effective drugs. Inhibitors for PLpro are the primary strategies for locating suitable COVID-19 drugs. Natural compounds comprise the majority of COVID-19 drugs. Due to limitations on the safety of clinical trials in cases of COVID, computational methods are typically utilized for inhibition studies. Whereas papain is highly similar to PLpro and is entirely safe, the current study aimed to examine several plant secondary metabolites to identify the most effective papain inhibitor and validate the results using molecular dynamics and docking. This simulation was conducted identically for PLpro and the optimal inhibitor. The results indicated that the experimental results are comparable to those obtained In-Silico, and the inhibition effects of Chlorogenic acid (CGA) on papain attained in the experiment were validated (IC50=0.54 mM). CGA as an inhibitor was located in the active site of PLpro and papain (total energy -2009410 and -456069 kJ/mol, respectively) at the desired location and distance. The study revealed that CGA and its derivatives are effective PLpro inhibitors against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

In silico study on antidiabetic and antioxidant activity of bioactive compounds in Ficus carica L

Hyperglycemia is one of the diagnostic issues in diabetes mellitus and is considered as a complex metabolic condition. It has been one of the most prevalent illnesses of the twenty-first century and still rising at an alarming rate across the globe and expected to impact 693 million individuals by 2045. Therefore, it is mandatory to develop more effective and safer treatments to manage diabetes. One of the ways to manage hyperglycemia is through inhibiting carbohydrate digestion and thereby lowering the glucose formation in the human body. The enzyme salivary amylase and pancreatic amylase is responsible for cleaving α-1,4-glucoside bond. Amylase inhibitors can lower blood glucose in diabetics by slowing digestion. Ficus carica is commonly known for its medicinal properties due to its various phytochemicals. In the present study, 10 phytochemicals present in F. carica compounds named, β-carotene, lutein, cyanidin-3-glucoside, gallic acid, luteolin, catechin, kaempferol, vanillic acid, peonidin-3-glucoside, and quercetin hydrate were taken to study their inhibition potential against pancreatic amylase and salivary amylase through molecular docking and molecular dynamics simulations. Further, density functional theory calculations are used to investigate the delocalization of electron density on the molecule as well as study ADME properties of the molecules take. A QSAR model has been developed using the binding energy obtained using molecular docking and thermodynamic parameters from DFT calculations.Communicated by Ramaswamy H. Sarma.

Novel computational and drug design strategies for inhibition of monkeypox virus and Babesia microti: molecular docking, molecular dynamic simulation and drug design approach by natural compounds

Background

The alarming increase in tick-borne pathogens such as human Babesia microti is an existential threat to global public health. It is a protozoan parasitic infection transmitted by numerous species of the genus Babesia. Second, monkeypox has recently emerged as a public health crisis, and the virus has spread around the world in the post-COVID-19 period with a very rapid transmission rate. These two novel pathogens are a new concern for human health globally and have become a significant obstacle to the development of modern medicine and the economy of the whole world. Currently, there are no approved drugs for the treatment of this disease. So, this research gap encourages us to find a potential inhibitor from a natural source.

Methods and materials

In this study, a series of natural plant-based biomolecules were subjected to in-depth computational investigation to find the most potent inhibitors targeting major pathogenic proteins responsible for the diseases caused by these two pathogens.

Results

Among them, most of the selected natural compounds are predicted to bind tightly to the targeted proteins that are crucial for the replication of these novel pathogens. Moreover, all the molecules have outstanding ADMET properties such as high aqueous solubility, a higher human gastrointestinal absorption rate, and a lack of any carcinogenic or hepatotoxic effects; most of them followed Lipinski's rule. Finally, the stability of the compounds was determined by molecular dynamics simulations (MDs) for 100 ns. During MDs, we observed that the mentioned compounds have exceptional stability against selected pathogens.

Conclusion

These advanced computational strategies reported that 11 lead compounds, including dieckol and amentoflavone, exhibited high potency, excellent drug-like properties, and no toxicity. These compounds demonstrated strong binding affinities to the target enzymes, especially dieckol, which displayed superior stability during molecular dynamics simulations. The MM/PBSA method confirmed the favorable binding energies of amentoflavone and dieckol. However, further in vitro and in vivo studies are necessary to validate their efficacy. Our research highlights the role of Dieckol and Amentoflavone as promising candidates for inhibiting both monkeypox and Babesia microti, demonstrating their multifaceted roles in the control of these pathogens.

MD-Ligand-Receptor: A High-Performance Computing Tool for Characterizing Ligand-Receptor Binding Interactions in Molecular Dynamics Trajectories

Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand-receptor binding interactions (lrbi) to be studied. In this study, we present MD-ligand-receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand-receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand-receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Deciphering the molecular details of interactions between anti-COVID drugs and functional human proteins: in silico approach

The molecular docking calculations have been employed to investigate the interactions a set of proteins with the repurposed anti-COVID drugs. The position of the therapeutic agents within the protein structure was dependent on a particular drug-protein system and varied from the binding cleft to the periphery of the polypeptide chain. Interactions involved in the drug-protein complexation includes predominantly hydrogen bonding and hydrophobic contacts. The obtained results may be of particular importance while developing the anti-COVID strategies as well as for deeper understanding of the drug pharmacodynamics and pharmacokinetics.

A computational study of structural analysis of Class I human glucose-6-phosphate dehydrogenase (G6PD) variants: Elaborating the correlation to chronic non-spherocytic hemolytic anemia (CNSHA)

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common human enzyme defect that affects more than 500 million people worldwide. Individuals affected with G6PD deficiency may occasionally suffer mild-to-severe chronic hemolytic anemia. Chronic non-spherocytic hemolytic anemia (CNSHA) is a potential result of the Class I G6PD variants. This comparative computational study attempted to correct the defect in variants structure by docking the AG1 molecule to selected Class I G6PD variants [G6PD^Nashville (Arg393His), G6PD^Alhambra (Val394Leu), and G6PD^Durham (Lys238Arg)] at the dimer interface and structural NADP⁺ binding site. It was followed by an analysis of the enzyme conformations before and after binding to the AG1 molecule using the molecular dynamics simulation (MDS) approach, while the severity of CNSHA was determined via root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bonds, salt bridges, radius of gyration (Rg), solvent accessible surface area analysis (SASA), and principal component analysis (PCA). The results revealed that G6PD^Nashville (Arg393His) and G6PD^Durham (Lys238Arg) had lost the direct contact with structural NADP⁺ and salt bridges at Glu419 - Arg427 and Glu206 - Lys407 were disrupted in all selected variants. Furthermore, the AG1 molecule re-stabilized the enzyme structure by restoring the missing interactions. Bioinformatics approaches were also used to conduct a detailed structural analysis of the G6PD enzyme at a molecular level to understand the implications of these variants toward enzyme function. Our findings suggest that despite the lack of treatment for G6PDD to date, AG1 remains a novel molecule that promotes activation in a variety of G6PD variants.

Ensemble-based, high-throughput virtual screening of potential inhibitor targeting putative farnesol dehydrogenase of Metisa plana (Lepidoptera: Psychidae)

Metisa plana (Lepidoptera: Psychidae) bagworm is a leaf-eater caterpillar ubiquitously found as a damaging pest in oil palm plantations, specifically in Malaysia. Various strategies have been implemented, including the usage of chemical insecticides. However, the main challenges include the development of insecticide resistance and its detrimental effects on the environment and non-target organisms. Therefore, a biorational insecticide is introduced by targeting the juvenile hormone (JH) biosynthetic pathway, which is mainly present in the insect and vital for the insect's growth, diapause, metamorphosis, and adult reproduction. This study aimed to investigate the potential inhibitor for the rate-limiting enzyme involved in the JH pathway known as farnesol dehydrogenase. A 255 amino acids sequence encoded for the putative M. plana farnesol dehydrogenase (MpFolDH) open reading frame had been identified and isolated. The three-dimensional structure of MpFolDH was predicted to have seven β- sheets with α-helices at both sides, showing typical characteristics for classical short-chain dehydrogenase and associated with oxidoreductase activity. Then, the ensemble-based virtual screening was conducted based on the ZINC20 database, in which 43 768 compounds that fulfilled pesticide-likeness criteria were screened by site-specific molecular docking. After a short molecular dynamics simulation (5 ns) was conducted towards 102 compounds, only the top 10 compounds based on their most favourable binding energy were selected for a more extended simulation (100 ns). Based on the protein-ligand stability, protein compactness, residues rigidity, binding interaction, binding energy throughout the 100 ns simulation, and physicochemical analysis, ZINC000408743205 was selected as a potential inhibitor for this enzyme. Amino acids decomposition analysis indicates Ile18, Ala95, Val198 and Val202 were the critical contributor residues for MpFolDH-inhibitors(s) complex.

A mathematical system of COVID-19 disease model: Existence, uniqueness, numerical and sensitivity analysis

A compartmental mathematical model of spreading COVID-19 disease in Wuhan, China is applied to investigate the pandemic behaviour in Iran. This model is a system of seven ordinary differential equations including individual behavioural reactions, governmental actions, holiday extensions, travel restrictions, hospitalizations, and quarantine. We fit the Chinese model to the Covid-19 outbreak in Iran and estimate the values of parameters by trial-error approach. We use the Adams-Bashforth predictor-corrector method based on Lagrange polynomials to solve the system of ordinary differential equations. To prove the existence and uniqueness of solutions of the model we use Banach fixed point theorem and Picard iterative method. Also, we evaluate the equilibrium points and the stability of the system. With estimating the basic reproduction number R 0 , we assess the trend of new infected cases in Iran. In addition, the sensitivity analysis of the model is assessed by allocating different parameters to the system. Numerical simulations are depicted by adopting initial conditions and various values of some parameters of the system.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

SARS-CoV-2 main protease inhibitors: What is moving in the field of peptides and peptidomimetics?

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still affecting people worldwide. Despite the good degree of immunological protection achieved through vaccination, there are still severe cases that require effective antivirals. In this sense, two specific pharmaceutical preparations have been marketed already, the RdRp polymerase inhibitor molnupiravir and the main viral protease inhibitor nirmatrelvir (commercialized as Paxlovid, a combination with ritonavir). Nirmatrelvir is a peptidomimetic acting as orally available, covalent, and reversible inhibitor of SARS-CoV-2 main viral protease. The success of this compound has revitalized the search for new peptide and peptidomimetic protease inhibitors. This highlight collects some selected examples among those recently published in the field of SARS-CoV-2.

MD Simulation Studies for Selective Phytochemicals as Potential Inhibitors against Major Biological Targets of Diabetic Nephropathy

Diabetes is emerging as an epidemic and is becoming a public health concern worldwide. Diabetic nephropathy is one of the serious complications of diabetes, and about 40% of individuals with diabetes develop diabetic nephropathy. The consistent feature of diabetes and its associated nephropathy is hyperglycemia, and in some cases, hyperamylinemia. Currently, the treatment includes the use of medication for blood pressure control, sugar control, and cholesterol control, and in the later stage requires dialysis and kidney transplantation, making the management of this complication very difficult. Bioactive compounds, herbal medicines, and extracts are extensively used in the treatment and prevention of several diseases, and some are reported to be efficacious in diabetes too. Therefore, in this study, we tried to identify the therapeutic potential of phytochemicals used in in silico docking and molecular dynamic simulation studies using a library of 5284 phytochemicals against the two potential targets of type 2 diabetes-associated nephropathy. We identified two phytochemicals (i.e., gentisic acid and michelalbine) that target human amylin peptide and dipeptidyl peptidase-4, respectively, with good binding affinity. These phytochemicals can be further evaluated using in vitro and in vivo studies for their anti-hyperglycemia and anti-hyperamylinemia effects.

The Inhibitory Potential of Ferulic Acid Derivatives against the SARS-CoV-2 Main Protease: Molecular Docking, Molecular Dynamics, and ADMET Evaluation

The main protease (Mpro) of SARS-CoV-2 is an appealing target for the development of antiviral compounds, due to its critical role in the viral life cycle and its high conservation among different coronaviruses and the continuously emerging mutants of SARS-CoV-2. Ferulic acid (FA) is a phytochemical with several health benefits that is abundant in plant biomass and has been used as a basis for the enzymatic or chemical synthesis of derivatives with improved properties, including antiviral activity against a range of viruses. This study tested 54 reported FA derivatives for their inhibitory potential against Mpro by in silico simulations. Molecular docking was performed using Autodock Vina, resulting in comparable or better binding affinities for 14 compounds compared to the known inhibitors N3 and GC376. ADMET analysis showed limited bioavailability but significantly improved the solubility for the enzymatically synthesized hits while better bioavailability and druglikeness properties but higher toxicity were observed for the chemically synthesized ones. MD simulations confirmed the stability of the complexes of the most promising compounds with Mpro, highlighting FA rutinoside and compound e27 as the best candidates from each derivative category.