Molecular docking and simulation studies of Chloroquine, Rimantadine and CAP-1 as potential repurposed antivirals for decapod iridescent virus 1 (DIV1)

Introduction: Milk kefir, a fermenting milk made with kefir grains, has shown potential in promoting apoptosis, regulating the cell cycle, and reducing tumor growth in breast cancer cells. This study aimed to investigate the stability and potential of milk kefir metabolites as inhibitors of breast cancer growth by interacting with the estrogen receptor alpha (ER-α), a key protein involved in breast cancer cell proliferation. We used computational methods, specifically molecular docking simulations with AutoDock and molecular dynamics (MD) simulations with Gromacs, to analyze how these metabolites bind to ER-α. Methods: A combination of molecular docking and MD simulations was used to explore how metabolites derived from milk kefir interact with ER-α, a crucial target in breast cancer therapy. The methodology included multiple stages: preparation of target proteins, preparation and screening of the metabolites, geometry optimization, molecular docking, and MD simulations. Results: The molecular docking simulations of 43 metabolites revealed three promising candidates: 2-Methyl (S35), benzeneethanol (S42), and 2,6-dimethyl-4-heptanone (S54), with binding affinities (ΔG) of -5.08, -5.06, and -4.90 kcal/mol, respectively. MD simulations further showed that the selected metabolites stabilized the ER-α-metabolite complex, with the 2,6-dimethyl-4-heptanone (S54) metabolite demonstrating the most negative total MM-GBSA energy value (ΔG = -22.98 kcal/mol), indicating a strong and stable binding interaction. Conclusion: 2,6-Dimethyl-4-heptanone, a metabolite from milk kefir, showed promising potential as a candidate for further development as a breast cancer treatment, offering a novel alternative to conventional therapies.

Human T-cell leukemia virus type I (HTLV-1) belongs to the delta retrovirus family and the etiological agent of adult T-cell leukemia (ATL(. While the current HTLV-1 therapy, relies on using Zidovudine plus IFN-γ, there is no FDA approved drugs against it. In silico drug repurposing is a fast and accurate way for screening US-FDA approved drugs to find a therapeutic option for the HTLV-1 infection. So that, this research aims to analyze a dataset of approved antiviral drugs as a potential prospect for an anti-viral drug against HTLV-1 infection. Molecular docking simulation was performed to identify interactions of the antiviral drugs with the key residues in the HTLV-1 protease binding site. Then, molecular dynamics simulation was also performed for the potential protein-ligand complexes to confirm the stable behavior of the ligands inside the binding pocket. The best docking scores with the target was found to be Simeprevir, Atazanavir, and Saquinavir compounds which indicate that these drugs can firmly bind to the HTLV-1 protease. The MD simulation confirmed the stability of Simeprevir-protease, Atazanavir-Protease, and Saquinavir-Protease interactions. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against HTLV-1 infection. Communicated by Ramaswamy H. Sarma

This study aims to systematically investigate the multi-target mechanisms of cobalamin in the treatment of ischemic stroke using network pharmacology and molecular docking approaches. We screened databases to identify the targets of cobalamin and performed intersected with with ischemic stroke-related targets to construct a “drug-target-disease” interaction network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to identify key biological processes and signaling pathways. Additionally, molecular docking simulations were performed to assess the binding affinity between cobalamin and hub proteins. Molecular dynamics (MD) simulations were used to assess the stability of the protein–ligand complexes over a 500 ns simulation period. Additionally, ADME (Absorption, Distribution, Metabolism, Excretion) and blood–brain barrier (BBB) permeability predictions were made using ADMETlab 3.0 and admetSAR 3.0. A total of 95 therapeutic targets of cobalamin for ischemic stroke were identified. Network analysis and molecular docking highlighted eight core targets—ALB, TIMP1, PLG, FN1, AGT, SERPINE1, APOE, and SPP1—with high binding affinities to cobalamin. GO analysis suggested that cobalamin regulates inflammatory responses, post-translational modifications, complement binding, and lipoprotein particle binding. KEGG analysis identified complement and coagulation cascades, the PI3K/AKT pathway, and inflammation-related signaling as central to its therapeutic effects. Molecular docking showed strong binding to ALB and TIMP1, which was further confirmed by MD simulations, with minimal conformational changes. The PLG-cobalamin complex exhibited more fluctuations. ADME analysis revealed low passive permeability, particularly across the blood–brain barrier, but moderate distribution and high plasma protein binding. This study provides evidence that cobalamin may offer neuroprotective effects in ischemic stroke by interacting with key target proteins involved in coagulation, inflammation, and lipid metabolism. The findings highlight the potential of cobalamin as a therapeutic agent, although its limited ability to cross the blood–brain barrier may restrict its oral use. Further experimental validation and development of suitable delivery methods are needed to fully realize cobalamin’s potential in stroke therapy.

The advent of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) pandemic, has impacted physical and mental health worldwide. The lack of effective antiviral drugs necessitates a robust therapeutic approach to develop anti-SARS-CoV-2 drugs. Various investigations have recognized ACE2 as the primary receptor of SARS-CoV-2, and this amalgamation of ACE2 with the spike protein of the coronavirus is paramount for viral entry into the host cells and inducing infection. Consequently, restricting the virus's accessibility to ACE2 offers an alternative therapeutic approach to averting this illness. The study aimed to identify potent inhibitors with enhanced affinity for the ACE2 protein and validate their stability and efficacy against established inhibitors via molecular docking, machine learning, and MD simulations. 202 ACE2 inhibitors (PDB ID and 6LZG), comprising repurposed antiviral compounds and specific ACE2 inhibitors, were selected for molecular docking. The two most effective compounds obtained from docking were further analyzed using machine learning to identify potential compounds with enhanced ACE2-binding affinity. To refine the dataset, molecular decoys were generated through the Database of Useful Decoys: Enhanced (DUD-E) server, and Singular Value Decomposition (SVD) was applied for data preprocessing. The Tree-based Pipeline Optimization Tool (TPOT) was then utilized to optimize the machine learning pipeline. The most promising ML-predicted compounds were re-evaluated through docking and subjected to Molecular Dynamics (MD) simulations to evaluate their structural stability and interactions with ACE2. Finally, these compounds were evaluated against the top two pre-established inhibitors using various computational tools. The two best pre-established inhibitors were identified as Birinapant and Elbasvir, while the best machine-learning-predicted compounds were PubChem ID: 23658468 and PubChem ID: 117637105. Pharmacophore studies were conducted on the most effective machine-learning-predicted compounds, followed by a comparative ADME/T analysis between the best ML-screened and pre-established inhibitors. The results indicated that the top ML compound (PubChem ID: 23658468) demonstrated favorable BBB permeability and a high HIA index, highlighting its potential for therapeutic applications. The ML-screened ligand demonstrated structural stability with an RMSD (0.24 nm) and greater global stability (Rg: 2.08 nm) than Birinapant. Hydrogen bonding interactions further validated their strong binding affinity. MM/PBSA analysis confirmed the ML-screened compound's stronger binding affinity, with a binding free energy of - 132.90 kcal/mol, indicating enhanced stability in complex formation. The results emphasize the efficacy of integrating molecular docking, machine learning, and molecular dynamics simulations in facilitating the rapid identification of novel inhibitors. PubChem ID: 23658468 demonstrates robust binding affinity to ACE2 and favorable pharmacokinetic properties, establishing it as a promising candidate for further investigation.

In this COVID-19 pandemic situation, an appropriate drug is urgent to fight against this infectious disease to save lives and prevent mortality. Repurposed drugs and vaccines are the immediate solutions for this medical emergency until discover a new drug to treat this disease. As of now, no specific drug is available to cure this disease completely. Several drug targets were identified in SARS-CoV-2, in which RdRp protein is one of the potential targets to inhibit this virus infection. In-Silico studies plays a vital role to understand the binding nature of the drugs at the atomic level against the disease targets. The present study explores the binding mechanism of reported 53 nucleoside and non-nucleoside RdRp inhibitors and Ivermectin which are in clinical trials. These molecules were screened by molecular docking simulation; in which, the molecules are showing high binding affinity and forming interactions with the key amino acids of active site of RdRp protein are chosen for molecular dynamics simulation (MD) and binding free energy analysis. The results of molecular docking and MD simulation studies reveal that IDX184 is a stable molecule and forms strong interactions with the key amino acids and shows high binding affinity towards RdRp. Hence, IDX184 may also be considered as a potential inhibitor of RdRp after clinical study. Communicated by Ramaswamy H. Sarma

Kyasanur forest disease (KFD) is a tick-borne, neglected tropical disease, caused by KFD virus (KFDV) which belongs to Flavivirus (Flaviviridae family). This emerging viral disease is a major threat to humans. Currently, vaccination is the only controlling method against the KFDV, and its effectiveness is very low. An effective control strategy is required to combat this emerging tropical disease using the existing resources. In this regard, in silico drug repurposing method offers an effective strategy to find suitable antiviral drugs against KFDV proteins. Drug repurposing is an effective strategy to identify new use for approved or investigational drugs that are outside the scope of their initial usage and the repurposed drugs have lower risk and higher safety compared to de novo developed drugs, because their toxicity and safety issues are profoundly investigated during the preclinical trials in human/other models. In the present work, we evaluated the effectiveness of the FDA approved and natural compounds against KFDV proteins using in silico molecular docking and molecular simulations. At present, no experimentally solved 3D structures for the KFD viral proteins are available in Protein Data Bank and hence their homology model was developed and used for the analysis. The present analysis successfully developed the reliable homology model of NS3 of KFDV, in terms of geometry and energy contour. Further, in silico molecular docking and molecular dynamics simulations successfully presented four FDA approved drugs and one natural compound against the NS3 homology model of KFDV. Communicated by Ramaswamy H. Sarma

Simple SummaryThe ongoing coronavirus disease-2019 (COVID-19) pandemic shows unprecedented challenges for the worldwide healthcare system. Despite the large clinical data concerning several therapeutic interventions and drug repurposing, results are still either preliminary or lacking adequate-clinical efficiency. Herein, different pharmacoinformatics approaches have been adopted such as molecular docking, ADME properties prediction and all-atom MD simulation to investigate several marine-derived scalarane derivatives as lead candidates against two of the major COVID-19 targets; main protease and Nsp15 endoribonuclease. The presented study clearly illustrates the fitness of the proposed scalarane molecules as promising clinical candidates for further development and future in-vitro/in-vivo studies against SARS-CoV-2.Presently, the world is under the toll of pandemic coronavirus disease-2019 (COVID-19) outbreak caused by SARS-CoV-2. Lack of effective and safe therapeutics has stressed the scientific community for developing novel therapeutics capable of alleviating and stopping this pandemic. Within the presented study, molecular docking, ADME properties and all-atom molecular dynamic (MD) simulation, along with two standard antiviral agents (lopinavir and benzopurpurin-4B), were applied to investigate 15 scalaranes sesterterpenes natural compounds, purified from the Red Sea marine sponge Hyrtios erectus, as potential COVID-19 dual-target inhibitors. Following multi-step docking within COVID-19 main protease and Nsp15 endoribonuclease cavities, nine promising drug-like compounds exhibited higher docking scores as well as better interactions with the target’s crucial residues than those of reference ligands. Compounds 2, 6, 11, and 15, were predicted to simultaneously subdue the activity of the two COVID-19 targets. Dynamics behavior of the best-docked molecules, compounds 15 and 6, within COVID-19 target pockets showed substantial stability of ligand-protein complexes as presented via several MD simulation parameters. Furthermore, calculated free-binding energies from MD simulation illustrated significant ligand’s binding affinity towards respective target pockets. All provided findings supported the utility of scalarane-based sesterterpenes, particularly compounds 15 and 6, as promising lead candidates guiding the development of effective therapeutics against SARS-CoV-2.

Objective: Drug resistance is an imperative issue in the treatment of patients with lung cancer. In this work, investigation of the drug resistance mechanism of G2032R mutation in ROS1 is carried out using computational simulation techniques.Methods: Molecular docking and molecular dynamics (MD) simulation approach have been utilized to uncover the mechanism behind crizotinib resistance in ROS1 at a molecular level. Normal mode analysis was carried out using ElNemo server which examines the movements and conformational changes in the protein structure. ArgusLab, PEARLS, and Autodock were employed for the docking analysis, whereas GROMACS package 4.5.3 was used for MD simulation approach.Results: The results from our analysis indicates that wild-type ROS1 (Protein Data Bank Code 3ZBF) could be more crucial for the crizotinib binding as it indicates largest binding affinity, minimum number of H-bonds, and higher flexibility than mutant-type ROS1. Moreover, the theoretical basis for the cause of drug insensitivity is the differences in the electrostatic properties of binding site residues between the wild and mutant ROS1 structures. Our analysis theoretically suggests that E-2027 is a key residue responsible for the ROS1 drug selectivity.Conclusion: Molecular docking and MD simulation results provide an explanation of the resistance caused by G2032R and may give a key clue for the drug design to encounter drug resistance.Keywords: ROS1, Crizotinib resistance, Molecular docking, Normal mode analysis, Molecular dynamic simulation.

Tilapia Lake Virus (TiLV) is a disease that affects tilapia fish, causing a high rate of sudden death at any stage in their life cycle. Unfortunately, there are currently no effective antiviral drugs or vaccines to prevent or control the progression of this disease. Researchers have discovered that the CRM1 protein plays a critical function in the development and spreading of animal viruses. By inhibiting CRM1, the virus's spread in commercial fish farms can be suppressed. With this in mind, this study intended to identify potential antiviral drugs from two different tropical mangrove plants from tropical regions: Heritiera fomes and Ceriops candolleana. To identify promising compounds that target the CRM1 protein, a computer-aided drug discovery approach is employed containing molecular docking, ADME (absorption, distribution, metabolism and excretion) analysis, toxicity assessment as well as molecular dynamics (MD) simulation. To estimate binding affinities of all phytochemicals, molecular docking is used and the top three candidate compounds with the highest docking scores were selected, which are CID107876 (-8.3 Kcal/mol), CID12795736 (-8.2 Kcal/mol), and CID12303662 (-7.9 Kcal/mol). We also evaluated the ADME and toxicity properties of these compounds. Finally, MD simulation was conducted to analyze the stability of the protein-ligand complex structures and confirm the suitability of these compounds. The computational study demonstrated that the phytochemicals found in H. fomes and C. candolleana could potentially serve as important inhibitors of TiLV, offering practical utility. However, further in vivo investigations are necessary to investigate and potentially confirm the effectiveness of these compounds as antiviral drugs against the virus TiLV.

PurposeNowadays, cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have been approved for treating metastatic breast cancer and have achieved inspiring curative effects. But some discoveries have indicated that CDK 4/6 are not the requisite factors in some cell types because CDK2 partly compensates for the inhibition of CDK4/6. Thus, it is urgent to design CDK2/4/6 inhibitors for significantly enhancing their potency. This study aims to explore the mechanism of the binding of CDK2/4/6 kinases and their inhibitors to design novel CDK2/4/6 inhibitors for significantly enhancing their potency in different kinds of cancers.Materials and methodsA series of 72 disparately functionalized 4-substituted N-phenylpyrimidin-2-amine derivatives exhibiting potent inhibitor activities against CDK2, CDK4 and CDK6 were collected to apply to this research. The total set of these derivatives was divided into a training set (54 compounds) and a test set (18 compounds). The derivatives were constructed through the sketch molecule module in SYBYL 6.9 software. A Powell gradient algorithm and Tripos force field were used to calculate the minimal structural energy and the minimized structure was used as the initial conformation for molecular docking. By the means of 3D-QSAR models, partial least squares (PLS) analysis, molecular dynamics (MD) simulations and binding free energy calculations, we can find the relationship between structure and biological activity.ResultsIn this study, we used molecular docking, 3D-QSAR and molecular dynamics simulation methods to comprehensively analyze the interaction and structure–activity relationships of 72 new CDK2/4/6 inhibitors. We used detailed statistical data to reasonably verify the constructed 3D-QSAR models for three receptors (q2 of CDK2 = 0.714, R2pred = 0.764, q2 = 0.815; R2pred of CDK4 = 0.681, q2 = 0.757; R2pred of CDK6 = 0.674). MD simulations and decomposition energy analysis validated the reasonability of the docking results and identified polar interactions as crucial factors that influence the different bioactivities of the studied inhibitors of CDK2/4/6 receptors, especially the electrostatic interactions of Lys33/35/43 and Asp145/158/163. The nonpolar interaction with Ile10/12/19 was also critical for the differing potencies of the CDK2/4/6 inhibitors. We concluded that the following probably enhanced the bioactivity against CDK2/4/6 kinases: (1) electronegative groups at the N1-position and electropositive and moderate-sized groups at ring E; (2) electrogroups featured at R2; (3) carbon atoms at the X-position or ring C replaced by a benzene ring; and (4) an electrogroup as R4.ConclusionPrevious studies, to our knowledge, only utilized a single approach of 3D-QSAR and did not integrate this method with other sophisticated techniques such as molecular dynamics simulations to discover new potential inhibitors of CDK2, CDK4, or CDK6. So we applied the intergenerational technology, such as 3D-QSAR technology, molecular docking simulation techniques, molecular dynamics simulations and MMPBSA19/MMGBSA20-binding free energy calculations to statistically explore the correlations between the structure with biological activities. The constructed 3D-QSAR models of the three receptors were reasonable and confirmed by the excellent statistical data. We hope the results obtained from this work will provide some useful references for the development of novel CDK2/4/6 inhibitors.

Evaluation of inhibition effect and interaction mechanism of antiviral drugs on main protease of novel coronavirus: Molecular docking and molecular dynamics studies

Protein-ligand interaction is an imperative subject in structure-based drug design and protein function prediction process. Molecular docking is a computational method which predicts the binding of a ligand molecule to the particular receptor. It predicts the binding pose, strength and binding affinity of the molecules using various scoring functions. Molecular docking and molecular dynamics simulations are widely used in combination to predict the binding modes, binding affinities and stability of different protein-ligand systems. With advancements in algorithms and computational power, molecular dynamics simulation is now a fundamental tool to investigative bio-molecular assemblies at atomic level. These methods in association with experimental support have been of great value in modern drug discovery and development. Nowadays, it has become an increasingly significant method in drug discovery process. In this review, we focus on protein-ligand interactions using molecular docking, virtual screening and molecular dynamics simulations. Here, we cover an overview of the available methods for molecular docking and molecular dynamics simulations, and their advancement and applications in the area of modern drug discovery. The available docking software and their advancement including application examples of different approaches for drug discovery are also discussed. We have also introduced the physicochemical foundations of molecular docking and simulations, mainly from the perception of bio-molecular interactions.

Exploring the mechanism and inhibitory effect of robinin on xanthine oxidase using multi-spectroscopy and molecular dynamics simulation methods

Heat shock protein 90(Hsp90), as a molecular chaperone, play a crucial role in folding and proper function of many proteins. Hsp90 inhibitors containing isoxazole scaffold are currently being used in the treatment of cancer as tumor suppressers. Here in the present studies, new compounds based on isoxazole scaffold were predicted using a combination of molecular modeling techniques including three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamic (MD) simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were also done. The steric and electrostatic contour map of CoMFA and CoMSIA were created. Hydrophobic, hydrogen bond donor and acceptor of CoMSIA model also were generated, and new compounds were predicted by CoMFA and CoMSIA contour maps. To investigate the binding modes of the predicted compounds in the active site of Hsp90, a molecular docking simulation was carried out. MD simulations were also conducted to evaluate the obtained results on the best predicted compound and the best reported Hsp90 inhibitors in the 3D-QSAR model. Findings indicate that the predicted ligands were stable in the active site of Hsp90.

Unraveling the molecular interaction of hydroxytyrosol with human serum albumin via multi-spectroscopy, thermodynamic analysis, molecular docking and molecular dynamics simulation.