Identification of novel brain penetrant GSK-3ß inhibitors toward Alzheimer's disease therapy by virtual screening, molecular docking, dynamic simulation, and MMPBSA analysis.

One of the significant therapeutic targets for Alzheimer's disease (AD) is Glycogen Synthase Kinase-3ß (GSK-3ß). Inhibition of GSK-3ß can prevent hyperphosphorylation of tau, and thus prevent formation and accumulation of neurofibrillary tangles and neuropil threads that block intracellular transport, trigger unfolded protein response, and increase oxidative stress, cumulatively leading to neurodegeneration. In this study, we have performed structure-based virtual screening of two small-molecule libraries from ChemDiv CNS databases using AutoDock Vina to identify hit molecules based on their binding affinities compared to that of an established GSK-3ß inhibitor, indirubin-3'-monoxime (IMO). Pharmacoinformatic screening on SwissADME and pkCSM servers enabled identification of lead molecules with favorable pharmacoinformatic properties for drug likeliness, including blood brain barrier (BBB) permeability. Further, molecular dynamic simulations identified six candidate lead molecules that show stable complex formation with GSK-3ß in dynamic state under physiological conditions. Principal component analysis of the dynamic state was used to plot Free Energy Landscapes (FELs) of GSK-3ß-ligand complexes. STRIDE secondary structure analysis of the lowest energy conformations identified from FEL plots, and binding free energy calculations by Molecular Mechanics Poisson-Boltzmann Surface Area ((ΔGbind)MM-PBSA) of the simulation trajectories led to the identification of two ligands as potential lead molecules having favorable free energy landscape profiles as well as significantly lower (ΔGbind)MM-PBSA in dynamic state compared to that of reference inhibitor IMO. Hence, this study identifies two novel brain penetrant GSK-3ß inhibitors that are likely to have therapeutic potential against Alzheimer's disease.

Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis.

Bacterial vaginosis (BV), primarily attributed to Gardnerella vaginalis, poses significant challenges due to antibiotic resistance and suboptimal treatment outcomes. This study presents an integrated approach to identify potential drug targets and screen compounds against this bacterium by leveraging a computational methodology. Subtractive proteomics of the reference strain ASM286196v1/UMB0386 (assembly accession: GCA_002861965.1) facilitated the prioritization of proteins with essential bacterial functions and pathways as potential drug targets. We selected 3-deoxy-7-phosphoheptulonate synthase (aroG gene product; also known as DAHP synthase) for downstream analysis. Molecular docking was employed in PyRx (AutoDock Vina) to predict binding affinities between aroG inhibitors from the ZINC database and 3-deoxy-7-phosphoheptulonate synthase. Molecular dynamics simulations of 100 ns, using GROMACS, validated the stability of drug-target interactions. Additionally, ADMET profiling aided in the selection of compounds with favorable pharmacokinetic properties and safety profile for human hosts. PBPK profiling showed that ZINC98088375 had the highest bioavailability and efficient systemic circulation. Conversely, ZINC5113880 demonstrated the lowest absorption rate (39.661%). Moreover, cirrhosis, steatosis, and renal impairment appeared to influence blood concentration of the drug, impacting bioavailability. The integrative -omics approach utilized in this study underscores the potential of computer-aided drug design and offers a rational strategy for targeted inhibitor discovery against G. vaginalis. The strategy is an attempt to address the limitations of current BV treatments, including antibiotic resistance, and pave way for the development of safer and more effective therapeutics.

Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration.

The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (-5.8Kcal/mol) to zein compared to retinol (-4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products.

Comprehensive structural and functional analysis of hVEGFR1: Insights into phosphorylation, molecular interactions, and potential inhibitors through docking and dynamics simulations.

Vascular Endothelial Growth Factor Receptor 1 (VEGFR1), is an enzyme with tyrosine kinase activity that plays a pivotal role in angiogenesis, the process of new blood vessel formation. This receptor is of significant clinical importance as it is implicated in various cancers, particularly non-small cell lung cancer (NSCLC), where its dysregulation leads to uncontrolled cell growth through ligand-induced phosphorylation. While commercially available drugs target VEGFR1, their prolonged use often leads to drug resistance and the emergence of mutations in cancer patients. To address these challenges, researchers have identified the human tyrosine kinase (hTK) domain of VEGFR1 as a potential therapeutic marker for lung malignancies. The 3D crystal structure of the hTK domain, obtained from Protein Data Bank (PDB ID: 3HNG), has provided vital structural insights of hVEGFR1. This study has revealed variations within the hVEGFR1 tyrosine kinase domain, distinguishing between regions associated with phosphorylase kinase and transferase activities. We identified numerous potential phosphorylation sites within the TK domain, shedding light on the protein's regulation and signaling possible. Detailed molecular interaction analyses have elucidated the binding forces between lead molecules and hVEGFR1, including hydrogen bonds, electrostatic, hydrophobic, and π-sigma interactions. The stability observed during molecular dynamics simulations further underscores the biological relevance of these interactions. Furthermore, docked complexes has highlighted localized structural fluctuations, offering insight into potential allosteric effects and dynamic conformational changes induced by lead molecules. These findings not only provide a comprehensive characterization of hVEGFR1 but also pave the way for the development of targeted therapies. Eventually, this study has the potential in identifying drug to combat diseases associated with hVEGFR1 dysregulation, including cancer and angiogenesis-related disorders, contributing to effective treatment strategies.

Behenic Acid as a multi-target inhibiting antibacterial phytochemical against Vibrio parahaemolyticus and Aeromonas hydrophila for effective management of aquaculture infections: an in-silico, in-vitro & in-vivo experimentation.

Multi-Target Inhibitors are the upcoming frontrunners of the antibiotic world as they provide significant advantage over drug resistance development. Antibacterial drug discovery research, requires more robust and innovative approaches such as multi-target inhibiting drugs, which over comes the innate hurdles in the field of antibiotics. In this current study, a curated set of 5,112 phytochemical molecules were virtually screened for its multi-target inhibition potential against 7 antibacterial protein drug-targets. Behenic Acid was identified to be the most significant phytochemical molecule with potential to inhibit Catalase Peroxidase (KatG), Adenylosuccinate Synthetase (ADSS) and Pyridoxine 5'-Phosphate Synthase (PdxJ), based on SeeSAR and AutoDock Vina results. Further, the inhibition potential of Behenic Acid was validated using 500 ns Molecular Dynamics (MD) Simulation based on Desmond analysis. Behenic Acid was further investigated in-vitro using agar-well-diffusion and Minimal Inhibitory Concentration (MIC) assay, where it demonstrated 20 ± 1mm zone-of-inhibition and 50 µg/ml MIC value against both Vibrio parahaemolyticus and Aeromonas hydrophila. Zebrafish based investigations was carried to confirm the in-vivo antibacterial efficacy of Behenic Acid. It was observed that, there is a progressive dose-dependent recovery from the bacterial infection, with highest recovery and survival observed in fishes fed with 100 µg/day of Behenic Acid. Results of the in-vitro and in-vivo assays strongly support the in-silico prediction of the antibacterial activity of Behenic Acid. Based on the results presented in this study, it is concluded that, Behenic Acid is a strong multi-target antibacterial phytochemical, that exerts antagonism against aquaculture bacterial pathogens such as V. parahaemolytics and A. hydrophila.Communicated by Ramaswamy H. Sarma.

VSpipe-GUI, an Interactive Graphical User Interface for Virtual Screening and Hit Selection.

Virtual screening of large chemical libraries is essential to support computer-aided drug development, providing a rapid and low-cost approach for further experimental validation. However, existing computational packages are often for specialised users or platform limited. Previously, we developed VSpipe, an open-source semi-automated pipeline for structure-based virtual screening. We have now improved and expanded the initial command-line version into an interactive graphical user interface: VSpipe-GUI, a cross-platform open-source Python toolkit functional in various operating systems (e.g., Linux distributions, Windows, and Mac OS X). The new implementation is more user-friendly and accessible, and considerably faster than the previous version when AutoDock Vina is used for docking. Importantly, we have introduced a new compound selection module (i.e., spatial filtering) that allows filtering of docked compounds based on specified features at the target binding site. We have tested the new VSpipe-GUI on the Hepatitis C Virus NS3 (HCV NS3) protease as the target protein. The pocket-based and interaction-based modes of the spatial filtering module showed efficient and specific selection of ligands from the virtual screening that interact with the HCV NS3 catalytic serine 139.

Virtual screening of flavonoids as potential RIPK1 inhibitors for neurodegeneration therapy.

Background: Global prevalence of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease is increasing gradually, whereas approvals of successful therapeutics for central nervous system disorders are inadequate. Accumulating evidence suggests pivotal roles of the receptor-interacting serine/threonine-protein kinase 1 (RIPK1) in modulating neuroinflammation and necroptosis. Discoveries of potent small molecule inhibitors for RIPK1 with favorable pharmacokinetic properties could thus address the unmet medical needs in treating neurodegeneration. Methods: In a structure-based virtual screening, we performed site-specific molecular docking of 4,858 flavonoids against the kinase domain of RIPK1 using AutoDock Vina. We predicted physicochemical descriptors of the top ligands using the SwissADME webserver. Binding interactions of the best ligands and the reference ligand L8D were validated using replicated 500-ns Gromacs molecular dynamics simulations and free energy calculations. Results: From Vina docking, we shortlisted the top 20 flavonoids with the highest binding affinities, ranging from -11.7 to -10.6 kcal/mol. Pharmacokinetic profiling narrowed down the list to three orally bioavailable and blood-brain-barrier penetrant flavonoids: Nitiducarpin, Pinocembrin 7-O-benzoate, and Paratocarpin J. Next, trajectories of molecular dynamics simulations of the top protein-ligand complexes were analyzed for binding interactions. The root-mean-square deviation (RMSD) was 1.191 Å (±0.498 Å), 1.725 Å (±0.828 Å), 1.923 Å (±0.942 Å), 0.972 Å (±0.155 Å) for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D, respectively. The radius of gyration (Rg) was 2.034 nm (±0.015 nm), 2.0.39 nm (± 0.025 nm), 2.053 nm (±0.021 nm), 2.037 nm (±0.016 nm) for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D, respectively. The solvent accessible surface area (SASA) was 159.477 nm2 (±3.021 nm2), 159.661 nm2 (± 3.707 nm2), 160.755 nm2 (±4.252 nm2), 156.630 nm2 (±3.521 nm2), for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D complexes, respectively. Therefore, lower RMSD, Rg, and SASA values demonstrated that Nitiducarpin formed the most stable complex with the target protein among the best three ligands. Finally, 2D protein-ligand interaction analysis revealed persistent hydrophobic interactions of Nitiducarpin with the critical residues of RIPK1, including the catalytic triads and the activation loop residues, implicated in the kinase activity and ligand binding. Conclusion: Our target-based virtual screening identified three flavonoids as strong RIPK1 inhibitors, with Nitiducarpin exhibiting the most potent inhibitory potential. Future in vitro and in vivo studies with these ligands could offer new hope for developing effective therapeutics and improving the quality of life for individuals affected by neurodegeneration.

EasyDock: customizable and scalable docking tool.

Docking of large compound collections becomes an important procedure to discover new chemical entities. Screening of large sets of compounds may also occur in de novo design projects guided by molecular docking. To facilitate these processes, there is a need for automated tools capable of efficiently docking a large number of molecules using multiple computational nodes within a reasonable timeframe. These tools should also allow for easy integration of new docking programs and provide a user-friendly program interface to support the development of further approaches utilizing docking as a foundation. Currently available tools have certain limitations, such as lacking a convenient program interface or lacking support for distributed computations. In response to these limitations, we have developed a module called EasyDock. It can be deployed over a network of computational nodes using the Dask library, without requiring a specific cluster scheduler. Furthermore, we have proposed and implemented a simple model that predicts the runtime of docking experiments and applied it to minimize overall docking time. The current version of EasyDock supports popular docking programs, namely Autodock Vina, gnina, and smina. Additionally, we implemented a supplementary feature to enable docking of boron-containing compounds, which are not inherently supported by Vina and smina, and demonstrated its applicability on a set of 55 PDB protein-ligand complexes.

ReverseDock: a web server for blind docking of a single ligand to multiple protein targets using AutoDock Vina.

Several platforms exist to perform molecular docking to computationally predict binders to a specific protein target from a library of ligands. The reverse, that is, docking a single ligand to various protein targets, can currently be done by very few web servers, which limits the search to a small set of pre-selected human proteins. However, the possibility to in silico predict which targets a compound identified in a high-throughput drug screen bind would help optimize and reduce the costs of the experimental workflow needed to reveal the molecular mechanism of action of a ligand. Here, we present ReverseDock, a blind docking web server based on AutoDock Vina specifically designed to allow users with no computational expertise to dock a ligand to 100 protein structures of their choice. ReverseDock increases the number and type of proteins a ligand can be docked to, making the task of in silico docking of a ligand to entire families of proteins straightforward. We envision ReverseDock will support researchers by providing the possibility to apply inverse docking computations using web browser. ReverseDock is available at: https://reversedock.biologie.uni-freiburg.de/.

Fenretinide induces apoptosis and synergises the apoptosis inducing effect of gemcitabine through inhibition of key signalling molecules involved in A549 cell survival in in silico and in vitro analyses.

Fenretinide is a synthetic retinoid compound, which induces apoptosis via generating reactive oxygen species (ROS) and modulating PI3K/Akt/mTOR signalling pathway. We hypothesise that fenretinide's mechanism of action in triggering apoptosis may involve other targets, beside mTOR signalling pathway and it may augment apoptosis inducing effects of chemotherapeutic drugs in lung cancer. Time-lapse microscopy and Western blotting were used to evaluate apoptosis and apoptotic marker cleaved-Caspase 3 in A549 cells. Relative levels of protein phosphorylation and ROS were quantified by Human Phospho-Kinase Array Kit and CellROX® Green Reagent, respectively. Docking and simulation analyses of proteins and fenretinide interactions were identified and visualised by Discovery Studio Visualizer and AutoDock Vina software. Our results showed that fenretinide induced apoptosis in a dose dependant manner and combinations of fenretinide (5 µg/mL) and gemcitabine (1, 2, 4, 8 and 16 µg/mL) synergistically enhanced apoptosis in A549 cells. Fenretinide caused significant increase of cleaved-Caspase 3, de-phosphorylated p-S473 of Akt and failed to inhibit mTORC1 downstream targets. In silico results revealed that Akt required the lowest energy (-10.2 kcal/mol) to interact with fenretinide in comparison with other proteins. In conclusion, Akt may be exploited as a good target for induction of apoptosis in A549 cells and fenretinide has great potentials to fulfil this task. The mechanism by which fenretinide boosts the apoptosis inducing effects of gemcitabine, which is likely expected to be via inhibiting mTORC2 downstream targets. However, docking investigation revealed that fenretinide lacks specificity as it may also interact with several secondary targets beside Akt.

Setmelanotide optimization through fragment-growing, molecular docking in-silico method targeting MC4 receptor.

Obesity has emerged as a global issue, but with the complex structures of multiple related important targets and their agonists or antagonists determined, the mechanism of ligand-protein interaction may offer new chances for developing new generation agonists anti-obesity. Based on the molecule surface of the cryo-EM protein structure 7AUE, we tried to replace D-Ala3 with D-Met in setmelanotide as the linker site for fragment-growing with De novo evolution. The simulation results indicate that the derivatives could improve the binding abilities with the melanocortin 4 receptor and the selectivity over the melanocortin 1 receptor. The improved selectivity of the newly designed derivatives is mainly due to the shape difference of the molecular surface at the orthosteric peptide-binding pocket between melanocortin 4 receptor and melanocortin 1 receptor. The new extended fragments could not only enhance the binding affinities but also function as a gripper to seize the pore, making it easier to balance and stabilize the other component of the new derivatives. Although it is challenging to synthesize the compounds designed in silico, this study may perhaps serve as a trigger for additional anti-obesity research.Communicated by Ramaswamy H. Sarma.

Evaluation of piperine analogs against prostate cancer targeting AKT1 kinase domain through network pharmacological analysis.

Prostate cancer is the second most fatal malignancy in men after lung cancer, and the fifth leading cause of death. Piperine has been utilized for its therapeutic effects since the time of Ayurveda. According to traditional Chinese medicine, piperine has a wide variety of pharmacological effects, including anti-inflammatory, anti-cancer, and immune-regulating properties. Based on the previous study, Akt1 (protein kinase B) is one of the targets of piperine, it belongs to the group of oncogenes and the mechanism of the Akt1 is an interesting approach for anticancer drug design. From the peer-reviewed literature, five piperine analogs were identified altogether, and a combinatorial collection was formed. However, may not be entirely clear how piperine analogs work to prevent prostate cancer. In the present study, serine-threonine kinase domain Akt1 receptor was employed to analyze the efficacy of piperine analogs against standards using in silico methodologies. Additionally, their drug-likeness was evaluated utilizing online servers like Molinspiration and preADMET. Using AutoDock Vina, the interactions of five piperine analogs and two standards with Akt1 receptor was investigated. Our study reveals that piperine analog-2 (pip2) shows highest binding affinity (- 6.0 kcal/mol) by forming 6 hydrogen bonds with more hydrophobic interactions compared to other four analogs and standards. In conclusion, the piperine analog pip2, which shows strong inhibition affect in Akt1-cancer pathway, may be employed as chemotherapeutic drugs.

Potential effect of luteolin, epiafzelechin, and albigenin on rats under cadmium-induced inflammatory insult: In silico and in vivo approach.

Introduction: Cadmium(Cd) an industrial poison present abundantly in the environment, causes human toxicity by an inflammatory process. Chronic exposure of cadmium can cause a number of molecular lesions that could be relevant to oncogenesis, through indirect or epigenetic mechanisms, potentially including abnormal activation of oncogenes and suppression of apoptosis by depletion of antioxidants. As induction of cyclooxygenase (COX)-2 is linked to inflammatory processes, use of luteolin, epiafzelechin, and albigenin alone or in different combinations may be used as anti-inflammatory therapeutic agents. Methods: We, herein, performed in silico experiments to check the binding affinity of phytochemicals and their therapeutic effect against COX-2 in cadmium administered rats. Wistar albino rats were given phytochemicals in different combinations to check their anti-inflammatory activities against cadmium intoxication. The level of alanine aminotransferases (ALT), 4-hydroxynonenal (4HNE), 8-hydroxy-2-deoxyguanosine (8-OHdG), tumor necrosis factor-alpha (TNF-α), isoprostanes (IsoP-2α), COX-2, and malondialdehyde (MDA) were estimated with their respective ELISA and spectrophotometric methods. Results: The generated results show that phytocompounds possessed good binding energy potential against COX-2, and common interactive behavior was observed in all docking studies. Moreover, the level of ALT, 4HNE, 8-OHdG, TNF-α, IsoP-2α, malondialdehyde, and COX-2 were significantly increased in rats with induced toxicity compared to the control group, whereas in combinational therapy of phytocompounds, the levels were significantly decreased in the group. Discussion: Taken together, luteolin, epiafzelechin, and albigenin can be used as anti-inflammatory therapeutic agents for future novel drug design, and thus it may have therapeutic importance against cadmium toxicity.

AlteQ: a new complementarity principle-centered method for the evaluation of docking poses.

Molecular docking is the most popular and widely used method for identifying novel molecules against a target of interest. However, docking procedures and their validation are still under intense development. In the present investigation, we evaluate a quantum free-orbital AlteQ method for evaluating docking complexes generated by taking EGFR complexes as an example. The AlteQ method calculates the electron density using Slater's type atomic contributions in the interspace between the receptor and the ligand. Since the interactions are determined by the overlap of electron clouds, they follow the complementarity principle, and an equation can be obtained that describes these interactions. The AlteQ method evaluates the quality of the interaction between the receptor and the ligand, how complementary the interactions are, and due to this, it is used to reject less realistic structures obtained by docking methods. Here, three different equations were used to determine the quality of the interactions in experimental complexes and docked complexes obtained using AutoDock Vina and AutoDock 4.2.6.Communicated by Ramaswamy H. Sarma.

Identification of potential inhibitors of Mycobacterium tuberculosis shikimate kinase: molecular docking, in silico toxicity and in vitro experiments.

Tuberculosis (TB) is one of the main causes of death from a single pathological agent, Mycobacterium tuberculosis (Mtb). In addition, the emergence of drug-resistant TB strains has exacerbated even further the treatment outcome of TB patients. It is thus needed the search for new therapeutic strategies to improve the current treatment and to circumvent the resistance mechanisms of Mtb. The shikimate kinase (SK) is the fifth enzyme of the shikimate pathway, which is essential for the survival of Mtb. The shikimate pathway is absent in humans, thereby indicating SK as an attractive target for the development of anti-TB drugs. In this work, a combination of in silico and in vitro techniques was used to identify potential inhibitors for SK from Mtb (MtSK). All compounds of our in-house database (Centro de Pesquisas em Biologia Molecular e Funcional, CPBMF) were submitted to in silico toxicity analysis to evaluate the risk of hepatotoxicity. Docking experiments were performed to identify the potential inhibitors of MtSK according to the predicted binding energy. In vitro inhibitory activity of MtSK-catalyzed chemical reaction at a single compound concentration was assessed. Minimum inhibitory concentration values for in vitro growth of pan-sensitive Mtb H37Rv strain were also determined. The mixed approach implemented in this work was able to identify five compounds that inhibit both MtSK and the in vitro growth of Mtb.

Benchmarked molecular docking integrated molecular dynamics stability analysis for prediction of SARS-CoV-2 papain-like protease inhibition by olive secoiridoids.

Objectives: We performed a virtual screening of olive secoiridoids of the OliveNetTM library to predict SARS-CoV-2 PLpro inhibition. Benchmarked molecular docking protocol that evaluated the performance of two docking programs was applied to execute virtual screening. Molecular dynamics stability analysis of the top-ranked olive secoiridoid docked to PLpro was also carried out. Methods: Benchmarking virtual screening used two freely available docking programs, AutoDock Vina 1.1.2. and AutoDock 4.2.1. for molecular docking of olive secoiridoids to a single PLpro structure. Screening also included benchmark structures of known active and decoy molecules from the DEKOIS 2.0 library. Based on the predicted binding energies, the docking programs ranked the screened molecules. We applied the usual performance evaluation metrices to evaluate the docking programs using the predicted ranks. Molecular dynamics of the top-ranked olive secoiridoid bound to PLpro and computation of MM-GBSA energy using three iterations during the last 50 ps of the analysis of the dynamics in Desmond supported the stability prediction. Results and discussions: Predictiveness curves suggested that AutoDock Vina has a better predictive ability than AutoDock, although there was a moderate correlation between the active molecules rankings (Kendall's correlation of rank (τ) = 0.581). Interestingly, two same molecules, Demethyloleuropein aglycone, and Oleuroside enriched the top 1 % ranked olive secoiridoids predicted by both programs. Demethyloleuropein aglycone bound to PLpro obtained by docking in AutoDock Vina when analyzed for stability by molecular dynamics simulation for 50 ns displayed an RMSD, RMSF<2 Å, and MM-GBSA energy of -94.54 ± 6.05 kcal/mol indicating good stability. Molecular dynamics also revealed the interactions of Demethyloleuropein aglycone with binding sites 2 and 3 of PLpro, suggesting a potent inhibition. In addition, for 98 % of the simulation time, two phenolic hydroxy groups of Demethyloleuropein aglycone maintained two hydrogen bonds with Asp302 of PLpro, specifying the significance of the groups in receptor binding. Conclusion: AutoDock Vina retrieved the active molecules accurately and predicted Demethyloleuropein aglycone as the best inhibitor of PLpro. The Arabian diet consisting of olive products rich in secoiridoids benefits from the PLpro inhibition property and reduces the risk of viral infection.

TULP3 NLS inhibition: an in silico study to hamper cargo transport to nucleus.

TULP3 is involved in cell regulation pathways including transcription and signal transduction. In some pathological states like in cancers, increased level of TULP3 has been observed so it can serve as a potential target to hamper the activation of those pathways. We propose a novel idea of inhibiting nuclear localization signal (NLS) to interrupt nuclear translocation of TULP3 so that the downstream activations of pathways are blocked. In current in silico study, 3D structure of TULP3 was modeled using 8 different tools including I-TASSER, CABS-FOLD, Phyre2, PSIPRED, RaptorX, Robetta, Rosetta and Prime by Schrödinger. Best structure was selected after quality evaluation by SAVES and implied for the investigation of NLS sequence. Mapped NLS sequence was further used to dock with natural ligand importin-α as control docking to validate the NLS sequence as binding site. After docking and molecular dynamics (MD) simulation validation, these residues were used as binding side for subsequent docking studies. 70 alkaloids were selected after intensive literature survey and were virtually docked with NLS sequence where natural ligand importin-α is supposed to be bound. This study demonstrates the virtual inhibition of NLS sequence so that it paves a way for future in-vivo studies to use NLS as a new drug target for cancer therapeutics.Communicated by Ramaswamy H. Sarma.

Investigating the Anticancer Activity of G-Rh1 Using In Silico and In Vitro Studies (A549 Lung Cancer Cells).

Ginsenoside Rh1 (G-Rh1), a possible bioactive substance isolated from the Korean Panax ginseng Meyer, has a wide range of pharmacological effects. In this study, we have investigated the anticancer efficacy of G-Rh1 via in silico and in vitro methodologies. This study mainly focuses on the two metastatic regulators, Rho-associated protein kinase 1 (ROCK1) and RhoA, along with other standard apoptosis regulators. The ROCK1 protein is a member of the active serine/threonine kinase family that is crucial for many biological processes, including cell division, differentiation, and death, as well as many cellular processes and muscle contraction. The abnormal activation of ROCK1 kinase causes several disorders, whereas numerous studies have also shown that RhoA is expressed highly in various cancers, including colon, lung, ovarian, gastric, and liver malignancies. Hence, inhibiting both ROCK1 and RhoA will be promising in preventing metastasis. Therefore, the molecular level interaction of G-Rh1 with the ROCK1 and RhoA active site residues from the preliminary screening clearly shows its inhibitory potential. Molecular dynamics simulation and principal component analysis give essential insights for comprehending the conformational changes that result from G-Rh1 binding to ROCK1 and RhoA. Further, MTT assay was employed to examine the potential cytotoxicity in vitro against human lung cancer cells (A549) and Raw 264.7 Murine macrophage cells. Thus, G-Rh1 showed significant cytotoxicity against human lung adenocarcinoma (A549) at 100 µg/mL. In addition, we observed an elevated level of reactive oxygen species (ROS) generation, perhaps promoting cancer cell toxicity. Additionally, G-Rh1 suppressed the mRNA expression of RhoA, ROCK1, MMP1, and MMP9 in cancer cell. Accordingly, G-Rh1 upregulated the p53, Bax, Caspase 3, caspase 9 while Bcl2 is downregulated intrinsic pathway. The findings from our study propose that the anticancer activity of G-Rh1 may be related to the induction of apoptosis by the RhoA/ROCK1 signaling pathway. As a result, this study evaluated the functional drug-like compound G-Rh1 from Panax ginseng in preventing and treating lung cancer adenocarcinoma via regulating metastasis and apoptosis.

Computational Design of Phosphatidylinositol 3-Kinase Inhibitors.

One of the most sought-after therapeutic targets for treating human cancers is the phosphoinositide 3-kinase; PI3k is an integral part of the PI3K/protein kinase B signaling arcade. This pathway is frequently activated in malignancies. Drug resistance and dose-limiting adverse effects are currently associated challenges with the existing anticancer chemotherapy. Therefore, in this research, a series of pyrimidine derivatives were designed and evaluated against human PI3K by using molecular docking analysis. The docking results were further verified by molecular dynamic simulation, which analyzed the strength of the macromolecular complex with respect to time. Compounds IV and XIV were found to be the most potent inhibitors of the human PI3K receptor with a high degree of stability within the active site of the target receptor for a timeframe of 50 ns. Thus, both of these compounds could be important drug candidates for the development of PI3K inhibitors as a prospective anticancer agent.

A Molecular Docking Study of Human STEAP2 for the Discovery of New Potential Anti-Prostate Cancer Chemotherapeutic Candidates.

Prostate cancer is a rising health concern and accounts for 3.8% of all cancer deaths globally. Uganda has one of the highest incidence rates of the disease in Africa at 5.2% with the majority of diagnosed patients found to have advanced disease. This study aimed to use the STEAP2 protein (prostate cancer-specific biomarker) for the discovery of new targeted therapy. To determine the most likely compound that can bind to the STEAP2 protein, we docked the modeled STEAP2 3D structure against 2466 FDA (Food and Drug Administration)-approved drug candidates using AutoDock Vina. Protein basic local alignment search tool (BLASTp) search, multiple sequence alignment (MSA), and phylogenetics were further carried out to analyze the diversity of this marker and determine its conserved domains as suitable target regions. Six promising drug candidates (ligands) were identified. Triptorelin had the highest binding energy (-12.1 kcal/mol) followed by leuprolide (docking energy: -11.2 kcal/mol). All the top two drug candidates interacted with residues Ser-372 and Gly-369 in close proximity with the iron-binding domain (an important catalyst of metal reduction). The two drugs had earlier been approved for the treatment of advanced prostate cancer with an elusive mode of action. Through this study, further insight into figuring out their interaction with STEAP2 might be important during treatment.