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.

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.

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.

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.

Raloxifene, a SERM targets PD-L1: an in-silico study.

OBJECTIVES: Immunotherapeutic interventions in cancer have been considerably successful and widely accepted for cancer treatment, but are costly and cannot be afforded by all patients. Because of the high cost, the pharmaceutical research groups across the world are sufficiently motivated to discover or design small molecule inhibitors to treat cancer through inhibition of the immune checkpoint proteins previously targeted with monoclonal antibodies. The presented study was designed with an aim to establish raloxifene, a selective estrogen receptor modulator (SERM) as a potential ligand of the immune checkpoint protein Programmed death ligand-1 (PD-L1). METHODS: In the presented study, the in-silico approach was used for identifying a lead molecule against PD-L1. The hits were screened using the similarity-search method, and drug-likeliness analysis, and the leads were identified through ligand-docking using Autodock. In-vitro cytotoxicity analysis was carried out using the standard sulphorhodamine B (SRB) assay and the wound healing analysis to show the inhibition of cellular migration was performed using the standard scratch assay. RESULTS: The in-silico study revealed that raloxifene showed a high drug likelihood and higher binding affinity with PD-L1 as compared to the positive control (BMS-1166; BMS is Bristol Myers Squibb). The binding of raloxifene was shown to occur in the same region as the FDA-approved monoclonal antibodies atezolizumab and durvalumab, indicating the potential of raloxifene for PD1/PD-L1 blockade. In the in-vitro studies, raloxifene showed a time-dependent reduction in IC50 values for the cell line HCT116 (colon cancer). The scratch assay also revealed that raloxifene significantly reduced the migratory potential of HCT-116 cells in-vitro. CONCLUSIONS: PD-L1 is a potential target of the SERM raloxifene in-silico. Overall, this study is one step further towards immune checkpoint blockade using small-molecule inhibitors.

Molecular Structural Analysis of Porcine CMAH-Native Ligand Complex and High Throughput Virtual Screening to Identify Novel Inhibitors.

Porcine meat is the most consumed red meat worldwide. Pigs are also vital tools in biological and medical research. However, xenoreactivity between porcine's N-glycolylneuraminic acid (Neu5Gc) and human anti-Neu5Gc antibodies poses a significant challenge. On the one hand, dietary Neu5Gc intake has been connected to particular human disorders. On the other hand, some pathogens connected to pig diseases have a preference for Neu5Gc. The Cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) catalyses the conversion of N-acetylneuraminic acid (Neu5Ac) to Neu5Gc. In this study, we predicted the tertiary structure of CMAH, performed molecular docking, and analysed the protein-native ligand complex. We performed a virtual screening from a drug library of 5M compounds and selected the two top inhibitors with Vina scores of -9.9 kcal/mol for inhibitor 1 and -9.4 kcal/mol for inhibitor 2. We further analysed their pharmacokinetic and pharmacophoric properties. We conducted stability analyses of the complexes with molecular dynamic simulations of 200 ns and binding free energy calculations. The overall analyses revealed the inhibitors' stable binding, which was further validated by the MMGBSA studies. In conclusion, this result may pave the way for future studies to determine how to inhibit CMAH activities. Further in vitro studies can provide in-depth insight into these compounds' therapeutic potential.

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.

Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents.

Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. We examined the performance of five molecular docking programs (AutoDock 4, AutoDock Vina, DOCK 6, rDock, and RLDock) for their ability to model ribosomal-ligand interactions with oxazolidinones. Eleven ribosomal crystal structures with oxazolidinones as the ligands were docked. The accuracy was evaluated by calculating the docked complexes' root-mean-square deviation (RMSD) and the program's internal scoring function. The rankings for each program based on the median RMSD between the native and predicted were DOCK 6 > AD4 > Vina > RDOCK >> RLDOCK. Results demonstrate that the top-performing program, DOCK 6, could accurately replicate the ligand binding in only four of the eleven ribosomes due to the poor electron density of said ribosomal structures. In this study, we have further benchmarked the performance of the DOCK 6 docking algorithm and scoring in improving virtual screening (VS) enrichment using the dataset of 285 oxazolidinone derivatives against oxazolidinone binding sites in the S. aureus ribosome. However, there was no clear trend between the structure and activity of the oxazolidinones in VS. Overall, the docking performance indicates that the RNA pocket's high flexibility does not allow for accurate docking prediction, highlighting the need to validate VS. protocols for ligand-RNA before future use. Later, we developed a re-scoring method incorporating absolute docking scores and molecular descriptors, and the results indicate that the descriptors greatly improve the correlation of docking scores and pMIC values. Morgan fingerprint analysis was also used, suggesting that DOCK 6 underpredicted molecules with tail modifications with acetamide, n-methylacetamide, or n-ethylacetamide and over-predicted molecule derivatives with methylamino bits. Alternatively, a ligand-based approach similar to a field template was taken, indicating that each derivative's tail groups have strong positive and negative electrostatic potential contributing to microbial activity. These results indicate that one should perform VS. campaigns of ribosomal antibiotics with care and that more comprehensive strategies, including molecular dynamics simulations and relative free energy calculations, might be necessary in conjunction with VS. and docking.

Engineered Glycosidase for Significantly Improved Production of Naturally Rare Vina-Ginsenoside R7.

Ginsenosides are the main bioactive ingredients in plants of the genus Panax. Vina-ginsenoside R7 (VG-R7) is one of the rare high-value ginsenosides with health benefits. The only reported method for preparing VG-R7 involves inefficient and low-yield isolation from highly valuable natural resources. Notoginsenoside Fc (NG-Fc) isolated in the leaves and stems of Panax notoginseng is a suitable substrate for the preparation of VG-R7 via specific hydrolysis of the outside xylose at the C-20 position. Here, we first screened putative enzymes belonging to the glycoside hydrolase (GH) families 1, 3, and 43 and found that KfGH01 can specifically hydrolyze the ß-d-xylopyranosyl-(1 → 6)-ß-d-glucopyranoside linkage of NG-Fc to form VG-R7. The I248F/Y410R variant of KfGH01 obtained by protein engineering displayed a kcat/KM value (305.3 min-1 mM-1) for the reaction enhanced by approximately 270-fold compared with wild-type KfGH01. A change in the shape of the substrate binding pockets in the mutant allows the substrate to sit closer to the catalytic residues which may explain the enhanced catalytic efficiency of the engineered enzyme. This study identifies the first glycosidase for bioconversion of a ginsenoside with more than four sugar units, and it will inspire efforts to investigate other promising enzymes to obtain valuable natural products.

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.

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.

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.

Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina.

Structure-based virtual high-throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the 'docking power'. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein-ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.

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.