The recent explosion of interest in the bioactivity of the flavonoids of microalgae is due to the potential health benefits of the polyphenolic components that are major dietary constituents. The ...present study focuses on the phytochemical screening and in silico studies of flavonoids. Total flavonoids content in Chlorella pyrenoidosa was estimated in two modes of cultivation (Autotrophic and Heterotrophic) and its implication in anti-proliferation and anti-inflammatory activity was assessed through in silico approach. H-Ras p21(PDB-4L9S) and Lipoxygenase (PDB-3V99) involved in proliferation pathway and inflammatory pathway were selected as the target proteins for in silico studies. Seven compounds were selected for molecular docking. Pharmacokinetic properties of these compounds were calculated using online tools and docking was performed using Auto Dock Vina. By comparing and analyzing their binding energies in Maestro Schrodinger, suite, it was observed that Epigallocatechin gallate exhibited least binding energy of −9.1 kcal/mol and hence has anti-inflammatory activity. Catechin has best binding affinity with H-Ras p21 and hence has anti proliferative activity.
•In both the modes, aqueous extract yielded more phytochemicals (1.21 mg PE/mg of total phenols and 0.87 mg RE/mg dry cell weight of flavonoids) when compared to hexane and ethyl acetate extracts.•Chlorella pyrenoidosa, showed the presence of flavonoids caffeine, catechin, epicatechin, epigallocatechingallate,dihyroquerecetin-7,4′-dimethyl-ether,caffeoyl-d-Glucose and protocatechuic acid.•It was observed that Epigallocatechin gallate had the best binding energy (BE) indicating the best possible pose with a BE of −9.1 kcal/mol against the target molecule 3V99.
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•A two-step protocol enables more effcient computational receptor-ligand docking.•Machine learning-based pipeline provides an effective way to evaluate binding regions for ...protein–ligand complexes.•The algorithm works for both peptide and non-peptide ligands, and also in cases where the binding site is buried.•When binding region data are not available, the algorithm provides a fast and effective alternative to classical predictors.•The approach can be generalized by extending the training dataset to include other classes of compounds.
We present a combined computational approach to protein–ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.
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.
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•Investigation into enhancing texture of plant-based cheese through molecular docking.•MD Analysis of tocopherol and retinol complexes with zein protein.•Integration of SVM for enhanced understanding of protein-ligand interactions.•Utilization of computational techniques for cheese innovation and development.
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
Objective: Mechanistic study of newly reported anti-Parkinson agents by molecular docking to predict possible target.Methods: Structures of newer drugs known anti-Parkinson agents were drawn using ...ChemBioDraw 2D software. Thereafter, they were converted to 3D structures using ChemBioDraw 3D software in which they were subjected to energy minimization using the MM2 method and then saved as PDB extension files, which can be accessed using the AutoDock Vina (ADT) interface. ADT 1.5.6 software version was used for molecular docking study.Results: Various molecular targets were selected (D2/D3, D2, A2A, and MAO-B) and studied for Pardoprunox, Istradefylline, Rasagiline, and Bromocriptine. Pardoprunox, Istradefylline, and Bromocriptine had more affinity with their corresponding receptor with −6.9, −8.5, and −9.4 kcal/mol binding affinity, respectively, except Rasagiline, who has less affinity with its corresponding receptor (−6.4kcal/mol) and shown better affinity with 3pbl receptor (−6.7 kcal/mol).Conclusion: Pardoprunox, Istradefylline, and Bromocriptine were found to act on D2/D3 (3pbl), A2A (3pwh), and D2 (4yyw), respectively, whereas Rasagiline found to be act on D2/D3 (3pbl) receptor. The results help in prediction of mechanism and interaction to various Parkinson’s disease targets.
Molecular docking is a powerful technique that helps uncover the structural and energetic bases of the interaction between macromolecules and substrates, endogenous and exogenous ligands, and ...inhibitors. Moreover, this technique plays a pivotal role in accelerating the screening of large libraries of compounds for drug development purposes. The need to promote community-driven drug development efforts, especially as far as neglected diseases are concerned, calls for user-friendly tools to allow non-expert users to exploit the full potential of molecular docking. Along this path, here is described the implementation of DockingApp, a freely available, extremely user-friendly, platform-independent application for performing docking simulations and virtual screening tasks using AutoDock Vina. DockingApp sports an intuitive graphical user interface which greatly facilitates both the input phase and the analysis of the results, which can be visualized in graphical form using the embedded JMol applet. The application comes with the DrugBank set of more than 1400 ready-to-dock, FDA-approved drugs, to facilitate virtual screening and drug repurposing initiatives. Furthermore, other databases of compounds such as ZINC, available also in AutoDock format, can be readily and easily plugged in.
Objective: APOBEC3B (A3B) enzyme causes C-to-T or C-to-G somatic alteration in the cancer genome, leading to the evolution of a broad spectrum of human cancers. The present study aims to identify A3B ...small molecule inhibitors using a top-down approach via pharmacoinformatic virtual screening.
Methods: Virtual screening of 2951 drug-alike molecules with diversified structures from the National Cancer Institute Development Therapeutics Program (DTP-NCI) compounds library was performed using GOLD and AutoDock Vina docking programs against the 3D structure of A3B (PDB ID: 5TD5).
Results: Amongst the docked compounds, Nordracorubin, NSC641233 and Raloxifene hydrochloride showed the most potent binding affinities towards A3B on both Autodock/Vina and GOLD. Several significant similarities were observed between A3B and the three hits, including hydrogen bonds and pi-pi stacking. The three compounds also exhibited interaction with the centralized zinc cofactor and amino acid residues that directly contribute the deaminase activity of A3B enzyme.
Conclusion: We hypothesize that the findings from this study could significantly shorten the quest for novel molecules against the A3B after confirmation with subsequent in vitro and in vivo studies in the near future.
Background
Computational approaches have emerged as an instrumental methodology in modern research. For example, virtual screening by molecular docking is routinely used in computer-aided drug ...discovery. One of the critical parameters for ligand docking is the size of a search space used to identify low-energy binding poses of drug candidates. Currently available docking packages often come with a default protocol for calculating the box size, however, many of these procedures have not been systematically evaluated.
Methods
In this study, we investigate how the docking accuracy of AutoDock Vina is affected by the selection of a search space. We propose a new procedure for calculating the optimal docking box size that maximizes the accuracy of binding pose prediction against a non-redundant and representative dataset of 3,659 protein-ligand complexes selected from the Protein Data Bank. Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening. Binding pockets in both datasets are derived from the experimental complex structures and, additionally, predicted by
e
FindSite.
Results
A systematic analysis of ligand binding poses generated by AutoDock Vina shows that the highest accuracy is achieved when the dimensions of the search space are 2.9 times larger than the radius of gyration of a docking compound. Subsequent virtual screening benchmarks demonstrate that this optimized docking box size also improves compound ranking. For instance, using predicted ligand binding sites, the average enrichment factor calculated for the top 1 % (10 %) of the screening library is 8.20 (3.28) for the optimized protocol, compared to 7.67 (3.19) for the default procedure. Depending on the evaluation metric, the optimal docking box size gives better ranking in virtual screening for about two-thirds of target proteins.
Conclusions
This fully automated procedure can be used to optimize docking protocols in order to improve the ranking accuracy in production virtual screening simulations. Importantly, the optimized search space systematically yields better results than the default method not only for experimental pockets, but also for those predicted from protein structures. A script for calculating the optimal docking box size is freely available at
www.brylinski.org/content/docking-box-size
.
Graphical Abstract
We developed a procedure to optimize the box size in molecular docking calculations. Left panel shows the predicted binding pose of NADP (green sticks) compared to the experimental complex structure of human aldose reductase (blue sticks) using a default protocol. Right panel shows the docking accuracy using an optimized box size.
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