MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations.

Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.

Identification of Phytochemicals with Inhibitory Potential Against Beta-lactamase Enzymes via Computer-aided Approach.

INTRODUCTION: Antibacterial drugs have been widely used for the past century to treat diseases, but their efficacy has been limited by multi-resistant pathogens, particularly those that utilize beta-lactamase enzymes. The inhibition of beta-lactamase enzymes holds great promise for reducing the influence of such pathogens. OBJECTIVE: This study aims to evaluate the mechanism of inhibition of phytochemicals with antibacterial activity against two classes of beta-lactamases using computational methods. METHODS: To achieve this objective, a total of thirty phytochemicals were docked against SHV-1 beta-lactamase and AmpC beta-lactamase after procurement from Protein Data Bank. The pharmacokinetics (ADMET) and density functional theory (DFT) analysis study were also conducted to unravel the nature of the top six most promising compounds on each protein. RESULTS: The results showed that a significant percentage of the compounds had binding affinities greater than that of avibactam, the positive control. Quercetin-3-O-rutinoside showed the most promising results against SHV-1 beta-lactamase with an affinity of -9.4 kcal/mol, while luteolin was found to be the most promising candidate against AmpC beta-lactamase with an affinity of -8.5 kcal/mol. DFT analysis demonstrated the reactivity of these compounds, and the ADMET study indicated that they were relatively safe. CONCLUSION: In conclusion, the study's findings suggest that the selected compounds have significant potential to inhibit beta-lactamase and may be used in combination with antibiotics against organisms that produce beta-lactamase. This study provides a basis for further research in a wet-lab setting to validate the results.

Navigating the complexities of docking tools with nicotinic receptors and acetylcholine binding proteins in the realm of neonicotinoids.

Molecular docking, pivotal in predicting small-molecule ligand binding modes, struggles with accurately identifying binding conformations and affinities. This is particularly true for neonicotinoids, insecticides whose impacts on ecosystems require precise molecular interaction modeling. This study scrutinizes the effectiveness of prominent docking software (Ledock, ADFR, Autodock Vina, CDOCKER) in simulating interactions of environmental chemicals, especially neonicotinoid-like molecules with nicotinic acetylcholine receptors (nAChRs) and acetylcholine binding proteins (AChBPs). We aimed to assess the accuracy and reliability of these tools in reproducing crystallographic data, focusing on semi-flexible and flexible docking approaches. Our analysis identified Ledock as the most accurate in semi-flexible docking, while Autodock Vina with Vinardo scoring function proved most reliable. However, no software consistently excelled in both accuracy and reliability. Additionally, our evaluation revealed that none of the tools could establish a clear correlation between docking scores and experimental dissociation constants (Kd) for neonicotinoid-like compounds. In contrast, a strong correlation was found with drug-like compounds, bringing to light a bias in considered software towards pharmaceuticals, thus limiting their applicability to environmental chemicals. The comparison between semi-flexible and flexible docking revealed that the increased computational complexity of the latter did not result in enhanced accuracy. In fact, the higher computational cost of flexible docking with its lack of enhanced predictive accuracy, rendered this approach useless for this class of compounds. Conclusively, our findings emphasize the need for continued development of docking methodologies, particularly for environmental chemicals. This study not only illuminates current software capabilities but also underscores the urgency for advancements in computational molecular docking as it is a relevant tool to environmental sciences.

Development of Novel Indazolyl-Acyl Hydrazones as Antioxidant and Anticancer Agents that Target VEGFR-2 in Human Breast Cancer Cells.

The increased expression of VEGFR-2 in a variety of cancer cells promotes a cascade of cellular responses that improve cell survival, growth, and proliferation. Heterocycles are common structural elements in medicinal chemistry and commercially available medications that target several biological pathways and induce cell death in cancer cells. Herein, the evaluation of indazolyl-acyl hydrazones as antioxidant and anticancer agents is reported. Compounds 4e and 4j showed inhibitory activity in free radical scavenging assays (DPPH and FRPA). The titled compounds were employed in cell viability studies using MCF-7 cells, and it was observed that compounds 4f and 4j exhibited IC50 values 15.83 µM and 5.72 µM, respectively. In silico docking revealed the favorable binding energies of -7.30 kcal/mol and -8.04 kcal/mol for these compounds towards Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2), respectively. In conclusion, compounds with antioxidant activity and that target VEGFR-2 in breast cancer cells are reported.

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.

AI protein structure prediction-based modeling and mutagenesis of a protostome receptor and peptide ligands reveal key residues for their interaction.

The protostome leucokinin (LK) signaling system, including LK peptides and their G protein-coupled receptors, has been characterized in several species. Despite the progress, molecular mechanisms governing LK peptide-receptor interactions remain to be elucidated. Previously, we identified a precursor protein for Aplysia leucokinin-like peptides (ALKs) that contains the greatest number of amidated peptides among LK precursors in all species identified so far. Here, we identified the first ALK receptor from Aplysia, ALKR. We used cell-based IP1 activation assays to demonstrate that two ALK peptides with the most copies, ALK1 and ALK2, activated ALKR with high potencies. Other endogenous ALK-derived peptides bearing the FXXWX-amide motif also activated ALKR to various degrees. Our examination of cross-species activity of ALKs with the Anopheles LK receptor was consistent with a critical role for the FXXWX-amide motif in receptor activity. Furthermore, we showed, through alanine substitution of ALK1, the highly conserved phenylalanine (F), tryptophan (W), and C-terminal amidation were each essential for receptor activation. Finally, we used an artificial intelligence-based protein structure prediction server (Robetta) and Autodock Vina to predict the ligand-bound conformation of ALKR. Our model predicted several interactions (i.e., hydrophobic interactions, hydrogen bonds, and amide-pi stacking) between ALK peptides and ALKR, and several of our substitution and mutagenesis experiments were consistent with the predicted model. In conclusion, our results provide important information defining possible interactions between ALK peptides and their receptors. The workflow utilized here may be useful for studying other ligand-receptor interactions for a neuropeptide signaling system, particularly in protostomes.

QSIdb: quorum sensing interference molecules.

Quorum sensing interference (QSI), the disruption and manipulation of quorum sensing (QS) in the dynamic control of bacteria populations could be widely applied in synthetic biology to realize dynamic metabolic control and develop potential clinical therapies. Conventionally, limited QSI molecules (QSIMs) were developed based on molecular structures or for specific QS receptors, which are in short supply for various interferences and manipulations of QS systems. In this study, we developed QSIdb (http://qsidb.lbci.net/), a specialized repository of 633 reported QSIMs and 73 073 expanded QSIMs including both QS agonists and antagonists. We have collected all reported QSIMs in literatures focused on the modifications of N-acyl homoserine lactones, natural QSIMs and synthetic QS analogues. Moreover, we developed a pipeline with SMILES-based similarity assessment algorithms and docking-based validations to mine potential QSIMs from existing 138 805 608 compounds in the PubChem database. In addition, we proposed a new measure, pocketedit, for assessing the similarities of active protein pockets or QSIMs crosstalk, and obtained 273 possible potential broad-spectrum QSIMs. We provided user-friendly browsing and searching facilities for easy data retrieval and comparison. QSIdb could assist the scientific community in understanding QS-related therapeutics, manipulating QS-based genetic circuits in metabolic engineering, developing potential broad-spectrum QSIMs and expanding new ligands for other receptors.

InstaDock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening.

Exploring protein-ligand interactions is a subject of immense interest, as it provides deeper insights into molecular recognition, mechanism of interaction and subsequent functions. Predicting an accurate model for a protein-ligand interaction is a challenging task. Molecular docking is a computational method used for predicting the preferred orientation, binding conformations and the binding affinity of a ligand to a macromolecular target, especially protein. It has been applied in 'virtual high-throughput screening' of chemical libraries containing millions of compounds to find potential leads in drug design and discovery. Here, we have developed InstaDock, a free and open access Graphical User Interface (GUI) program that performs molecular docking and high-throughput virtual screening efficiently. InstaDock is a single-click GUI that uses QuickVina-W, a modified version of AutoDock Vina for docking calculations, made especially for the convenience of non-bioinformaticians and for people who are not experts in using computers. InstaDock facilitates onboard analysis of docking and visual results in just a single click. To sum up, InstaDock is the easiest and more interactive interface than ever existing GUIs for molecular docking and high-throughput virtual screening. InstaDock is freely available for academic and industrial research purposes via https://hassanlab.org/instadock.

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.

A Bio-Indicator Pilot Study Screening Selected Heavy Metals in Female Hair, Nails, and Serum from Lifestyle Cosmetic, Canned Food, and Manufactured Drink Choices.

Lifestyles, genetic predispositions, environmental factors, and geographical regions are considered key factors of heavy metals initiatives related to health issues. Heavy metals enter the body via the environment, daily lifestyle, foods, beverages, cosmetics, and other products. The accumulation of heavy metals in the human body leads to neurological issues, carcinogenesis, failure of multiple organs in the body, and a reduction in sensitivity to treatment. We screened for Cr, Al, Pb, and Cd in selected foods, beverages, and cosmetics products depending on questionnaire outcomes from female volunteers. We also screened for Cr, Al, Pb, and Cd on hair, nails, and serum samples using inductively coupled plasma mass spectrometry (ICP-MS) from the same volunteers, and we analyzed the serum cholinesterase and complete blood picture (CBC). We performed an AutoDock study on Cr, Al, Pb, and Cd as potential ligands. Our results indicate that the most elevated heavy metal in the cosmetic sample was Al. In addition, in the food and beverages samples, it was Pb and Al, respectively. The results of the questionnaire showed that 71 percent of the female volunteers used the studied cosmetics, food, and beverages, which were contaminated with Cr, Al, Pb, and Cd, reflecting the high concentration of Cr, Al, Cd, and Pb in the three different types of biological samples of sera, nails, and hair of the same females, with 29 percent of the female volunteers not using the products in the studied samples. Our results also show an elevated level of cholinesterase in the serum of group 1 that was greater than group 2, and this result was confirmed by AutoDock. Moreover, the negative variation in the CBC result was compared with the reference ranges. Future studies should concentrate on the actions of these heavy metal contaminations and their potential health consequences for various human organs individually.

In Silico Identification of 1-DTP Inhibitors of Corynebacterium diphtheriae Using Phytochemicals from Andrographis paniculata.

A number of phytochemicals have been identified as promising drug molecules against a variety of diseases using an in-silico approach. The current research uses this approach to identify the phyto-derived drugs from Andrographis paniculata (Burm. f.) Wall. ex Nees (AP) for the treatment of diphtheria. In the present study, 18 bioactive molecules from Andrographis paniculata (obtained from the PubChem database) were docked against the diphtheria toxin using the AutoDock vina tool. Visualization of the top four molecules with the best dockscore, namely bisandrographolide (-10.4), andrographiside (-9.5), isoandrographolide (-9.4), and neoandrographolide (-9.1), helps gain a better understanding of the molecular interactions. Further screening using molecular dynamics simulation studies led to the identification of bisandrographolide and andrographiside as hit compounds. Investigation of pharmacokinetic properties, mainly ADMET, along with Lipinski's rule and binding affinity considerations, narrowed down the search for a potent drug to bisandrographolide, which was the only molecule to be negative for AMES toxicity. Thus, further modification of this compound followed by in vitro and in vivo studies can be used to examine itseffectiveness against diphtheria.

Parallel multi-swarm cooperative particle swarm optimization for protein-ligand docking and virtual screening.

BACKGROUND: A high-quality docking method tends to yield multifold gains with half pains for the new drug development. Over the past few decades, great efforts have been made for the development of novel docking programs with great efficiency and intriguing accuracy. AutoDock Vina (Vina) is one of these achievements with improved speed and accuracy compared to AutoDock4. Since it was proposed, some of its variants, such as PSOVina and GWOVina, have also been developed. However, for all these docking programs, there is still large room for performance improvement. RESULTS: In this work, we propose a parallel multi-swarm cooperative particle swarm model, in which one master swarm and several slave swarms mutually cooperate and co-evolve. Our experiments show that multi-swarm programs possess better docking robustness than PSOVina. Moreover, the multi-swarm program based on random drift PSO can achieve the best highest accuracy of protein-ligand docking, an outstanding enrichment effect for drug-like activate compounds, and the second best AUC screening accuracy among all the compared docking programs, but with less computation consumption than most of the other docking programs. CONCLUSION: The proposed multi-swarm cooperative model is a novel algorithmic modeling suitable for protein-ligand docking and virtual screening. Owing to the existing coevolution between the master and the slave swarms, this model in parallel generates remarkable docking performance. The source code can be freely downloaded from https://github.com/li-jin-xing/MPSOVina .

Improving ligand-ranking of AutoDock Vina by changing the empirical parameters.

AutoDock Vina (Vina) achieved a very high docking-success rate, p^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment Rset1=0.556±0.025 compared with RDefault=0.493±0.028 obtained by the original Vina and RVina1.2=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( Rset1=0.617±0.017 ) than the default package ( RDefault=0.543±0.020 ) and Vina version 1.2 ( RVina1.2=0.540±0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

RDPSOVina: the random drift particle swarm optimization for protein-ligand docking.

Protein-ligand docking is of great importance to drug design, since it can predict the binding affinity between ligand and protein, and guide the synthesis direction of the lead compounds. Over the past few decades, various docking programs have been developed, some of them employing novel optimization algorithms. However, most of those methods cannot simultaneously achieve both good efficiency and accuracy. Therefore, it is worthwhile to pour the efforts into the development of a docking program with fast speed and high quality of the solutions obtained. The research presented in this paper, based on the docking scheme of Vina, developed a novel docking program called RDPSOVina. The RDPSOVina employes a novel search algorithm but the same scoring function of Vina. It utilizes the random drift particle swarm optimization (RDPSO) algorithm as the global search algorithm, implements the local search with small probability, and applies Markov chain mutation to the particles' personal best positions in order to harvest more potential-candidates. To prove the outstanding docking performance in RDPSOVina, we performed the re-docking experiments on two PDBbind datasets and cross-docking experiments on the Sutherland-crossdock-set, respectively. The RDPSOVina exhibited superior protein-ligand docking accuracy and better cross-docking prediction with higher operation efficiency than most of the compared methods. It is available at https://github.com/li-jin-xing/RDPSOVina .

Combined ligand-based and structure-based design of PDE 9A inhibitors against Alzheimer's disease.

PDE9 enzyme hydrolyzes cGMP, which is involved in the regulation of synaptic plasticity through the NMDA pathway (a well-known excitotoxic target for AD) via activation of calcium/calmodulin-dependent neuronal NO synthases in the postsynaptic neurons. The inhibition of PDE9 leads to elevated cGMP levels, causing enhanced NMDA signaling and thus contributing to an increase in synaptic plasticity and stabilization. Therefore, it could be considered a pertinent target for AD drug discovery. PF-04447943 and BI-409306 targeting PDE9 are undergoing clinical trials (Phase II). The present study encompasses a pharmacophoric approach to identify potent PDE9 inhibitors using various computational methods. Pharmacophores generated from the PDB 6A3N yielded 37,554 virtual hits, which underwent drug likeliness and PAINS filtering to arrive at a few virtual leads. The leads were further subjected to extra precision docking, ADMET predictions, and molecular dynamics. The final hits, ZINC000001305675 and ZINC000000377099, showed superior docking scores of - 10.90 and - 10.30 kcal/mol and satisfactory predicted ADMET scores. The hits were subjected to molecular dynamics (MD) studies, wherein they formed stable complexes with PDE9 protein and had ligand RMSDs within acceptable limits. The processes involved in the combined ligand and structure-based strategies.

Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock.

Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.

Exploring GPR109A Receptor Interaction with Hippuric Acid Using MD Simulations and CD Spectroscopy.

Previous research has indicated that various metabolites belonging to phenolic acids (PAs), produced by gut microflora through the breakdown of polyphenols, help in promoting bone development and protecting bone from degeneration. Results have also suggested that G-protein-coupled receptor 109A (GPR109A) functions as a receptor for those specific PAs such as hippuric acid (HA) and 3-(3-hydroxyphenyl) propionic acid (3-3-PPA). Indeed, HA has a molecular structural similarity with nicotinic acid (niacin) which has been shown previously to bind to GPR109A receptor and to mediate antilipolytic effects; however, the binding pocket and the structural nature of the interaction remain to be recognized. In the present study, we employed a computational strategy to elucidate the molecular structural determinants of HA binding to GPR109A and GPR109B homology models in understanding the regulation of osteoclastogenesis. Based on the docking and molecular dynamics simulation studies, HA binds to GPR109A similarly to niacin. Specifically, the transmembrane helices 3, 4 and 6 (TMH3, TMH4 and TMH6) and Extracellular loop 1 and 2 (ECL1 and ECL2) residues of GRP109A; R111 (TMH3), K166 (TMH4), ECL2 residues; S178 and S179, and R251 (TMH6), and residues of GPR109B; Y87, Y86, S91 (ECL1) and C177 (ECL2) contribute for HA binding. Simulations and Molecular Mechanics Poisson-Boltzmann solvent accessible area (MM-PBSA) calculations reveal that HA has higher affinity for GPR109A than for GPR109B. Additionally, in silico mutation analysis of key residues have disrupted the binding and HA exited out from the GPR109A protein. Furthermore, measurements of time-resolved circular dichroism spectra revealed that there are no major conformational changes in the protein secondary structure on HA binding. Taken together, our findings suggest a mechanism of interaction of HA with both GPR109A and GPR109B receptors.

The Potential Role of Phenolic Acids from Salvia miltiorrhiza and Cynara scolymus and Their Derivatives as JAK Inhibitors: An In Silico Study.

JAK inhibition is a new strategy for treating autoimmune and inflammatory diseases. Previous studies have shown the immunoregulatory and anti-inflammatory effects of Salvia miltiorrhiza and Cynara scolymus and suggest that the bioactivity of their phenolic acids involves the JAK-STAT pathway, but it is unclear whether these effects occur through JAK inhibition. The JAK binding affinities obtained by docking Rosmarinic acid (RosA), Salvianolic acid A (SalA), Salvianolic acid C (SalC), Lithospermic acid, Salvianolic acid B and Cynarin (CY) to JAK (PDB: 6DBN) with AutoDock Vina are -8.8, -9.8, -10.7, -10.0, -10.3 and -9.7 kcal/mol, respectively. Their predicted configurations enable hydrogen bonding with the hinge region and N- and C-terminal lobes of the JAK kinase domain. The benzofuran core of SalC, the compound with the greatest binding affinity, sits near Leu959, such as Tofacitinib's pyrrolopyrimidine. A SalC derivative with a binding affinity of -12.2 kcal/mol was designed while maintaining this relationship. The docking results show follow-up studies of these phenolic acids as JAK inhibitors may be indicated. Furthermore, derivatives of SalC, RosA, CY and SalA can yield better binding affinity or bioavailability scores, indicating that their structures may be suitable as scaffolds for the design of new JAK inhibitors.

Accelerating AutoDock Vina with GPUs.

AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.

Phytochemical Analysis, Antimutagenic and Antiviral Activity of Moringa oleifera L. Leaf Infusion: In Vitro and In Silico Studies.

Moringa oleifera (M. oleifera) leaves are rich in nutrients and antioxidant compounds that can be consumed to prevent and overcome malnutrition. The water infusion of its leaf is the easiest way to prepare the herbal drink. So far, no information is available on the antioxidant, antimutagenic, and antivirus capacities of this infusion. This study aimed to determine the composition of the bioactive compounds in M. oleifera leaf infusion, measuring for antioxidant and antimutagenic activity, and evaluating any ability to inhibit the SARS-CoV-2 main protease (Mpro). The first two objectives were carried out in vitro. The third objective was carried out in silico. The phytochemical analysis of M. oleifera leaf infusion was carried out using liquid chromatography-mass spectrometry (LC-MS). Antioxidant activity was measured as a factor of the presence of the free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH). The antimutagenicity of M. oleifera leaf powder infusion was measured using the plasmid pBR322 (treated free radical). The interaction between bioactive compounds and Mpro of SARS-CoV-2 was analyzed via molecular docking. The totals of phenolic compound and flavonoid compound from M. oleifera leaf infusion were 1.780 ± 5.00 µg gallic acid equivalent/g (µg GAE/g) and 322.91 ± 0.98 µg quercetin equivalent/g (µg QE/g), respectively. The five main bioactive compounds involved in the infusion were detected by LC-MS. Three of these were flavonoid glucosides, namely quercetin 3-O-glucoside, kaempferol 3-O-neohesperidoside, and kaempferol 3-α-L-dirhamnosyl-(1→4)-ß-D-glucopyranoside. The other two compounds were undulatoside A, which belongs to chromone-derived flavonoids, and gentiatibetine, which belongs to alkaloids. The antioxidant activity of M. oleifera leaf infusion was IC50 8.19 ± 0.005 µg/mL, which is stronger than the standard butylated hydroxytoluene (BHT) IC50 11.60 ± 0.30 µg/mL. The infusion has an antimutagenic effect and therefore protects against deoxyribonucleic acid (DNA) damage. In silico studies showed that the five main bioactive compounds have an antiviral capacity. There were strong energy bonds between Mpro molecules and gentiatibetine, quercetin, undulatoside A, kaempferol 3-o-neohesperidoside, and quercetin 3-O-glucoside. Their binding energy values are -5.1, -7.5, -7.7, -5.7, and -8.2 kcal/mol, respectively. Their antioxidant activity, ability to maintain DNA integrity, and antimutagenic properties were more potent than the positive controls. It can be concluded that leaf infusion of M. oleifera does provide a promising herbal drink with good antioxidant, antimutagenic, and antivirus capacities.