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.

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.

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/.

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.

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.

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.

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.

Assessing Molecular Docking Tools to Guide the Design of Polymeric Materials Formulations: A Case Study of Canola and Soybean Protein

After more than 40 years of biopolymer development, the current research is still based on conventional laboratory techniques, which require a large number of experiments. Therefore, finding new research methods are required to accelerate and power the future of biopolymeric development. In this study, promising biopolymer-additive ranking was described using an integrated computer-aided molecular design platform. In this perspective, a set of 21 different additives with plant canola and soy proteins were initially examined by predicting the molecular interactions scores and mode of molecule interactions within the binding site using AutoDock Vina, Molecular Operating Environment (MOE), and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA). The findings of the investigated additives highlighted differences in their binding energy, binding sites, pockets, types, and distance of bonds formed that play crucial roles in protein-additive interactions. Therefore, the molecular docking approach can be used to rank the optimal additive among a set of candidates by predicting their binding affinities. Furthermore, specific molecular-level insights behind protein-additives interactions were provided to explain the ranking results. The highlighted results can provide a set of guidelines for the design of high-performance polymeric materials at the molecular level. As a result, we suggest that the implementation of molecular modeling can serve as a fast and straightforward tool in protein-based bioplastics design, where the correct ranking of additives among sets of candidates is often emphasized. Moreover, these approaches may open new ways for the discovery of new additives and serve as a starting point for more in-depth investigations into this area.

PyPLIF HIPPOS and Receptor Ensemble Docking Increase the Prediction Accuracy of the Structure-Based Virtual Screening Protocol Targeting Acetylcholinesterase

In this article, the upgrading process of the structure-based virtual screening (SBVS) protocol targeting acetylcholinesterase (AChE) previously published in 2017 is presented. The upgraded version of PyPLIF called PyPLIF HIPPOS and the receptor ensemble docking (RED) method using AutoDock Vina were employed to calculate the ensemble protein–ligand interaction fingerprints (ensPLIF) in a retrospective SBVS campaign targeting AChE. A machine learning technique called recursive partitioning and regression trees (RPART) was then used to optimize the prediction accuracy of the protocol by using the ensPLIF values as the descriptors. The best protocol resulting from this research outperformed the previously published SBVS protocol targeting AChE.

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.

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.

PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow

A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein–ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein–ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of 0.6 million mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein–ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is open source and freely available at https://psnpbind.org.

In search of HIV entry inhibitors using molecular docking, ADME and toxicity studies of some Thiazolidinone-Pyrazine derivatives against CXCR4 co-receptor

BACKGROUND

Entry inhibitors prevent binding of human immunodeficiency virus protein to the chemokine receptor CXCR4 and are used along with conventional anti-HIV therapy. They aid in restoring immunity and can prevent the development of HIV-TB co-infection.

AIM

In the present study various thiazolidinone-pyrazine derivatives earlier studied for NNRT inhibition activity were gauged for their entry inhibitor potential.

OBJECTIVE

Objective of the study is to perform molecular docking, ADME, toxicity studies of some Thiazolidinone-Pyrazine Derivatives as entry inhibitors targeting CXCR4 co-receptors.

METHODS

In-silico docking studies were performed using AutoDock Vina software and compounds were further studied for ADME and toxicity using SwissADME and pkCSM software respectively.

RESULTS

Taking into consideration the docking results, pharmacokinetic behaviour and toxicity profile four molecules (compound 1, 9, 11 and 16) have shown potential as entry inhibitors.

CONCLUSION

These compounds have shown potential as both NNRTI and entry inhibitors and hence can be used in management of immune compromised diseases like TB-HIV coinfection.

Methylxanthines as Potential Inhibitor of SARS-CoV-2: an In Silico Approach

The aim of the present study was to test the binding affinity of methylxanthines (caffeine/theine, methylxanthine, theobromine, theophylline and xanthine) to three potential target proteins namely Spike protein (6LZG), main protease (6LU7) and nucleocapsid protein N-terminal RNA binding domain (6M3M) of SARS-CoV-2. Proteins and ligand were generated using AutoDock 1.5.6 software. Binding affinity of methylxanthines with SARS-CoV-2 target proteins was determined using Autodock Vina. MD simulation of the best interacting complexes was performed using GROMACS 2018.3 (in duplicate) and Desmond program version 2.0 (academic version) (in triplicate) to study the stabile interaction of protein-ligand complexes. Among the selected methylxanthines, theophylline showed the best binding affinity with all the three targets of SARS-CoV-2 (6LZG - 5.7 kcal mol^-1, 6LU7 - 6.5 kcal mol^-1, 6M3M - 5.8 kcal mol^-1). MD simulation results of 100 ns (in triplicate) showed that theophylline is stable in the binding pockets of all the selected SARS-CoV-2 proteins. Moreover, methylxanthines are safer and less toxic as shown by high LD₅₀ value with Protox II software as compared to drug chloroquine. This research supports the use of methylxanthines as a SARS-CoV-2 inhibitor. It also lays the groundwork for future studies and could aid in the development of a treatment for SARS-CoV-2 and related viral infections.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40495-021-00276-3.

In silico molecular docking and dynamic simulation of eugenol compounds against breast cancer

Breast cancer is one of the most severe problems, and it is the primary cause of cancer-related death in females worldwide. The adverse effects and therapeutic resistance development are among the most potent clinical issues for potent medications for breast cancer treatment. The eugenol molecules have a significant affinity for breast cancer receptors. The aim of the study has been on the eugenol compounds, which has potent actions on Erα, PR, EGFR, CDK2, mTOR, ERBB2, c-Src, HSP90, and chemokines receptors inhibition. Initially, the drug-likeness property was examined to evaluate the anti-breast cancer activity by applying Lipinski's rule of five on 120 eugenol molecules. Further, structure-based virtual screening was performed via molecular docking, as protein-like interactions play a vital role in drug development. The 3D structure of the receptors has been acquired from the protein data bank and is docked with 87 3D PubChem and ZINC structures of eugenol compounds, and five FDA-approved anti-cancer drugs using AutoDock Vina. Then, the compounds were subjected to three replica molecular dynamic simulations run of 100 ns per system. The results were evaluated using root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein-ligand interactions to indicate protein-ligand complex stability. The results confirm that Eugenol cinnamaldehyde has the best docking score for breast cancer, followed by Aspirin eugenol ester and 4-Allyl-2-methoxyphenyl cinnamate. From the results obtained from in silico studies, we propose that the selected eugenols can be further investigated and evaluated for further lead optimization and drug development.

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

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 R set 1 = 0.556 ± 0.025 compared with R Default = 0.493 ± 0.028 obtained by the original Vina and R Vina 1.2 = 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 ( R set 1 = 0.617 ± 0.017 ) than the default package ( R Default = 0.543 ± 0.020 ) and Vina version 1.2 ( R Vina 1.2 = 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.

Virtual screening, drug-likeness analysis, and molecular docking study of potential severe acute respiratory syndrome coronavirus 2 main protease inhibitors

Due to the length of time required to develop specific antiviral agents, the World Health Organization adopted the strategy of repurposing existing medications to treat Coronavirus disease 2019 infection. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease is possible biological target for potential antiviral drugs. We selected various compounds from PubChem database based on the structure of main protease inhibitors in Protein Data Bank database. Ten compounds showed nontumorigenic and nonmutagenic potential and met Egan's and Lipinski's rules. Molecular docking analysis was performed using AutoDock Vina software. Based on number and type of key binding interactions, as well as docking scores, we selected compounds 6, 8, and 17 that demonstrated the highest binding affinity for the target protein. Molecular dynamics simulations were then carried out on the protein-top docked ligand complexes which were subjected to molecular mechanics/generalized Born and surface area calculations. The molecular dynamics simulation results indicated that protein-top docked ligand complexes showed good conformational stability. Among analyzed molecules, compound 17 emerged as the best in silico hit based on the docking score, MM/GBSA binding energy and MD results.

nCOV-19 peptides mass fingerprinting identification, binding, and blocking of inhibitors flavonoids and anthraquinone of Moringa oleifera and hydroxychloroquine

An rare pandemic of viral pneumonia occurs in December 2019 in Wuhan, China, which is now recognized internationally as Corona Virus Disease 2019 (COVID-19), the etiological agent classified as Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). According to the World Health Organization (WHO), it has so far expanded to more than 213 countries/territories worldwide. Our study aims to find the viral peptides of SARS-COV-2 by peptide mass fingerprinting (PMF) in order to predict its novel structure and find an inhibitor for each viral peptide. For this reason, we calculated the mass of amino acid sequences translated from the SARS-CoV2 whole genome and identify the peptides that may be a target for inhibition. Molecular peptide docking with Moringa oleifera, phytochemicals (aqueous and ethanolic) leaf extracts of flavonoids (3.56 ± 0.03), (3.83 ± 0.02), anthraquinone (11.68 ± 0.04), (10.86 ± 0.06) and hydroxychloroquine present therapy of COVID-19 in Pakistan for comparative study. Results indicate that 15 peptides of SARS-CoV2 have been identified from PMF, which is then used as a selective inhibitor. The maximum energy obtained from AutoDock Vina for hydroxychloroquine is -5.1 kcal/mol, kaempferol (flavonoid) is -6.2 kcal/mol, and for anthraquinone -6 kcal/mol. Visualization of docking complex, important effects are observed regarding the binding of peptides to drug compounds. In conclusion, it is proposed that these compounds are effective antiviral agents against COVID-19 and can be used in clinical trials.Communicated by Ramaswamy H. Sarma.

Computational Evaluation of Interaction Between Curcumin Derivatives and Amyloid-β Monomers and Fibrils: Relevance to Alzheimer's Disease

BACKGROUND

The most important hallmark in the neuropathology of Alzheimer's disease (AD) is the formation of amyloid-β (Aβ) fibrils due to the misfolding/aggregation of the Aβ peptide. Preventing or reverting the aggregation process has been an active area of research. Naturally occurring products are a potential source of molecules that may be able to inhibit Aβ42 peptide aggregation. Recently, we and others reported the anti-aggregating properties of curcumin and some of its derivatives in vitro, presenting an important therapeutic avenue by enhancing these properties.

OBJECTIVE

To computationally assess the interaction between Aβ peptide and a set of curcumin derivatives previously explored in experimental assays.

METHODS

The interactions of ten ligands with Aβ monomers were studied by combining molecular dynamics and molecular docking simulations. We present the in silico evaluation of the interaction between these derivatives and the Aβ42 peptide, both in the monomeric and fibril forms.

RESULTS

The results show that a single substitution in curcumin could significantly enhance the interaction between the derivatives and the Aβ42 monomers when compared to a double substitution. In addition, the molecular docking simulations showed that the interaction between the curcumin derivatives and the Aβ42 monomers occur in a region critical for peptide aggregation.

CONCLUSION

Results showed that a single substitution in curcumin improved the interaction of the ligands with the Aβ monomer more so than a double substitution. Our molecular docking studies thus provide important insights for further developing/validating novel curcumin-derived molecules with high therapeutic potential for AD.

Molecular Human Targets of Bioactive Alkaloid-Type Compounds from Tabernaemontana cymose Jacq

Alkaloids are a group of secondary metabolites that have been widely studied for the discovery of new drugs due to their properties on the central nervous system and their anti-inflammatory, antioxidant and anti-cancer activities. Molecular docking was performed for 10 indole alkaloids identified in the ethanol extract of Tabernaemontana cymosa Jacq. with 951 human targets involved in different diseases. The results were analyzed through the KEGG and STRING databases, finding the most relevant physiological associations for alkaloids. The molecule 5-oxocoronaridine proved to be the most active molecule against human proteins (binding energy affinity average = −9.2 kcal/mol) and the analysis of the interactions between the affected proteins pointed to the PI3K/ Akt/mTOR signaling pathway as the main target. The above indicates that indole alkaloids from T. cymosa constitute a promising source for the search and development of new treatments against different types of cancer.

Virtual screening on the web for drug repurposing: a primer

We describe a procedure of performing in silico (virtual) screening using a web-based service, the MTiOpenScreen, which is freely accessible to non-commercial users. We shall use the SARS-CoV-2 main protease as an example. Starting from a structure downloaded from the Protein Data Bank, we discuss how to prepare the coordinates file, taking into account the known biochemical background information of the target protein. The reader will find that this preparation step takes up most of the effort before the target is ready for screening. The steps for uploading the target structure and defining the search volume by critical residues, and the main parameters to use, are outlined. When this protocol is followed, the user will expect to obtain a ranked list of small approved drug compounds docked into the target structure. The results can be readily examined graphically on the web site or downloaded for studying in a local molecular graphics program such as PyMOL.

Direct acting anti-hepatitis C combinations as potential COVID-19 protease inhibitors

The coronavirus pandemic could be the most threatening outbreak in the twenty-first century. According to the latest records of world health organization, more than 130 millions have been infected by COVID-19, with more than 2.9 million reported deaths. Yet, there is no magic cure for treatment of COVID-19. The concept of drug repurposing has been introduced as a fast, life-saving approach for drug discovery. Drug repurposing infers investigating already approved drugs for new indications, using the available information about pathophysiology of diseases and pharmacodynamics of drugs. In a recent work, more than 3000 FDA approved drugs were tested using virtual screening as potential antiviral agents for COVID-19. In this work, the top ranked five hits from the previous docking results together with drugs of similar chemical feature and/or mechanistic destinations were further tested using AutoDock Vina. The results showed that anti-HCV combinations could be potential therapeutic regimens for COVID-19 infections.

Structure-based virtual screening of bioactive compounds from Indonesian medical plants against severe acute respiratory syndrome coronavirus-2

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a virus that causes the infectious disease coronavirus disease-2019. Currently, there is no effective drug for the prevention and treatment of this virus. This study aimed to identify secondary metabolites that potentially inhibit the key proteins of SARS-CoV-2. This was an in silico molecular docking study of several secondary metabolites of Indonesian herbal plant compounds and other metabolites with antiviral testing history. Virtual screening using AutoDock Vina of 216 Lipinski rule-compliant plant metabolites was performed on 3C-like protease (3CL^pro), RNA-dependent RNA polymerase (RdRp), and spike glycoprotein. Ligand preparation was performed using JChem and Schrödinger's software, and virtual protein elucidation was performed using AutoDockTools version 1.5.6. Virtual screening identified several RdRp, spike, and 3CL^pro inhibitors. Justicidin D had binding affinities of −8.7, −8.1, and −7.6 kcal mol⁻¹ on RdRp, 3CL^pro, and spike, respectively. 10-methoxycamptothecin had binding affinities of −8.5 and −8.2 kcal mol⁻¹ on RdRp and spike, respectively. Inoxanthone had binding affinities of −8.3 and −8.1 kcal mol⁻¹ on RdRp and spike, respectively, while binding affinities of caribine were −9.0 and −7.5 mol⁻¹ on 3CL^pro and spike, respectively. Secondary metabolites of compounds from several plants were identified as potential agents for SARS-CoV-2 therapy.

PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors

Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.

Biochemical characterization and molecular docking of cloned xylanase gene from Bacillus subtilis RTS expressed in E. coli

This study employed mesophilic Bacillus subtilis RTS strain isolated from soil with high xylanolytic activity. A 642 bp (xyn) xylanase gene (GenBank accession number MT677937) was extracted from Bacillus subtilis RTS and cloned in Escherichia coli BL21 cells using pET21c expression system. The cloned gene belongs to glycoside hydrolase family 11 with protein size of approximately 23 KDa. The recombinant xylanase showed optimal enzyme activity at 60 °C and at pH 6.5. Thermostability of recombinant xylanase was observed between the temperature range of 30-60 °C. Xylanase also remained stable in different concentration of various organic solvents (ethanol, butanol). This might be due to the formation of protein/organic solvent interface which prevents stripping of essential water molecules from enzyme, thus enzyme conformation and activity remained stable. Finally, the molecular docking analysis through AutoDock Vina showed the involvement of Tyr 108, Arg140 and Pro144 in protein-ligand interaction, which stabilizes this complex. The observed stability of recombinant xylanase at higher temperature and in the presence of organic solvent (ethanol, butanol) suggested possible application of this enzyme in biofuel and other industrial applications.

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.

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.

AMDock: a versatile graphical tool for assisting molecular docking with Autodock Vina and Autodock4

Abstract

AMDock (Assisted Molecular Docking) is a user-friendly graphical tool to assist in the docking of protein-ligand complexes using Autodock Vina and AutoDock4, including the option of using the Autodock4Zn force field for metalloproteins. AMDock integrates several external programs (Open Babel, PDB2PQR, AutoLigand, ADT scripts) to accurately prepare the input structure files and to optimally define the search space, offering several alternatives and different degrees of user supervision. For visualization of molecular structures, AMDock uses PyMOL, starting it automatically with several predefined visualization schemes to aid in setting up the box defining the search space and to visualize and analyze the docking results. One particularly useful feature implemented in AMDock is the off-target docking procedure that allows to conduct ligand selectivity studies easily. In summary, AMDock’s functional versatility makes it a very useful tool to conduct different docking studies, especially for beginners. The program is available, either for Windows or Linux, at https://github.com/Valdes-Tresanco-MS.

Reviewers

This article was reviewed by Alexander Krah and Thomas Gaillard.

Structure-Based Drug Design of an Inhibitor of the SARS-CoV-2 (COVID-19) Main Protease Using Free Software: A Tutorial for Students and Scientists

This paper describes the structure-based design of a preliminary drug candidate against COVID-19 using free software and publicly available X-ray crystallographic structures. The goal of this tutorial is to disseminate skills in structure-based drug design and to allow others to unleash their own creativity to design new drugs to fight the current pandemic. The tutorial begins with the X-ray crystallographic structure of the main protease (Mpro) of the SARS coronavirus (SARS-CoV) bound to a peptide substrate and then uses the UCSF Chimera software to modify the substrate to create a cyclic peptide inhibitor within the Mpro active site. Finally, the tutorial uses the molecular docking software AutoDock Vina to show the interaction of the cyclic peptide inhibitor with both SARS-CoV Mpro and the highly homologous SARS-CoV-2 Mpro. The supporting information (supplementary material) provides an illustrated step-by-step guide for the inhibitor design, to help readers design their own drug candidates for COVID-19 and the coronaviruses that will cause future pandemics. An accompanying preprint in bioRxiv [https://doi.org/10.1101/2020.08.03.234872] describes the synthesis of the cyclic peptide and the experimental validation as an inhibitor of SARS-CoV-2 Mpro.

GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking

Protein–ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, gwovina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side‐chain sampling. The new method was validated for rigid and flexible‐receptor docking using four independent datasets. In rigid docking, gwovina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible‐receptor docking, gwovina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, gwovina can play a role in solving the complex flexible‐receptor docking cases and is suitable for virtual screening of compound libraries. gwovina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.

MDockPeP: A Web Server for Blind Prediction of Protein-Peptide Complex Structures

Protein-peptide interactions mediate a wide range of important cellular tasks. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. Recently, we developed a docking-based method for predicting protein-peptide complex structures, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. The produced modes are then evaluated with a statistical potential-based scoring function. The method has been implemented into an online server, MDockPeP server, which is freely available at http://zougrouptoolkit.missouri.edu/mdockpep . The server can be used for protein-peptide complex structure prediction. The server can also be used for initial-stage sampling of the protein-peptide binding modes for computational-demanding simulation or docking methods.

Computational screening of antagonists against the SARS-CoV-2 (COVID-19) coronavirus by molecular docking

In the current spread of novel coronavirus (SARS-CoV-2), antiviral drug discovery is of great importance. AutoDock Vina was used to screen potential drugs by molecular docking with the structural protein and non-structural protein sites of new coronavirus. Ribavirin, a common antiviral drug, remdesivir, chloroquine and luteolin were studied. Honeysuckle is generally believed to have antiviral effects in traditional Chinese medicine. In this study, luteolin (the main flavonoid in honeysuckle) was found to bind with a high affinity to the same sites of the main protease of SARS-CoV-2 as the control molecule. Chloroquine has been proved clinically effective and can bind to the main protease; this may be the antiviral mechanism of this drug. The study was restricted to molecular docking without validation by molecular dynamics simulations. Interactions with the main protease may play a key role in fighting against viruses. Luteolin is a potential antiviral molecule worthy of attention.

Predictive Binding Affinity of Plant-Derived Natural Products Towards the Protein Kinase G Enzyme of Mycobacterium tuberculosis (MtPknG)

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a growing public health concern worldwide, especially with the emerging challenge of drug resistance to the current drugs. Efforts to discover and develop novel, more effective, and safer anti-TB drugs are urgently needed. Products from natural sources, such as medicinal plants, have played an important role in traditional medicine and continue to provide some inspiring templates for the design of new drugs. Protein kinase G, produced by M. tuberculosis (MtPKnG), is a serine/threonine kinase, that has been reported to prevent phagosome-lysosome fusion and help prolong M. tuberculosis survival within the host’s macrophages. Here, we used an in silico, target-based approach (docking) to predict the interactions between MtPknG and 84 chemical constituents from two medicinal plants (Pelargonium reniforme and Pelargonium sidoides) that have a well-documented historical use as natural remedies for TB. Docking scores for ligands towards the target protein were calculated using AutoDock Vina as the predicted binding free energies. Ten flavonoids present in the aerial parts of P. reniforme and/or P.sidoides showed docking scores ranging from −11.1 to −13.2 kcal/mol. Upon calculation of all ligand efficiency indices, we observed that the (−ΔG/MW) ligand efficiency index for flavonoids (4), (5) and (7) was similar to the one obtained for the AX20017 control. When taking all compounds into account, we observed that the best (−ΔG/MW) efficiency index was obtained for coumaric acid, coumaraldehyde, p-hydroxyphenyl acetic acid and p-hydroxybenzyl alcohol. We found that methyl gallate and myricetin had ligand efficiency indices superior and equal to the AX20017 control efficiency, respectively. It remains to be seen if any of the compounds screened in this study exert an effect in M. tuberculosis-infected macrophages.

In silico evaluation of apoptogenic potential and toxicological profile of triterpenoids

AIM:

Caspases-3 and 8 are key mediators of intrinsic and extrinsic pathway of apoptosis, respectively. Triterpenoids of natural and synthetic origin reported as anticancer agents with apoptotic potential and hence may prove to be good candidates for in silico testing against caspases-3 and 8.

MATERIALS AND METHODS:

Various naturally-occurring and synthetic triterpenoids were subjected to activity prediction using PASS Online software, and among them, 67 compounds were selected for further processing. Protein structure of caspase-3 (3DEI) and caspase-8 (3KJQ) was obtained from the protein data bank and docked with selected triterpenoids using AutoDock Tools and AutoDock Vina. Toxicological profile was predicted based on clinical manifestations using PASS online software.

RESULTS:

The high docking score of -10.0, -9.9, -9.8, and -9.5 were shown by friedelin, tingenone, albiziasaponin A, and albiziasaponin C, respectively, for caspase-3, and -11.0, -9.6, -9.6, and -9.4 by β-boswellic acid, bryonolic acid, canophyllic acid, and CDDO, respectively, for caspase-8. Possible adverse events were predicted with varying degree of probability and major relevant effects were reported. Hydrostatic interactions along with formation of hydrogen bonds with specific amino acids in the binding pocket were identified with each triterpenoid.

CONCLUSION:

Lead molecules identified through this in silico study such as friedelin, tingenone, albiziasaponin, bryonolic acid, and canophyllic acid may be utilized for further in vitro/in vivo studies as apoptotic agents targeting caspases-3 and 8.

A Hybrid Cuckoo Search and Differential Evolution Approach to Protein⁻Ligand Docking

Protein⁻ligand docking is a molecular modeling technique that is used to predict the conformation of a small molecular ligand at the binding pocket of a protein receptor. There are many protein⁻ligand docking tools, among which AutoDock Vina is the most popular open-source docking software. In recent years, there have been numerous attempts to optimize the search process in AutoDock Vina by means of heuristic optimization methods, such as genetic and particle swarm optimization algorithms. This study, for the first time, explores the use of cuckoo search (CS) to solve the protein⁻ligand docking problem. The result of this study is CuckooVina, an enhanced conformational search algorithm that hybridizes cuckoo search with differential evolution (DE). Extensive tests using two benchmark datasets, PDBbind 2012 and Astex Diverse set, show that CuckooVina improves the docking performances in terms of RMSD, binding affinity, and success rate compared to Vina though it requires about 9⁻15% more time to complete a run than Vina. CuckooVina predicts more accurate docking poses with higher binding affinities than PSOVina with similar success rates. CuckooVina's slower convergence but higher accuracy suggest that it is better able to escape from local energy minima and improves the problem of premature convergence. As a summary, our results assure that the hybrid CS⁻DE process to continuously generate diverse solutions is a good strategy to maintain the proper balance between global and local exploitation required for the ligand conformational search.

Rational Drug Discovery of HCV Helicase Inhibitor: Improved Docking Accuracy with Multiple Seeding in AutoDock Vina and In Situ Minimization

BACKGROUND

Hepatitis C is a significant cause for end-stage liver diseases and liver transplantation which affects approximately 3% of the global populations. Despite the current several direct antiviral agents in the treatment of Hepatitis C, the standard treatment for HCV infection is accompanied by several drawbacks, such as adverse side effects, high pricing of medications and the rapid emerging rate of resistant HCV variants.

OBJECTIVES

To discover potential inhibitors for HCV helicase through an optimized in silico approach.

METHODS

In this study, a homology model (HCV Genotype 3 helicase) was used as the target and screened through a benzopyran-based virtual library. Multiple-seedings of AutoDock Vina and in situ minimization were to account for the non-deterministic nature of AutoDock Vina search algorithm and binding site flexibility, respectively. ADME/T and interaction analyses were also done on the top hits via FAFDRUG3 web server and Discovery Studio 4.5.

RESULTS

This study involved the development of an improved flow for virtual screening via implemention of multiple-seeding screening approach and in situ minimization. With the new docking protocol, the redocked standards have shown better RMSD value in reference to their native conformations. Ten benzopyran-like compounds with satisfactory physicochemical properties were discovered to be potential inhibitors of HCV helicase. ZINC38649350 was identified as the most potential inhibitor.

CONCLUSION

Ten potential HCV helicase inhibitors were discovered via a new docking optimization protocol with better docking accuracy. These findings could contribute to the discovery of novel HCV antivirals and serve as an alternative approach of in silico rational drug discovery.

Synthesis, characterization, molecular modelling and biological evaluation of thieno-pyrimidinone methanesulphonamide thio-derivatives as non-steroidal anti-inflammatory agents

This study reports the synthesis, molecular docking and biological evaluation of eight (5-8 and 5a-8a) newly synthesized thieno-pyrimidinone methanesulphonamide thio-derivatives. The synthetic route used to prepare the new isomers thioaryl and thio-cycloesyl derivatives of the heterocyclic system 6-phenylthieno[3,2]pyrimidinone was economically and environmentally very advantageous and characterized by the simplicity of procedure, reduction in isolation steps, purification phases, time, costs and waste production. The study in silico for the evaluation of cyclooxygenase (COX)-1 and COX-2 selective inhibition was carried out by AutoDock Vina, an open-source program for doing molecular docking which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. The research in vitro for the biological evaluation was performed by using human cartilage and chondrocytes cultures treated with 10 ng/mL of interleukin-1beta as inflammation models. The anti-inflammatory activity of each new compound at the concentration of 10 μmol/L was determined by assaying COX-2, inducible nitric oxide synthetase (iNOS) and intercellular adhesion molecule 1 (ICAM 1) through Western blot. The examined derivatives showed interesting pharmacological activity, and the compound N-[2-[2,4-difluorophenyl)thio]-4-oxo-6-phenylthieno[3,2-d]pyridine-34H-yl]methanesulphonamide (7) was excellent COX-2 inhibitor. In agreement with the biological data, compound 7 was able to fit into the active site of COX-2 with highest interaction energy. These results can support the design of novel specific inhibitors of COX-2 by the comparative modelling of COX-1 and COX-2 enzymes with the available pharmacophore.

Comparative subtractive proteomics based ranking for antibiotic targets against the dirtiest superbug: Acinetobacter baumannii

Multidrug-resistant Acinetobacter baumannii is indeed to be the most successful nosocomial pathogen responsible for myriad infections in modern health care system. Computational methodologies based on genomics and proteomics proved to be powerful tools for providing substantial information about different aspects of A. baumannii biology that made it possible to design new approaches for treating multi, extensive and total drug resistant isolates of A. baumannii. In this current approach, 35 completely annotated proteomes of A. bauamnnii were filtered through a comprehensive subtractive proteomics pipeline for broad-spectrum drug candidates. In total, 10 proteins (KdsA, KdsB, LpxA, LpxC, LpxD, GpsE, PhoB, UvrY, KdpE and OmpR) could serve as ideal candidates for designing novel antibiotics. The work was extended with KdsA enzyme for structure information, prediction of intrinsic disorders, active site details, and structure based virtual screening of library containing natural product-like scaffolds. Most of the enzyme structure has fixed three-dimensional conformation. The selection of inhibitor for KdsA enzyme was based on druglikeness, pharmacokinetics and docking scores. Compound-4636 (5-((3-chloro-5-methyl-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)methoxy)-2-(((1-hydroxy-4-methylpentan-2-yl)amino)methyl)phenol) was revealed as the most potent inhibitor against A. baumannii KdsA enzyme having Gold fitness score of 77.68 and Autodock binding energy of -6.2 kcal/mol. The inhibitor completely follows Lipinski rule of five, Ghose rule, and Egan rule. Molecular dynamics simulation for KdsA and KdsA-4636 complex was performed for 100 ns to unveil what conformational changes the enzyme underwent in the absence and presence of the inhibitor, respectively. The average root means square deviation (RMSD) for both systems was found 3.5 Å, which signifies stable structure of the enzyme in both bounded and unbounded states. Absolute binding energy using Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) reflected high affinity and vigorous interactions of the inhibitor with enzyme active residues. Findings of the current study could open up new avenues for experimentalists to design new potent antibiotics by targeting the targets screened in this study.

Synthesis, kinetics and inhibition of Escherichia coli Heptosyltransferase I by monosaccharide analogues of Lipid A

Gram-negative bacteria comprise the majority of microbes that cause infections that are resistant to pre-existing antibiotics. The complex cell wall architecture contributes to their ability to form biofilms, which are often implicated in hospital-acquired infections. Biofilms promote antibiotic resistance by enabling the bacteria to survive hostile environments such as UV radiation, pH shifts, and antibiotics. The outer membrane of Gram-negative bacteria contains lipopolysaccharide (LPS), which plays a role in adhesion to surfaces and formation of biofilms. The main focus of this work was the synthesis of a library of glycolipids designed to be simplified analogues of the Lipid A, the membrane embedded portion component of LPS, to be tested as substrates or inhibitors of Heptosyltransferase I (HepI or WaaC, a glycosyltransferase enzyme involved in the biosynthesis of LPS). Fourteen analogues were synthesized successfully and characterized. While these compounds were designed to function as nucleophilic substrates of HepI, they all demonstrated mild inhibition of HepI. Kinetic characterization of inhibition mechanism identified that the compounds exhibited uncompetitive and mixed inhibition of HepI. Since both uncompetitive and mixed inhibition result in the formation of an Enzyme-Substrate-inhibitor complex, molecular docking studies (using AutoDock Vina) were performed, to identify potential allosteric binding site for these compounds. The inhibitors were shown to bind to a pocket formed after undergoing a conformational change from an open to a closed active site state. Inhibition of HepI via an allosteric site suggest that disruption of protein dynamics might be a viable mechanism for the inhibition of HepI and potentially other enzymes of the GT-B structural class.

Bean peptides have higher in silico binding affinities than ezetimibe for the N-terminal domain of cholesterol receptor Niemann-Pick C1 Like-1

Niemann-Pick C1 like-1 (NPC1L1) mediates cholesterol absorption at the apical membrane of enterocytes through a yet unknown mechanism. Bean, pea, and lentil proteins are naturally hydrolyzed during digestion to produce peptides. The potential for pulse peptides to have high binding affinities for NPC1L1 has not been determined. In this study , in silico binding affinities and interactions were determined between the N-terminal domain of NPC1L1 and 14 pulse peptides (5≥ amino acids) derived through pepsin-pancreatin digestion. Peptides were docked in triplicate to the N-terminal domain using docking program AutoDock Vina, and results were compared to those of ezetimibe, a prescribed NPC1L1 inhibitor. Three black bean peptides (-7.2 to -7.0kcal/mol) and the cowpea bean dipeptide Lys-Asp (-7.0kcal/mol) had higher binding affinities than ezetimibe (-6.6kcal/mol) for the N-terminal domain of NPC1L1. Lentil and pea peptides studied did not have high binding affinities. The common bean peptide Tyr-Ala-Ala-Ala-Thr (-7.2kcal/mol), which can be produced from black or navy bean proteins, had the highest binding affinity. Ezetimibe and peptides with high binding affinities for the N-terminal domain are expected to interact at different locations of the N-terminal domain. All high affinity black bean peptides are expected to have van der Waals interactions with SER130, PHE136, and LEU236 and a conventional hydrogen bond with GLU238 of NPC1L1. Due to their high affinity for the N-terminal domain of NPC1L1, black and cowpea bean peptides produced in the digestive track have the potential to disrupt interactions between NPC1L1 and membrane proteins that lead to cholesterol absorption.

Natural Products as Chemopreventive Agents by Potential Inhibition of the Kinase Domain in ErbB Receptors

Small molecules found in natural products provide therapeutic benefits due to their pharmacological or biological activity, which may increase or decrease the expression of human epidermal growth factor receptor (HER), a promising target in the modification of signaling cascades involved in excessive cellular growth. In this study, in silico molecular protein-ligand docking protocols were performed with AutoDock Vina in order to evaluate the interaction of 800 natural compounds (NPs) from the NatProd Collection (http://www.msdiscovery.com/natprod.html), with four human HER family members: HER1 (PDB: 2ITW), HER2 (PDB: 3PP0), HER3 (PDB: 3LMG) and HER4 (PDB: 2R4B). The best binding affinity values (kcal/mol) for docking pairs were obtained for HER1-podototarin (−10.7), HER2-hecogenin acetate (−11.2), HER3-hesperidin (−11.5) and HER4-theaflavin (−10.7). The reliability of the theoretical calculations was evaluated employing published data on HER inhibition correlated with in silico binding calculations. IC₅₀ values followed a significant linear relationship with the theoretical binding Affinity data for HER1 (R = 0.656, p < 0.0001) and HER2 (R = 0.543, p < 0.0001), but not for HER4 (R = 0.364, p > 0.05). In short, this methodology allowed the identification of several NPs as HER inhibitors, being useful in the discovery and design of more potent and selective anticancer drugs.

Structure-Based Virtual Screening

Structure-based virtual screening (SBVS) is a computational approach used in the early-stage drug discovery campaign to search a chemical compound library for novel bioactive molecules against a certain drug target. It utilizes the three-dimensional (3D) structure of the biological target, obtained from X-ray, NMR, or computational modeling, to dock a collection of chemical compounds into the binding site and select a subset of these compounds based on the predicted binding scores for further biological evaluation. In the present work, we illustrate the basic process of conducting a SBVS with examples using freely accessible tools and resources.

Mechanisms of interaction between persistent organic pollutants (POPs) and CYP2B6: An in silico approach

Human Cytochrome P450s (CYP450) are a group of heme-containing metalloenzymes responsible for recognition and metabolism of numerous xenobiotics, including drugs and environmental contaminants. CYP2B6, a member of CYP450, is well known for being a highly inducible and polymorphic enzyme and for its important role in the oxidative metabolism of environmental pollutants, such as the Polybrominated Diphenyl Ethers (PBDEs) and Polychlorinated Biphenyls (PCBs). However the mechanisms of interaction of PBDEs and PCBs with CYP2B6 is not entirely known. In this work, a computational approach was carried out to study the interactions of 41 POPs (17 PBDEs, 17 PCBs, and 7 Dioxins) with four CYP2B6 protein structures downloaded from PDB data base (PDB: 3UA5, 3QOA, 3QU8 and 4I91) using molecular docking protocols with AutoDock Vina. The best binding affinity values (kcal/mol) were obtained for PBDE-99 (-8.5), PCB-187 (-9.6), and octachloro-dibenzo-dioxin (-9.8) that can be attributed to the hydrophobic interactions with important residues, such as Phe-363, in the catalytic site of CYP2B6. Molecular docking validation revealed the best values for PDB: 3UA5 (R = 0.622, p = 0.001) demonstrating the reliability of molecular docking predictions. The information obtained in this work can be useful in evaluating the modes of interaction of xenobiotic compounds with the catalytic site of CYP2B6 and provide insights on the important role of these enzymes in the metabolism of potentially toxic compounds in humans.

Structure of 'linkerless' hydroxamic acid inhibitor-HDAC8 complex confirms the formation of an isoform-specific subpocket

Histone deacetylases (HDACs) catalyze the hydrolysis of acetylated lysine side chains in histone and non-histone proteins, and play a critical role in the regulation of many biological processes, including cell differentiation, proliferation, senescence, and apoptosis. Aberrant HDAC activity is associated with cancer, making these enzymes important targets for drug design. In general, HDAC inhibitors (HDACi) block the proliferation of tumor cells by inducing cell differentiation, cell cycle arrest, and/or apoptosis, and comprise some of the leading therapies in cancer treatments. To date, four HDACi have been FDA approved for the treatment of cancers: suberoylanilide hydroxamic acid (SAHA, Vorinostat, Zolinza®), romidepsin (FK228, Istodax®), belinostat (Beleodaq®), and panobinostat (Farydak®). Most current inhibitors are pan-HDACi, and non-selectively target a number of HDAC isoforms. Six previously reported HDACi were rationally designed, however, to target a unique sub-pocket found only in HDAC8. While these inhibitors were indeed potent against HDAC8, and even demonstrated specificity for HDAC8 over HDACs 1 and 6, there were no structural data to confirm the mode of binding. Here we report the X-ray crystal structure of Compound 6 complexed with HDAC8 to 1.98Å resolution. We also describe the use of molecular docking studies to explore the binding interactions of the other 5 related HDACi. Our studies confirm that the HDACi induce the formation of and bind in the HDAC8-specific subpocket, offering insights into isoform-specific inhibition.

1001 Ways to run AutoDock Vina for virtual screening

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

Molecular docking and simulation of Curcumin with Geranylgeranyl Transferase1 (GGTase1) and Farnesyl Transferase (FTase)

Protein prenylation is a posttranslational modification that is indispensable for translocation of membrane GTPases like Ras, Rho, Ras etc. Proteins of Ras family undergo farnesylation by FTase while Rho family goes through geranylgeranylation by GGTase1. There is only an infinitesimal difference in signal recognition between FTase and GGTase1. FTase inhibitors mostly end up selecting the cells with mutated Ras proteins that have acquired affinity towards GGTase1 in cancer microcosms. Therefore, it is of interest to identify GGTase1 and FTase dual inhibitors using the docking tool AutoDock Vina. Docking data show that curcumin (from turmeric) has higher binding affinity to GGTase1 than that of established peptidomimetic GGTase1 inhibitors (GGTI) such as GGTI-297, GGTI-298, CHEMBL525185. Curcumin also interacts with FTase with binding energy comparable to co-crystalized compound 2-[3-(3-ethyl-1-methyl-2-oxo-azepan-3-yl)-phenoxy]-4-[1-amino-1-(1-methyl-1h-imidizol-5-yl)-ethyl]-benzonitrile (BNE). The docked complex was further simulated for 10 ns using molecular dynamics simulation for stability. Thus, the molecular basis for curcumin binding to GGTase1 and FTase is reported.

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets

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 eFindSite.

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

Docking small peptides remains a great challenge: an assessment using AutoDock Vina

There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner.