AUDocker LE: A GUI for virtual screening with AUTODOCK Vina

Computational and in vitro analyses of the antibacterial effect of the ethanolic extract of Pluchea indica L. leaves

The most common gram-negative, Escherichia coli, and gram-positive bacteria, Bacillus spp., have evolved different mechanisms that have caused the emergence of multi-drug resistance. As a result, drugs that block the bacterial growth cycle are needed. Here, in silico and in vitro studies were performed to assess compounds in the Pluchea indica leaf extract, a medicinal plant, that can inhibit bacterial proteins. Briefly, P. indica leaves were extracted using ethanol. The crude extract was then subjected to gas chromatography-mass spectrometry for metabolite screening. Molecular docking simulations with rhomboid protease (Rpro) (Protein data bank ID number: 3ZMI from E. coli and filamenting temperature-sensitive mutant Z (FtsZ) protein data bank ID number: 2VAM from Bacillus subtilis were performed. Moreover, the well diffusion method was used to confirm the antibacterial activity of P. indica leaf extract. A total of 10 compounds were identified in the P. indica extract and used for computational analysis. Based on drug-likeness prediction, P. indica compounds may be drug-like molecules. Binding affinity tests indicated that 10,10-Dimethyl-2,6-dimethylenebicyclo(7.2.0)undecan-5.β.-ol and 11,11-Dimethyl-4,8-dimethylenebicyclo(7.2.0)undecan-3-ol had the most negative values. Accordingly, these compounds may be potential ligands that bind to bacterial proteins. The root mean square fluctuation values was <2 Å, indicating stable fluctuation binding for the ligand-protein complex. According to in vitro antibacterial assays, a high concentration (50%) of the P. indica extract markedly inhibited E. coli and B. subtilis, with inhibitory zone diameters of 31.86±1.63 and 21.09±0.09 mm, respectively. Overall, the compounds in the P. indica leaf extract were identified as functional inhibitors of E. coli and B. subtilis proteins via in silico analysis. This may facilitate development of antibacterial agents.

Baicalin target protein, Annexin A2, is a target of new antitumor drugs

Baicalin is a flavonoid extracted from Scutellaria baicalensis Georgi. As it has significant antitumor and apoptosis-inducing effects, baicalin may be useful as a lead compound in new antitumor drug development. However, as the pharmacological actions of baicalin have yet to be elucidated, we isolated its target protein, which was successfully identified as Annexin A2. Annexin A2 forms a heterotetramer with S100A10 protein, which plays an important role in the plasminogen activator system. The heterotetramer bound to tissue plasminogen activator (tPA) activates the conversion of plasminogen to plasmin and promotes the expression of STAT-3 and NF-κB, which are target genes involved in the development of cancer. Moreover, NF-κB and STAT-3 induce the expression of cell inhibitors of apoptotic proteins and inhibit apoptosis. To examine whether these antitumor and apoptosis-inducing effects of baicalin are mediated by Annexin A2, we prepared Annexin A2 knockdown HepG2 cells. We compared mRNA expression by RT-qPCR and apoptosis by caspase-3 activity assays in Annexin A2 knockdown HepG2 cells. The results showed that the antitumor and apoptosis-inducing effects of baicalin are mediated by Annexin A2. The results of this study suggest that agents capable of inhibiting Annexin A2 may be useful candidates for the development of novel antitumor agents.

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.

Synthesis, in silico studies, and in vitro biological evaluation of newly-designed 5-amino-1H-tetrazole-linked 5-fluorouracil analog as a potential antigastric-cancer agent

5-Fluorouracil (5FU) is a chemotherapy drug used to treat various cancers, such as colorectal, prostate, skin, pancreas, and stomach, as an ointment or solution. However, its consumption has several side effects. Therefore, a new derivative of fluorouracil containing 5-Amino-1H-tetrazole was designed and synthesized through multi-step synthesis to reduce urea excretion and toxicity. The effectiveness of the synthesized drug on the Adenocarcinoma gastric cell line (AGS) gastric cancer cell line was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test, which showed that the new 5-fluorouracil (5FU) analog, with an IC50 of 15.67 µg/mL, is more effective in inhibiting the proliferation of AGS cells after 24 h compared to both synthesized and reported 5FU. In addition, In-silico studies showed that the new 5FU derivative based on amino tetrazole, with a binding energy of -7.2 kcal/mol, exhibits greater anti-cancer activity against the BCL2 enzyme than 5FU, with a binding energy of - 4.8 kcal/mol. It is predicted that the new 5FU derivative will be effective in treating gastric and colorectal cancers. The new derivative of the 5-fluorouracil drug was characterized and identified using FTIR and NMR spectroscopy.Communicated by Ramaswamy H. Sarma.

Exploring the antioxidant, antimicrobial, cytotoxic and biothermodynamic properties of novel morpholine derivative bioactive Mn(ii), Co(ii) and Ni(ii) complexes - combined experimental and theoretical measurements towards DNA/BSA/SARS-CoV-2 3CLPro

A novel class of bioactive complexes (1-3) [MII(L)2(bpy)], where, L = 2-(4-morpholinobenzylideneamino)phenol, bpy = 2,2'-bipyridine, MII = Mn (1), Co (2) or Ni (3), were assigned to octahedral geometry based on analytical and spectral measurements. Gel electrophoresis showed that complex (2) demonstrated significant DNA cleavage activity compared to the other complexes under the action of oxidation agent (H2O2). The DNA binding constant properties measured by various techniques were in the following sequence: (2) > (3) > (1) > (HL), which suggests that the complexes might intercalate DNA, a possibility that is also supported by their biothermodynamic characteristics. The binding constant results for BSA from electronic absorption and fluorometric titrations demonstrate that complex (2) exhibits the highest binding effectiveness among them all, which means that all the compounds could interact with BSA through a static approach, additionally supported by FRET measurements. DFT and docking calculations were employed to realize the electronic structure, reactivity, and interaction capability of all substances with DNA, BSA, and the SARS-CoV-2 main protease. These binding energies fell within the ranges -7.7 to -8.5, -8.2 to -10.1 and -6.7 to -9.3 kcal mol-1, respectively. The higher reactivity of the complexes than the ligand is supported by FMO theory. The in vitro antibacterial, cytotoxicity, and radical scavenging characteristics revealed that complexes (2-3) have better biological efficacy than the others. The cytotoxicity and binding properties also show good correlation with the partition coefficient (log P), which is encouraging because all of the experimental findings are closely correlated with the theoretical measurements.

Isolation and identification of the new baicalin target protein to develop flavonoid structure-based therapeutic agents

Proteins are vital constituents of all living organisms. As many therapeutic agents alter the activity of functional proteins, identifying functional target proteins of small bioactive molecules isessential for the rational design of stronger medicines. Flavonoids with antioxidant, anti-allergy, and anti-inflammatory effects are expected to have preventive effects for several diseases closely related to oxidation and inflammation, including heart disease, cancer, neurodegenerative disorders, and eye diseases. Therefore, identifying the proteins involved in the pharmacological actions of flavonoids, and designing a flavonoid structure-based medicine that strongly and specifically inhibits flavonoid target proteins, could aid the development of more effective medicines for treating heart disease, cancer, neurodegenerative disorders, and ocular diseases with few side effects. To isolate the flavonoid target protein, we conducted a novel affinity chromatography in a column wherein baicalin, a representative flavonoid, was attached to Affi-Gel 102. Through affinity chromatography and nano LC-MS/MS, we identified GAPDH as a flavonoid target protein. Then, we performed fluorescence quenching and an enzyme inhibition assay to experimentally confirmbaicalin's binding affinity for, and inhibition of, GAPDH. We also conducted in silico docking simulations to visualize the binding modes of baicalin and the newly identified flavonoid target protein, GAPDH. From the results of this study, it was considered that one of the reasons why baicalin exhibits the effects on cancer and neurodegenerative diseases is that it inhibits the activity of GAPDH. In summary, we showed that Affi-Gel102 could quickly and accurately isolate the target protein for bioactive small molecules, without the need for isotopic labeling or a fluorescent probe. By using the method presented here, it was possible to easily isolate the target protein of a medicine containing a carboxylic acid.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

Korupensamine A, but not its atropisomer, korupensamine B, inhibits SARS-CoV-2 in vitro by targeting its main protease (Mpro)

By combining docking and molecular dynamics simulations, we explored a library of 65 mostly axially chiral naphthylisoquinoline alkaloids and their analogues, with most different molecular architectures and structural analogues, for their activity against SARS-CoV-2. Although natural biaryls are often regarded without consideration of their axial chirality, they can bind to protein targets in an atroposelective manner. By combining docking results with steered molecular dynamics simulations, we identified one alkaloid, korupensamine A, that atropisomer-specifically inhibited the main protease (M^pro) activity of SARS-CoV-2 significantly in comparison to the reference covalent inhibitor GC376 (IC₅₀ = 2.52 ± 0.14 and 0.88 ± 0.15 μM, respectively) and reduced viral growth by five orders of magnitude in vitro (EC₅₀ = 4.23 ± 1.31 μM). To investigate the binding pathway and mode of interaction of korupensamine A within the active site of the protease, we utilized Gaussian accelerated molecular dynamics simulations, which reproduced the docking pose of korupensamine A inside the active site of the enzyme. The study presents naphthylisoquinoline alkaloids as a new class of potential anti-COVID-19 agents.

An Integrated Analysis of Mechanistic Insights into Biomolecular Interactions and Molecular Dynamics of Bio-Inspired Cu(II) and Zn(II) Complexes towards DNA/BSA/SARS-CoV-2 3CLpro by Molecular Docking-Based Virtual Screening and FRET Detection

Novel constructed bioactive mixed-ligand complexes (1b) [Cu^II(L)₂(phen)] and (2b) [Zn^II(L)₂(phen)] {where, L = 2-(4-morpholinobenzylideneamino)phenol), phen = 1,10-phenanthroline} have been structurally analysed by various analytical and spectroscopic techniques, including, magnetic moments, thermogravimetric analysis, and X-ray crystallography. Various analytical and spectral measurements assigned showed that all complexes appear to have an octahedral geometry. Agar gel electrophoresis's output demonstrated that the Cu(II) complex (1b) had efficient deoxyribonucleic cleavage and complex (2b) demonstrated the partial cleavage accomplished with an oxidation agent, which generates spreadable OH^● through the Fenton type mechanism. The DNA binding constants observed from viscosity, UV-Vis spectral, fluorometric, and electrochemical titrations were in the following sequence: (1b) > (2b) > (HL), which suggests that the complexes (1b-2b) might intercalate DNA, a possibility that is supported by the biothermodynamic measurements. In addition, the observed binding constant results of BSA by electronic absorption and fluorometric titrations indicate that complex (1b) revealed the best binding efficacy as compared to complex (2b) and free ligand. Interestingly, all compounds are found to interact with BSA through a static approach, as further attested by FRET detection. The DFT and molecular docking calculations were also performed to realize the electronic structure, reactivity, and binding capability of all test samples with CT-DNA, BSA, and the SARS-CoV-2 3CL^Pro, which revealed the binding energies were in a range of -8.1 to -8.9, -7.5 to -10.5 and -6.7--8.8 kcal/mol, respectively. The higher reactivity of the complexes than the free ligand is supported by the FMO theory. Among all the observed data for antioxidant properties against DPPH^᛫, ^᛫OH, O₂^-• and NO^᛫ free radicals, complex (1a) had the best biological efficacy. The antimicrobial and cytotoxic characteristics of all test compounds have been studied by screening against certain selected microorganisms as well as against A549, HepG2, MCF-7, and NHDF cell lines, respectively. The observed findings revealed that the activity enhances coordination as compared to free ligand via Overtone's and Tweedy's chelation mechanisms. This is especially encouraging given that in every case, the experimental findings and theoretical detections were in perfect accord.

Comprehensive Assessment of Biomolecular Interactions of Morpholine-Based Mixed Ligand Cu(II) and Zn(II) Complexes of 2,2'-Bipyridine as Potential Anticancer and SARS-CoV-2 Agents: A Synergistic Experimental and Structure-Based Virtual Screening

A new class of pharmacologically active mixed-ligand complexes (1a-2a) [M^II(L)₂ (bpy)], where L = 2-(4-morpholinobenzylideneamino)phenol), bpy = 2,2'-bipyridine, M^II = Cu (1a), and Zn (2a), were assigned an octahedral geometry by analytical and spectral measurements. Gel electrophoresis showed that complex (1a) demonstrated the complete DNA cleavage mediated by H₂O₂. The overall DNA-binding constants observed from UV-vis, fluorometric, hydrodynamic, and electrochemical titrations were in the following sequence: (1a) > (2a) > (HL), which suggests that the complexes might intercalate DNA, a possibility that is further supported by the biothermodynamic characteristics. The binding constant results of BSA by electronic absorption and fluorometric titration demonstrate that complex (1a) exhibits the highest binding effectiveness among others, which means that all compounds could interact with BSA through a static approach, additionally supported by FRET measurements. Density FunctionalTheory (DFT) and molecular docking calculations were relied on to unveil the electronic structure, reactivity, and interacting capability of all substances with DNA, BSA, and SARS-CoV-2 main protease (Mpro). These observed binding energies fell within the following ranges: -7.7 to -8.6, -7.2 to -10.2, and -6.7 to -8.2 kcal/mol, respectively. The higher reactivity of the complexes compared to free ligand is supported by the Frontier MolecularOrbital (FMO) theory. The in vitro antibacterial, cytotoxic, and radical scavenging characteristics revealed that complex (1a) has the best biological efficacy compared to others. This is encouraged because all experimental findings are closely correlated with the theoretical measurements.

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.

Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery

We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users' local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps-which run calculations "in the cloud" rather than on users' local computers-browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software.

In Silico Drug Discovery Strategies Identified ADMET Properties of Decoquinate RMB041 and Its Potential Drug Targets against Mycobacterium tuberculosis

The highly adaptive cellular response of Mycobacterium tuberculosis to various antibiotics and the high costs for clinical trials, hampers the development of novel antimicrobial agents with improved efficacy and safety. Subsequently, in silico drug screening methods are more commonly being used for the discovery and development of drugs, and have been proven useful for predicting the pharmacokinetics, toxicities, and targets, of prospective new antimicrobial agents. In this investigation we used a reversed target fishing approach to determine potential hit targets and their possible interactions between M. tuberculosis and decoquinate RMB041, a propitious new antituberculosis compound. Two of the 13 identified targets, Cyp130 and BlaI, were strongly proposed as optimal drug-targets for dormant M. tuberculosis, of which the first showed the highest comparative binding affinity to decoquinate RMB041. The metabolic pathways associated with the selected target proteins were compared to previously published molecular mechanisms of decoquinate RMB041 against M. tuberculosis, whereby we confirmed disrupted metabolism of proteins, cell wall components, and DNA. We also described the steps within these pathways that are inhibited and elaborated on decoquinate RMB041’s activity against dormant M. tuberculosis. This compound has previously showed promising in vitro safety and good oral bioavailability, which were both supported by this in silico study. The pharmacokinetic properties and toxicity of this compound were predicted and investigated using the online tools pkCSM and SwissADME, and Discovery Studio software, which furthermore supports previous safety and bioavailability characteristics of decoquinate RMB041 for use as an antimycobacterial medication.

IMPORTANCE This article elaborates on the mechanism of action of a novel antibiotic compound against both, active and dormant Mycobacterium tuberculosis and describes its pharmacokinetics (including oral bioavailability and toxicity). Information provided in this article serves useful during the search for drugs that shorten the treatment regimen for Tuberculosis and cause minimal adverse effects.

Potential Anti-Mycobacterium tuberculosis Activity of Plant Secondary Metabolites: Insight with Molecular Docking Interactions

Tuberculosis (TB) is a recurrent and progressive disease, with high mortality rates worldwide. The drug-resistance phenomenon of Mycobacterium tuberculosis is a major obstruction of allelopathy treatment. An adverse side effect of allelopathic treatment is that it causes serious health complications. The search for suitable alternatives of conventional regimens is needed, i.e., by considering medicinal plant secondary metabolites to explore anti-TB drugs, targeting the action site of M. tuberculosis. Nowadays, plant-derived secondary metabolites are widely known for their beneficial uses, i.e., as antioxidants, antimicrobial agents, and in the treatment of a wide range of chronic human diseases (e.g., tuberculosis), and are known to “thwart” disease virulence. In this regard, in silico studies can reveal the inhibitory potential of plant-derived secondary metabolites against Mycobacterium at the very early stage of infection. Computational approaches based on different algorithms could play a significant role in screening plant metabolites against disease virulence of tuberculosis for drug designing.

Biology-oriented drug synthesis and evaluation of secnidazole esters as novel enzyme ınhibitors

The identification of novel compounds that can inhibit physiologically and metabolically important drug targets or enzymes has prime importance in medicinal chemistry. With this aim, a range of secnidazole esters 1-30 were synthesized under the heading of biology-oriented drug synthesis by the 1,1'-carbonyldiimidazole-mediated coupling reaction between secnidazole and varyingly benzoic acid derivatives. All compounds were screened for inhibitory activity against human carbonic anhydrase (hCA) I and II, acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and α-glucosidase. The results indicate that all the synthesized compounds showed potent inhibitory activities against all targets, as compared to the standard inhibitors, revealed by IC₅₀ values. K_i values of the secnidazole derivatives 1-30 for hCA I, hCA II, AChE, BChE, and α-glucosidase enzymes were obtained in the ranges of 47.37-190.74, 44.38-198.21, 12.14-68.37, 8.04-61.53, and 7.78-45.91 nM, respectively. To assess the enzyme-ligand interactions, the optimized most active compounds 2, 3, 8, 9, 14, 17, and 23 were subjected to molecular docking studies with modeled AChE, BChE, hCA I, hCA II, and α-glucosidase enzymes, where several important and key interactions were monitored with amino acid residues of each target enzyme.

Molecular Docking, G-QSAR Studies, Synthesis and Anticancer Screening of Some New 2-Phenazinamines as Bcr-Abl Tyrosine Kinase Inhibitors

BACKGROUND

The computational studies on 2-phenazinamines with their protein targets have been carried out to design compounds with potential anticancer activity. This strategy of designing compounds possessing selectivity over specific tyrosine kinase has been achieved through G-QSAR and molecular docking studies.

METHODS

The objective of this research has been to design newer 2-phenazinamine derivatives as Bcr-Abl tyrosine kinase inhibitors by G-QSAR, molecular docking studies followed by wet lab studies along with evaluation of their anticancer potential. Computational chemistry was done by using VLife MDS 4.3 and Autodock 4.2 followed by wet lab experiments for synthesizing 2- phenazinamine derivatives. The chemical structures of ligands in 2D were drawn by employing Chemdraw 2D Ultra 8.0 and were converted into 3D. These were optimised by using semiempirical method called MOPAC. The protein structure was retrieved from RCSC protein data bank as PDB file. The binding interactions of protein and ligands were done by using PYMOL. The molecular properties of the designed compounds were predicted in silico by using Osiris property explorer. Later, we synthesized novel 13 2-phenazinamine derivatives by treating parent compound with various aldehydes in the presence of dicyclohexylcarbodiimide (DCC) and urea to afford 2-(2-chlorophenyl)-3-(phenazin-2-yl) thiazolidin-4-one and another series of derivatives synthesized with different aldehydes in the presence of p-toluylsulphonic acid, diphydropyridine and benzene sulfonyl chloride to afford benzenesulfonyl-N-(2-chlorobenzyl)-phenazin-2-amine. All the derivatives were tested for invitro anticancer activity on K562 human chronic myelogenous leukemia cell line by employing MTT assay method.

RESULTS

The developed G-QSAR models were found to be statistically significant with respect to training (r2=0.8074), cross-validation (q2=0.6521), and external validation (pred_r2=0.5892). The best developed G-QSAR model suggested that the XlogP values of phenazinamine derivatives were found to be highly influential in determining biological activity. The standard drug was found to exhibit binding energy - 6.79 kcal/mol and the derivatives 5b and 6c exhibited binding energy of - 7.46 and - 8.51; respectively.

CONCLUSION

Compounds 5b, 6c were observed to possess good lipophilicity and were found to exhibit better activity than other compounds in the series, although less than standard doxorubicin. The synthesis of these 2-phenazinamine derivatives (5a-m) is reported to be obtained from 2,4- dinitrodiphenylamine by applying appropriate synthetic route. Compounds 5b and 6c showed better cytotoxic activity against K562 cancer cell line when compared to other compounds of the series, although less than standard doxorubicin.

Treasuring the computational approach in medicinal plant research

Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.

Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser

MOTIVATION

Molecular docking is a computational technique for predicting how a small molecule might bind a macromolecular target. Among docking programs, AutoDock Vina is particularly popular. Like many docking programs, Vina requires users to download/install an executable file and to run that file from a command-line interface. Choosing proper configuration parameters and analyzing Vina output is also sometimes challenging. These issues are particularly problematic for students and novice researchers.

RESULTS

We created Webina, a new version of Vina, to address these challenges. Webina runs Vina entirely in a web browser, so users need only visit a Webina-enabled webpage. The docking calculations take place on the user's own computer rather than a remote server.

AVAILABILITY AND IMPLEMENTATION

A working version of the open-source Webina app can be accessed free of charge from http://durrantlab.com/webina.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DockCoV2: a drug database against SARS-CoV-2

The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.

Biophysical investigation of interactions between sorbic acid and human serum albumin through spectroscopic and computational approaches

This work provides an effective strategy to analyze the SA-induced microenvironmental changes in the HSA macromolecule, and also highlights the medicinal importance of SA.

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.

Understanding the structural insights of enzymatic conformations for adenylosuccinate lyase receptor in malarial parasite Plasmodium falciparum

The dreadful disease malaria is one among the infectious diseases that comes in third number after the tuberculosis and HIV. This disease is spread by female Anopheles mosquito and caused by the malarial parasite sp notably Plasmodium falciparum. In this, the organism has several enzymes for processing the infection and growth mechanism and among that, the adenylosuccinate lyase is an enzyme that plays a critical role in metabolism and cellular replication via its action in the de novo purine biosynthetic pathway. Adenylosuccinate has been studied for two reaction mechanisms, and in that, the adenylosuccinate to AMP and fumarate is core important. As of now, there have been several studies indicating the reaction mechanism of adenylosuccinate lyase, this study projects the conformations of the reactant and product changes through molecular docking and molecular dynamic simulations. Adenylosuccinate bound complex involves His role in the product than the reactant complex, and the complex shows high flexibility due to fumarate. Thus, identifying the core inhibitor that binds to His rings could be a standard adenylosuccinate lyase inhibitor, that can block the malarial diseases in humans. In addition to the competitive inhibition site, we also predicted the uncompetitive ligand binding site, which suggest the alternate region to be targeted. Thus, from this work, we suggest both competitive and uncompetitive binding regions for the purpose identifying the malarial inhibitors.

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.

Identification of a C2-symmetric diol based human immunodeficiency virus protease inhibitor targeting Zika virus NS2B-NS3 protease

Zika virus (ZIKV) is an emerging mosquito-borne flavivirus and infection by ZIKV Asian lineage is known to cause fetal brain anomalies and Guillain-Barrés syndrome. The WHO declared ZIKV a global public health emergency in 2016. However, currently neither vaccines nor antiviral prophylaxis/treatments are available. In this study, we report the identification of a C2-symmetric diol-based Human immunodeficiency virus type-1 (HIV) protease inhibitor active against ZIKV NS2B-NS3 protease. The compound, referred to as 9b, was identified by in silico screening of a library of 6265 protease inhibitors. Molecular dynamics (MD) simulation studies revealed that compound 9b formed a stable complex with ZIKV protease. Interaction analysis of compound 9b's binding pose from the cluster analysis of MD simulations trajectories predicted that 9b mostly interacted with ZIKV NS3. Although designed as an aspartyl protease inhibitor, compound 9b was found to inhibit ZIKV serine protease in vitro with IC₅₀ = 143.25 ± 5.45 µM, in line with the in silico results. Additionally, linear interaction energy method (LIE) was used to estimate binding affinities of compounds 9b and 86 (a known panflavivirus peptide hybrid with IC₅₀ = 1.64 ± 0.015 µM against ZIKV protease). The LIE method correctly predicted the binding affinity of compound 86 to be lower than that of 9b, proving to be superior to the molecular docking methods in scoring and ranking compounds. Since most of the reported ZIKV protease inhibitors are positively charged peptide-hybrids, with our without electrophilic warheads, compound 9b represents a less polar and more drug-like non-peptide hit compound useful for further optimization.Communicated by Ramaswamy Sarma.

FeptideDB: A web application for new bioactive peptides from food protein

BACKGROUND

Bioactive peptides derived from food are important sources for alternative medicine and possess therapeutic activity. Several biochemical methods have been achieved to isolate bioactive peptides from food, which are tedious and time consuming. In silico methods are an alternative process to reduce cost and time with respect to bioactive peptide production. In this paper, FeptideDB was used to collect bioactive peptide (BP) data from both published research articles and available bioactive peptide databases. FeptideDB was developed to assist in forecasting bioactive peptides from food by combining peptide cleavage tools and database matching. Furthermore, this application was able to predict the potential of cleaved peptides from 'enzyme digestion module' to identify new ACE (angiotensin converting enzyme) inhibitors using an automatic molecular docking approach.

RESULTS

The FeptideDB web application contains tools for generating all possible peptides cleaved from input protein by various available enzymes. This database was also used for analysis and visualization to assist in bioactive peptide discovery. One module of FeptideDB has the ability to create 3-dimensional peptide structures to further predict inhibitors for the target protein, ACE (angiotensin converting enzyme).

CONCLUSIONS

FeptideDB is freely available to researchers who are interested in exploring bioactive peptides. The FeptideDB interface is easy to use, allowing users to rapidly retrieve data based on desired search criteria. FeptideDB is freely available at http://www4g.biotec.or.th/FeptideDB/. Ultimately, FeptideDB is a computational aid for assessing peptide bioactivities.

POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening

High throughput virtual screening plays a crucial role in hit identification during the drug discovery process. With the rapid increase in the chemical libraries, virtual screening process becomes computationally challenging, thereby posing a demand for efficiently parallelized software pipelines. Here we present a GNU Parallel based pipeline-POAP that is programmed to run Open Babel and AutoDock suite under highly optimized parallelization. The ligand preparation module is a unique feature in POAP, as it offers extensive options for geometry optimization, conformer generation, parallelization and also quarantines erroneous datasets for seamless operation. POAP also features multi receptor docking that can be utilized for comparative virtual screening and drug repurposing studies. As demonstrated using different structural datasets, POAP proves to be an efficient pipeline that enables high scalability, seamless operability, dynamic file handling and optimal utilization of CPU's for computationally demanding tasks. POAP is distributed freely under GNU GPL license and can be downloaded at https://github.com/inpacdb/POAP.

Substrate Recognition Mechanism of the Putative Yeast Carnosine N-methyltransferase

Anserine (β-alanyl-N(P_i)-methyl-l-histidine) is a natural metabolite present in skeletal muscle and the central nervous system of vertebrates and plays important physiological roles in living organisms. The production of anserine is catalyzed by carnosine N-methyltransferases, which transfer a methyl group to carnosine (β-alanyl-l-histidine). However, the structural basis of the substrate recognition for the enzymes is unknown. We present the crystal structure of the putative carnosine N-methyltransferase from yeast named YNL092W in complex with SAH, solved by the single-wavelength anomalous dispersion (SAD) method. The protein contains a typical Rossmann domain and a characteristic N-terminal helical domain. At the cofactor-binding site, SAH forms an extensive interaction network with the enzyme. The individual contribution of each residue to ligand affinity and enzyme activity was assessed by ITC and methyltransferase assays after mutagenesis of the key residues. Additionally, docking studies and activity assays were conducted in order to identify the binding site for carnosine, and a plausible complex model was proposed. Furthermore, we discovered that two disulfide bridges might be functionally important to the enzyme. By comparison to structure- and sequence-similar methyltransferases, we deduce that the enzyme most likely acts on a protein substrate. Our structural analyses shed light on the catalytic mechanism and substrate recognition by YNL092W.

Tailored-pharmacophore model to enhance virtual screening and drug discovery: a case study on the identification of potential inhibitors against drug-resistant Mycobacterium tuberculosis (3R)-hydroxyacyl-ACP dehydratases

Aim: Virtual screening (VS) is powerful tool in discovering molecular inhibitors that are most likely to bind to drug targets of interest. Herein, we introduce a novel VS approach, so-called ‘tailored-pharmacophore’, in order to explore inhibitors that overcome drug resistance. Methodology & results: The emergence and spread of drug resistance strains of tuberculosis is one of the most critical issues in healthcare. A tailored-pharmacophore approach was found promising to identify in silico predicted hit with better binding affinities in case of the resistance mutations in MtbHadAB as compared with thiacetazone, a prodrug used in the clinical treatment of tuberculosis. Conclusion: This approach can potentially be enforced for the discovery and design of drugs against a wide range of resistance targets.

Structure-based modelling, scoring, screening, and in vitro kinase assay of anesthetic pkc inhibitors against a natural medicine library

Protein kinase C (PKC) is an intracellular effector of the inositol phosphate-mediated signal transduction pathway. Evidence is emerging that certain general anaesthetics can influence the activity of PKC by interacting with the regulatory domain of the enzyme, and targeting PKC kinase domain is considered as a strategy to modulate the anaesthetic effects. Here, an integrated method was used to perform virtual screening against a large library of natural compounds for the discovery of new and potent PKC modulators. A number of hits were identified and their inhibitory activity against PKC kinase domain was measured by using a standard kinase assay protocol. Three and five compounds were determined to have high and moderate activities with IC₅₀ values at nanomolar and micromolar levels, respectively. These compounds can be considered as promising lead molecular entities to develop efficacious anaesthetic modulators. Structural examination revealed a variety of nonbonded interactions such as hydrogen bonds, cation-π contacts, and hydrophobic forces across the complex interface of PKC with the identified compounds. This study helps to establish an integrative approach to rational kinase inhibitor discovery by efficiently exploiting various existing natural products.

Molecular mechanism of tobramycin with human serum albumin for probing binding interactions: multi-spectroscopic and computational approaches

Highlighting novelty: comprehensive in vitro and in silico insights for understanding the novel binding site of TOB with HSA.

StemCellCKB: An Integrated Stem Cell-Specific Chemogenomics KnowledgeBase for Target Identification and Systems-Pharmacology Research

Given the capacity of self-renewal and multilineage differentiation, stem cells are promising sources for use in regenerative medicines as well as in the clinical treatment of certain hematological malignancies and degenerative diseases. Complex networks of cellular signaling pathways largely determine stem cell fate and function. Small molecules that modulate these pathways can provide important biological and pharmacological insights. However, it is still challenging to identify the specific protein targets of these compounds, to explore the changes in stem cell phenotypes induced by compound treatment and to ascertain compound mechanisms of action. To facilitate stem cell related small molecule study and provide a better understanding of the associated signaling pathways, we have constructed a comprehensive domain-specific chemogenomics resource, called StemCellCKB ( http://www.cbligand.org/StemCellCKB/ ). This new cloud-computing platform describes the chemical molecules, genes, proteins, and signaling pathways implicated in stem cell regulation. StemCellCKB is also implemented with web applications designed specifically to aid in the identification of stem cell relevant protein targets, including TargetHunter, a machine-learning algorithm for predicting small molecule targets based on molecular fingerprints, and HTDocking, a high-throughput docking module for target prediction and systems-pharmacology analyses. We have systematically tested StemCellCKB to verify data integrity. Target-prediction accuracy has also been validated against the reported known target/compound associations. This proof-of-concept example demonstrates that StemCellCKB can (1) accurately predict the macromolecular targets of existing stem cell modulators and (2) identify novel small molecules capable of probing stem cell signaling mechanisms, for use in systems-pharmacology studies. StemCellCKB facilitates the exploration and exchange of stem cell chemogenomics data among members of the broader research community.

Cardiovascular Disease Chemogenomics Knowledgebase-guided Target Identification and Drug Synergy Mechanism Study of an Herbal Formula

Combination therapy is a popular treatment for various diseases in the clinic. Among the successful cases, Traditional Chinese Medicinal (TCM) formulae can achieve synergistic effects in therapeutics and antagonistic effects in toxicity. However, characterizing the underlying molecular synergisms for the combination of drugs remains a challenging task due to high experimental expenses and complication of multicomponent herbal medicines. To understand the rationale of combination therapy, we investigated Sini Decoction, a well-known TCM consisting of three herbs, as a model. We applied our established diseases-specific chemogenomics databases and our systems pharmacology approach TargetHunter to explore synergistic mechanisms of Sini Decoction in the treatment of cardiovascular diseases. (1) We constructed a cardiovascular diseases-specific chemogenomics database, including drugs, target proteins, chemicals, and associated pathways. (2) Using our implemented chemoinformatics tools, we mapped out the interaction networks between active ingredients of Sini Decoction and their targets. (3) We also in silico predicted and experimentally confirmed that the side effects can be alleviated by the combination of the components. Overall, our results demonstrated that our cardiovascular disease-specific database was successfully applied for systems pharmacology analysis of a complicated herbal formula in predicting molecular synergetic mechanisms, and led to better understanding of a combinational therapy.

Targeting druggable enzymome by exploiting natural medicines: An in silico-in vitro integrated approach to combating multidrug resistance in bacterial infection

CONTEXT

Antibiotic resistance is a major clinical and public health problem. Development of new therapeutic approaches to prevent bacterial multidrug resistance during antimicrobial chemotherapy has thus been becoming a primary consideration in the medicinal chemistry community.

OBJECTIVE

We described a new strategy that combats multidrug resistance by using natural medicines to target the druggable enzymome (i.e., enzymatic proteome) of Staphylococcus aureus.

MATERIALS AND METHODS

A pipeline of integrating in silico analysis and in vitro assay was purposed to identify antibacterial agents from a large library of natural products with diverse structures, high drug-likeness, and relatively low flexibility, with which a systematic interactome of 826 natural product candidates with 125 functionally essential S. aureus enzymes was constructed via a high-throughput cross-docking approach. The obtained docking score matrix was then converted into an array of synthetic scores; each corresponds to a natural product candidate. By systematically examining the docking results, a number of highly promising candidates with potent antibacterial activity were suggested.

RESULTS

Three natural products, i.e., radicicol, jorumycin, and amygdalin, have been determined to possess strong broad-spectrum potency combating both the drug-resistant and drug-sensitive strains (MIC value <10 μg/ml). In addition, some natural products such as tetrandrine, bilobalide, and arbutin exhibited selective inhibition on different strains.

DISCUSSION AND CONCLUSION

Combined quantum mechanics/molecular mechanics analysis revealed diverse non-bonded interactions across the complex interfaces of newly identified antibacterial agents with their putative targets GyrB ATPase and tyrosyl-tRNA synthetase.

Structure-based functional annotation of hypothetical proteins from Candida dubliniensis: a quest for potential drug targets

Candida dubliniensis is an emerging pathogenic yeast in humans and infections are usually restricted to mucosal parts of the body. However, its presence in specimens of immunocompromised individuals, especially in HIV-positive patients, is of major medical concern. There is a large fraction of genomes of C. dubliniensis in the database which are uncharacterized for their biochemical, biophysical, and/or cellular functions, and are identified as hypothetical proteins (HPs). Function annotation of Candida genome is, therefore, essentially required to facilitate the understanding of mechanisms of pathogenesis and biochemical pathways important for selecting novel therapeutic target. Here, we carried out an extensive analysis to explain the functional properties of genome, using available protein structure and function analysis tools. We successfully modeled the structures of eight HPs for which a template with moderate sequence similarity was available in the protein data bank. All modeled structures were analyzed and we found that these proteins may act as transporter, kinase, transferase, ketosteroid, isomerase, hydrolase, oxidoreductase, and binding targets for DNA and RNA. Since these unique HPs of Candida showed no homologs in humans, these proteins are expected to be a potential target for future antifungal therapy.

Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension

CONTEXT

Pulmonary hypertension (PH) is a devastating disease characterized by progressive elevation of pulmonary arterial pressure and vascular resistance due to pulmonary vasoconstriction and vessel remodeling. The activation of RhoA/Rho-kinase (ROCK) pathway plays a central role in the pathologic progression of PH and thus the Rho kinase, an essential effector of the ROCK pathway, is considered as a potential therapeutic target to attenuate PH.

OBJECTIVE

In the current study, a synthetic pipeline is used to discover new potent Rho inhibitors from various natural products.

MATERIALS AND METHODS

In the pipeline, the stepwise high-throughput virtual screening, quantitative structure-activity relationship (QSAR)-based rescoring, and kinase assay were integrated. The screening was performed against a structurally diverse, drug-like natural product library, from which six identified compounds were tested to determine their inhibitory potencies agonist Rho by using a standard kinase assay protocol.

RESULTS

With this scheme, we successfully identified two potent Rho inhibitors, namely phloretin and baicalein, with activity values of IC50 = 0.22 and 0.95 μM, respectively.

DISCUSSION AND CONCLUSION

Structural examination suggested that complicated networks of non-bonded interactions such as hydrogen bonding, hydrophobic forces, and van der Waals contacts across the complex interfaces of Rho kinase are formed with the screened compounds.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

istar: a web platform for large-scale protein-ligand docking

Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson's correlation coefficient and Spearman's correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at http://istar.cse.cuhk.edu.hk/idock.

(1→3)-β-D-Glucan reduces the damages caused by reactive oxygen species induced in human platelets by lipopolysaccharides

LPS (lipopolysaccharide) induces platelet activation and is a well-known fundamental agent of septic shock and disseminated intravascular coagulation (DIC). Biological activity of (1→3)-β-D-glucan is related due to its anti-inflammatory, antioxidant, and antitumor properties. We focus our attention on the (1→3)-β-D-glucan (antiplatelet) properties. The main purpose of our study was to evaluate the influence of (1→3)-β-D-glucan from Saccharomyces cerevisiae on destructive activity of LPS (from Escherichia coli and Pseudomonas aeruginosa) on human blood platelets. We assess biochemically in vitro if (1→3)-β-D-glucan might combat the oxidative stress caused by LPS stroke associated with nitrative and oxidative damages of human platelet biomolecules. We also make an attempt by in silico molecular docking to determine the interactions between the molecules of (1→3)-β-D-glucan and LPS. Our conclusion is that protective mechanism of (1→3)-β-D-glucan against LPS action on blood platelets is due to as well: its antioxidant properties, as to its interaction with LPS-binding region of TLR4-MD-2 complex.