Virtual screening of eighteen million compounds against dengue virus: Combined molecular docking and molecular dynamics simulations study

Evaluation of the inhibitory potency of anti-dengue phytocompounds against DENV-2 NS2B-NS3 protease: virtual screening, ADMET profiling and molecular dynamics simulation investigations

Dengue fever has been a worldwide concern, with 50-100 million new infections each year mainly due to five different serotypes of the Dengue virus (DENV). Designing a perfect anti-dengue agent that can inhibit all the serotypes by distinguishing antigenic differences is quite difficult. Previous anti-dengue researches have included chemical compounds screening against DENV enzymes. The ongoing analysis is meant for investigation of the plant-based compounds as antagonistic to DENV-2 focusing on the specific NS2B-NS3Pro target, a trypsin like serine protease that cuts the DENV polyprotein into separate proteins crucial for viral reproduction. Initially, a virtual library of more than 130 phytocompounds was prepared from previously published reports of plants with anti-dengue properties, which were then virtually screened and shortlisted against the WT, H51N and S135A mutant of DENV-2 NS2B-NS3Pro. The three top-most compounds were viewed as Gallocatechin (GAL), Flavokawain-C (FLV), and Isorhamnetin (ISO) showing docking scores of -5.8, -5.7, -5.7 kcal/mol for WT, -7.5, -6.8, -7.6 kcal/mol for the H51N, and -6.9, -6.5, -6.1 kcal/mol for the S135A mutant protease, respectively. 100 ns long MD simulations and MM-GBSA based free energy calculations were performed on the NS2B-NS3Pro complexes to witness the relative binding affinity of the compounds and favourable molecular interactions network. A comprehensive analysis of the study reveals some promising outcomes with ISO as the topmost compound with favourable pharmacokinetic properties for the WT and mutants (H51N and S135A) as well, suggesting as a novel anti-NS2B-NS3Pro agent with better adapting characters in both the mutants.Communicated by Ramaswamy H. Sarma.

Investigating Lycotoxin-An1a (An1a), a defense antiviral peptide from Alopecosa nagpag venom as prospective anti-dengue agent against DENV-2 NS2B-NS3 protease

Dengue fever is a global health concern with no effective therapy. Screening synthetic chemicals, animal-originated compounds, and phytocompounds against Dengue virus (DENV) targets has failed to find dengue antivirals. The current study examines animal drugs as antagonists against NS2B-NS3Pro, one of DENV's most promising therapeutic targets for dengue fever. Antiviral-Lycotoxin-An1a (An1a), a defence antiviral peptide isolated from the venom of Alopecosa nagpag, a toxic spider. Based on prior in vitro research, it was discovered that the venom peptide suppresses the action of DENV-2 NS2B-NS3Pro. An1a peptide with NS2B-NS3Pro wild type (WT) and two mutants (H51N and S135A) was tested for anti-dengue characteristics using in silico analysis. The WT NS2B-NS3Pro has a catalytic triad of His51, Asp75, and Ser135 in the active site, but the mutants have N51 instead of His51 and Ala135 instead of Ser135. The dynamic sites of the three proteases (WT, H51N, S135A) and the peptide toxin (An1a) were taken into account to achieve molecular docking of An1a with WT NS2B-NS3Pro in conjunction with H51N and S135A. Cluspro-2 performs rigid-flexible docking to predict peptide binding affinity, effectiveness, and inhibitory consistency. Since the ligand had a higher binding affinity, docking score, and molecular interaction network, MD simulations and MM-GBSA free energy calculations were used to investigate the stability of the three protein-peptide complexes. The computer-aided screening and manufacture of spider venom-based anti-dengue medicines yielded intriguing results in the preliminary studies. This study is significant in defining the ideal therapeutic candidate against dengue infections.

Molecular docking, molecular dynamics simulation, and MM/PBSA analysis of ginger phytocompounds as a potential inhibitor of AcrB for treating multidrug-resistant Klebsiella pneumoniae infections

The emergence of Multidrug resistance (MDR) in human pathogens has defected the existing antibiotics and compelled us to understand more about the basic science behind alternate anti-infective drug discovery. Soon, proteome analysis identified AcrB efflux pump protein as a promising drug target using plant-driven phytocompounds used in traditional medicine systems with lesser side effects. Thus, the present study aims to explore the novel, less toxic, and natural inhibitors of Klebsiella pneumoniae AcrB pump protein from 69 Zingiber officinale phyto-molecules available in the SpiceRx database through computational-biology approaches. AcrB protein's homology-modelling was carried out to get a 3D structure. The multistep-docking (HTVS, SP, and XP) were employed to eliminate less-suitable compounds in each step based on the docking score. The chosen hit-compounds underwent induced-fit docking (IFD). Based on the XP GScore, the top three compounds, epicatechin (-10.78), 6-gingerol (-9.71), and quercetin (-9.09) kcal/mol, were selected for further calculation of binding free energy (MM/GBSA). Furthermore, the short-listed compounds were assessed for their drug-like properties based on in silico ADMET properties and Pa, Pi values. In addition, the molecular dynamics simulation (MDS) studies for 250 ns elucidated the binding mechanism of epicatechin, 6-gingerol, and quercetin to AcrB. From the dynamic binding free energy calculations using MM/PBSA, 6-gingerol exhibited a strong binding affinity towards AcrB. Further, the 6-gingerol complex's energy fluctuation was observed from the free energy landscape. In conclusion, 6-gingerol has a promising inhibiting potential against the AcrB efflux pump and thus necessitates further validation through in vitro and in vivo experiments.Communicated by Ramaswamy H. Sarma.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

Design of 2-amino-6-methyl-pyrimidine benzoic acids as ATP competitive casein kinase-2 (CK2) inhibitors using structure- and fragment-based design, docking and molecular dynamic simulation studies

Overexpression of casein kinase-2 (CK2) has been implicated in several carcinomas, mainly lung, prostate and acute myeloid leukaemia. The smaller nucleotide pocket compared to related kinases provides a great opportunity to discover newer ATP-competitive CK2 inhibitors. In this study, we have employed an integrated structure- and fragment-based design strategy to design 2-amino-6-methyl-pyrimidine benzoic acids as ATP-competitive CK2 inhibitors. A statistically significant four features-based E-pharmacophore (ARRR) model was used to screen 780,092 molecules. Further, the retrieved hits were considered for molecular docking study to identify essential binding interactions. At the same time, fragment-based virtual screening was performed using a dataset of 1,542,397 fragments. The identified hits and fragments were used as structure templates to rationalize the design of 2-amino-6-methyl-pyrimidine benzoic acids as newer CK2 inhibitors. Finally, the binding interactions of the designed hits were identified using an induced fit docking (IFD) study, and their stability was estimated by a molecular dynamics (MD) simulation study of 100 ns.

Advances in Computational Methods to Discover New NS2B-NS3 Inhibitors Useful Against Dengue and Zika Viruses

The Flaviviridae virus family consists of the genera Hepacivirus, Pestivirus, and Flavivirus, with approximately 70 viral types that use arthropods as vectors. Among these diseases, dengue (DENV) and zika virus (ZIKV) serotypes stand out, responsible for thousands of deaths worldwide. Due to the significant increase in cases, the World Health Organization (WHO) declared DENV a potential threat for 2019 due to being transmitted by infected travelers. Furthermore, ZIKV also has a high rate of transmissibility, highlighted in the outbreak in 2015, generating consequences such as Guillain-Barré syndrome and microcephaly. According to clinical outcomes, those infected with DENV can be asymptomatic, and in other cases, it can be lethal. On the other hand, ZIKV has severe neurological symptoms in newborn babies and adults. More serious symptoms include microcephaly, brain calcifications, intrauterine growth restriction, and fetal death. Despite these worrying data, no drug or vaccine is approved to treat these diseases. In the drug discovery process, one of the targets explored against these diseases is the NS2B-NS3 complex, which presents the catalytic triad His51, Asp75, and Ser135, with the function of cleaving polyproteins, with specificity for basic amino acid residues, Lys- Arg, Arg-Arg, Arg-Lys or Gln-Arg. Since NS3 is highly conserved in all DENV serotypes and plays a vital role in viral replication, this complex is an excellent drug target. In recent years, computer-aided drug discovery (CADD) is increasingly essential in drug discovery campaigns, making the process faster and more cost-effective, mainly explained by discovering new drugs against DENV and ZIKV. Finally, the main advances in computational methods applied to discover new compounds against these diseases will be presented here. In fact, molecular dynamics simulations and virtual screening is the most explored approach, providing several hit and lead compounds that can be used in further optimizations. In addition, fragment-based drug design and quantum chemistry/molecular mechanics (QM/MM) provides new insights for developing anti-DENV/ZIKV drugs. We hope that this review offers further helpful information for researchers worldwide and stimulates the use of computational methods to find a promising drug for treating DENV and ZIKV.

Targeting the DENV NS2B-NS3 protease with active antiviral phytocompounds: structure-based virtual screening, molecular docking and molecular dynamics simulation studies

Dengue fever has been a global health concern. Mitigation is a challenging problem due to non-availability of workable treatments. The most difficult objective is to design a perfect anti-dengue agent capable of inhibiting infections caused by all four serotypes. Various tactics have been employed in the past to discover dengue antivirals, including screening of chemical compounds against dengue virus enzymes. The objective of the current study is to investigate phytocompounds as anti-dengue remedies that target the non-structural 2B and non-structural 3 protease (NS2B-NS3^pro), a possible therapeutic target for dengue fever. Initially, 300 + antiviral phytocompounds were collected from Duke’s phytochemical and ethnobotanical database and 30 phytocompounds with anti-dengue properties were identified from previously reported studies, which were virtually screened against NS2B-NS3^pro using molecular docking and toxicity evaluation. The top five most screened ligands were naringin, hesperidin, gossypol, maslinic acid and rhodiolin with binding affinities of − 8.7 kcal/mol, − 8.5 kcal/mol, − 8.5 kcal/mol, − 8.5 kcal/mol and − 8.1 kcal/mol, respectively. The finest docked compounds complexed with NS2B-NS3^pro were subjected for molecular dynamics (MD) simulations and binding free energy estimations through molecular mechanics generalized born surface area–based calculations. The results of the study are intriguing in the context of computer-aided screening and the binding affinities of the phytocompounds, proposing maslinic acid (MAS) as a potent bioactive antiviral for the development of phytocompound-based anti-dengue agent.

Computational structural assessment of BReast CAncer type 1 susceptibility protein (BRCA1) and BRCA1-Associated Ring Domain protein 1 (BARD1) mutations on the protein-protein interface

Breast cancer type 1 susceptibility protein (BRCA1) is closely related to the BRCA2 (breast cancer type 2 susceptibility protein) and BARD1 (BRCA1-associated RING domain-1) proteins. The homodimers were formed through their RING fingers; however they form more compact heterodimers preferentially, influencing BRCA1 residues 1-109 and BARD1 residues 26-119. We implemented an integrative computational pipeline to screen all the mutations in BRCA1 and identify the most significant mutations influencing the Protein-Protein Interactions (PPI) in the BRCA1-BARD1 protein complex. The amino acids involved in the PPI regions were identified from the PDBsum database with the PDB ID: 1JM7. We screened 2118 missense mutations in BRCA1 and none in BARD1 for pathogenicity and stability and analyzed the amino acid sequences for conserved residues. We identified the most significant mutations from these screenings as V11G, M18K, L22S, and T97R positioned in the PPI regions of the BRCA1-BARD1 protein complex. We further performed protein-protein docking using the ZDOCK server. The native protein-protein complex showed the highest binding score of 2118.613, and the V11G mutant protein complex showed the least binding score of 1992.949. The other three mutation protein complexes had binding scores between the native and V11G protein complexes. Finally, a molecular dynamics simulation study using GROMACS was performed to comprehend changes in the BRCA1-BARD1 complex's binding pattern due to the mutation. From the analysis, we observed the highest deviation with lowest compactness and a decrease in the intramolecular h-bonds in the BRCA1-BARD1 protein complex with the V11G mutation compared to the native complex or the complexes with other mutations.

Discovery of Potential Antiviral Compounds against Hendra Virus by Targeting Its Receptor-Binding Protein (G) Using Computational Approaches

Hendra virus (HeV) belongs to the paramyxoviridae family of viruses which is associated with the respiratory distress, neurological illness, and potential fatality of the affected individuals. So far, no competitive approved therapeutic substance is available for HeV. For that reason, the current research work was conducted to propose some novel compounds, by adopting a Computer Aided Drug Discovery approach, which could be used to combat HeV. The G attachment Glycoprotein (Ggp) of HeV was selected to achieve the primary objective of this study, as this protein makes the entry of HeV possible in the host cells. Briefly, a library of 6000 antiviral compounds was screened for potential drug-like properties, followed by the molecular docking of short-listed compounds with the Protein Data Bank (PDB) structure of Ggp. Docked complexes of top two hits, having maximum binding affinities with the active sites of Ggp, were further considered for molecular dynamic simulations of 200 ns to elucidate the results of molecular docking analysis. MD simulations and Molecular Mechanics Energies combined with the Generalized Born and Surface Area (MMGBSA) or Poisson-Boltzmann and Surface Area (MMPBSA) revealed that both docked complexes are stable in nature. Furthermore, the same methodology was used between lead compounds and HeV Ggp in complex with its functional receptor in human, Ephrin-B2. Surprisingly, no major differences were found in the results, which demonstrates that our identified compounds can also perform their action even when the Ggp is attached to the Ephrin-B2 ligand. Therefore, in light of all of these results, we strongly suggest that compounds (S)-5-(benzylcarbamoyl)-1-(2-(4-methyl-2-phenylpiperazin-1-yl)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide and 5-(cyclohexylcarbamoyl)-1-(2-((2-(3-fluorophenyl)-2-methylpropyl)amino)-2-oxoethyl)-6-oxo-3,6-dihydropyridin-1-ium-3-ide could be considered as potential therapeutic agents against HeV; however, further in vitro and in vivo experiments are required to validate this study.

Multifaceted targeting strategies in cancer against the human notch 3 protein: a computational study

Notch receptors play a significant role in the development and the regulation of cell-fate in several multicellular organisms. For normal differentiation, genomes are essential as their regular roles and play a role in cancer is dysregulated. Notch 3 has been shown to play a major role in lung cancer function and therefore, inhibition of notch 3 protein activation represents a clear plan for cancer treatment. This study accomplished a combined structure- and ligand-based pharmacophore hypothesis to explore novel notch 3 inhibitors. The analysis identified common lead molecule ZINC000013449462 that showed better XP GScore and binding energy score than the reference inhibitor DAPT. The identified lead compound that passed all the druggable characteristics exhibited stable binding. Furthermore, the lead molecule can also form hydrogen and salt bridge interactions with binding site residues Asp1621 and Arg1465 residues, respectively of the active pockets of notch 3 protein. In essence, the inhibitory activity of the hit was validated across 109 NSCLC cell lines by employing a deep neural network algorithm. Our study proposes that ZINC000013449462 would be a possible prototype molecule towards the notch 3 target and further examined by clinical studies to combat NSCLC.

4-acetamido-3-nitrobenzoic acid - structural, quantum chemical studies, ADMET and molecular docking studies of SARS-CoV2

In December 2019, a new type of SARS corona virus emerged from China and caused a globally pandemic corona virus disease (COVID-19). This highly infectious virus has been named as SARS-CoV-2 by the International Committee of the Taxonomy of Viruses. It has severely affected a large population and economy worldwide. Globally various scientific communities have been involved in studying this newly emerged virus and is lifecycle. Multiple diverse studies are in progress to design novel therapeutic agents, in which understanding of interactions between the target and drug ligand is a significant key for this challenge. Structures of proteins involved in the life cycle of the virus have been revealed in RCSB PDB by researchers. In this study, we employed molecular docking study of 4-Acetamido-3-nitrobenzoic acid (ANBA) with corona virus proteins (spike protein, spike binding domain with ACE2 receptor and Main protease, RNA-dependent RNA polymerase). Single crystal X-ray analysis and density functional theory calculations were carried out for ANBA to explore the structural and chemical-reactive parameters. Intermolecular interactions which are involved in the ligand-protein binding process are validated by Hirshfeld surface analysis. To study the behaviour of ANBA in a living organism and to calculate the physicochemical parameters, ADMET analysis was done using SwissADME and Osiris data warrior tools. Further, Toxicity of ANBA was predicted using pkCSM online software. Based on the molecular docking analysis, we introduce here a potent drug molecule that binds to the COVID-19 proteins.Communicated by Ramaswamy H. Sarma.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Virtual Screening for Potential Inhibitors of Human Hexokinase II for the Development of Anti-Dengue Therapeutics

Dengue fever, which is a disease caused by the dengue virus (DENV), is a major unsolved issue in many tropical and sub-tropical regions of the world. The absence of treatment that effectively prevent further viral propagation inside the human’s body resulted in a high number of deaths globally each year. Thus, novel anti-dengue therapies are required for effective treatment. Human hexokinase II (HKII), which is the first enzyme in the glycolytic pathway, is an important drug target due to its significant impact on viral replication and survival in host cells. In this study, 23.1 million compounds were computationally-screened against HKII using the Ultrafast Shape Recognition with a CREDO Atom Types (USRCAT) algorithm. In total, 300 compounds with the highest similarity scores relative to three reference molecules, known as Alpha-D-glucose (GLC), Beta-D-glucose-6-phosphate (BG6), and 2-deoxyglucose (2DG), were aligned. Of these 300 compounds, 165 were chosen for further structure-based screening, based on their similarity scores, ADME analysis, the Lipinski’s Rule of Five, and virtual toxicity test results. The selected analogues were subsequently docked against each domain of the HKII structure (PDB ID: 2NZT) using AutoDock Vina programme. The three top-ranked compounds for each query were then selected from the docking results based on their binding energy, the number of hydrogen bonds formed, and the specific catalytic residues. The best docking results for each analogue were observed for the C-terminus of Chain B. The top-ranked analogues of GLC, compound 10, compound 26, and compound 58, showed predicted binding energies of −7.2, −7.0, and −6.10 kcal/mol and 7, 5, and 2 hydrogen bonds, respectively. The analogues of BG6, compound 30, compound 36, and compound 38, showed predicted binding energies of −7.8, −7.4, and −7.0 kcal/mol and 11, 9, and 5 hydrogen bonds, while the top three analogues of 2DG, known as compound 1, compound 4, and compound 31, showed predicted binding energies of −6.8, −6.3, and −6.3 kcal/mol and 4, 3, and 1 hydrogen bonds, sequentially. The highest-ranked compounds in the docking analysis were then selected for molecular dynamics simulation, where compound 10, compound 30, and compound 1, which are the analogues of GLC, BG6, and 2DG, have shown strong protein-ligand stability with an RMSD value of ±5.0 A° with a 5 H bond, ±4.0 A° with an 8 H bond, and ±0.5 A° with a 2 H bond, respectively, compared to the reference molecules throughout the 20 ns simulation time. Therefore, by using the computational studies, we proposed novel compounds, which may act as potential drugs against DENV by inhibiting HKII’s activity.

Structure-guided screening of chemical database to identify NS3-NS2B inhibitors for effective therapeutic application in dengue infection

Dengue infection is the most common arthropod-borne disease caused by dengue viruses, predominantly affecting millions of human beings annually. To find out promising chemical entities for therapeutic application in Dengue, in the current research, a multi-step virtual screening effort was conceived to screen out the entire "screening library" of the Asinex database. Initially, through "Lipinski rule of five" filtration criterion almost 0.6 million compounds were collected and docked with NS3-NS2B protein. Thereby, the chemical space was reduced to about 3500 compounds through the analysis of binding affinity obtained from molecular docking study in AutoDock Vina. Further, the "Virtual Screening Workflow" (VSW) utility of Schrödinger suite was used, which follows a stepwise multiple docking programs such as - high-throughput virtual screening (HTVS), standard precision (SP), and extra precision (XP) docking, and in postprocessing analysis the MM-GBSA based free binding energy calculation. Finally, five potent molecules were proposed as potential inhibitors for the dengue NS3-NS2B protein based on the investigation of molecular interactions map and protein-ligand fingerprint analyses. Different pharmacokinetics and drug-likeness parameters were also checked, which favour the potentiality of selected molecules for being drug-like candidates. The molecular dynamics (MD) simulation analyses of protein-ligand complexes were explained that NS3-NS2B bound with proposed molecules quite stable in dynamic states as observed from the root means square deviation (RMSD) and root means square fluctuation (RMSF) parameters. The binding free energy was calculated using MM-GBSA method from the MD simulation trajectories revealed that all proposed molecules possess such a strong binding affinity towards the dengue NS3-NS2B protein. Therefore, proposed molecules may be potential chemical components for effective inhibition of dengue NS3-NS2B protein subjected to experimental validation.

Identifying new piperazine-based PARP1 inhibitors using text mining and integrated molecular modeling approaches

One of the important molecular targets for antitumor drug discovery is the polyadenosine diphosphate-ribose polymerase-1 (PARP1) enzyme. It is linked with various biological functions including DNA repair and apoptosis. It is primarily a nuclear enzyme linked to chromatin, which is activated by DNA damage. Improved expression of PARP1 in melanomas, breast cancer, lung cancer and other neoplastic diseases is often observed. A tremendous PARP research concerning cancer and ischemia is progressing very rapidly. There are currently four PARP1 inhibitors approved by the FDA on the market, namely Olaparib, Rucaparib, Niraparib and Talazoparib. All of these molecules are non-selective inhibitors of PARP1. Currently there is an urgent need for novel and selective PARP1 inhibitors. In this work, asmall molecule database (Specs SC) were used to identify the new selective lead inhibitors of PARP1. Piperazine scaffold is an important fragment that is used in many currently used FDA approved drugs in different diseases including PARP1 inhibitor Olaparib. Thus, based on text mining studies, 4674 compounds thatinclude piperazine fragments were identified and virtually screened at the binding pocket of target protein PARP1. Compounds that have high docking scores were used in molecular dynamics (MD) simulations. Free energy calculations were also performed to compare the predicted binding energies with known PARP1 inhibitors. The critical amino acid interactions of these newly identified hits in the binding pocket were also investigated in detail for better understanding of the structural features required for next generation PARP1 inhibitors. Thus, here together with combination of text-mining and integrated molecular modeling approaches, we identified novel piperazine-based hits against PARP1 enzyme.Communicated by Ramaswamy H. Sarma.

Research Techniques Made Simple: Molecular Docking in Dermatology - A Foray into In Silico Drug Discovery

Drug discovery is a complex process with many potential pitfalls. To go to market, a drug must undergo extensive preclinical optimization followed by clinical trials to establish its efficacy and minimize toxicity and adverse events. The process can take 10-15 years and command vast research and development resources costing over $1 billion. The success rates for new drug approvals in the United States are < 15%, and investment costs often cannot be recouped. With the increasing availability of large public datasets (big data) and computational capabilities, data science is quickly becoming a key component of the drug discovery pipeline. One such computational method, large-scale molecular modeling, is critical in the preclinical hit and lead identification process. Molecular modeling involves the study of the chemical structure of a drug and how it interacts with a potential disease-relevant target, as well as predicting its ADMET properties. The scope of molecular modeling is wide and complex. Here we specifically discuss docking, a tool commonly employed for studying drug-target interactions. Docking allows for the systematic exploration of how a drug interacts at a protein binding site and allows for the rank-ordering of drug libraries for prioritization in subsequent studies. This process can be efficiently used to virtually screen libraries containing over millions of compounds.

Structure-based virtual screening, molecular dynamics simulation and MM-PBSA toward identifying the inhibitors for two-component regulatory system protein NarL of Mycobacterium Tuberculosis

The nitrate/nitrite response regulatory protein NarL belongs to the two-component regulatory system of Mycobacterium tuberculosis (MTB), plays a crucial role in anaerobic survival of mycobacteria in host. The absence of this protein in humans, makes it an attractive drug target for MTB treatment. However, the specific drug molecules targeting NarL are yet to be identified. In this study, we identified the promising drug candidates using structure based virtual screening of compounds from chemical libraries (ChEMBL and ZINC), followed by the extensive physicochemical properties analyses and molecular dynamics (MD) simulation. As the initial results, we obtained 4,754 bioactive compounds from ChEMBL having anti-tuberculosis activity which is finally narrowed down to the best 10 hits. A similar approach was applied to search for structurally similar compounds from ZINC data, corresponding to the top hits obtained from ChEMBL. Our collective results show that two compounds, ChEMBL509609 (Gscore - 5.054 kcal/mol, Xscore - 6.47 kcal/mol) and ZINC01843143 (Gscore - 5.114 kcal/mol, Xscore - 6.46 kcal/mol) having the best docking score and ADMET profile. The structural stability and dynamics of lead molecules at active site of NarL were examined using MD simulation and the binding free energies were estimated with MM-PBSA. Essential dynamics and MM-PBSA demonstrated that NarL-ChEMBL509609 complex remains the most stable during simulation of 100 ns with the higher binding free energy which may be a suitable candidate for further experimental analysis. AbbreviationsADMEAbsorption, Distribution, Metabolism, And ExcretionBCGBacillus Calmette-GuerinCNSCentral nervous systemDOTSDirectly observed treatment, short courseEDEssential dynamicsHIVHuman immunodeficiency virusHKHistidine kinaseHOAHuman oral absorptionHTVSHigh throughput virtual screeningIRRIIrritationMDMolecular dynamicsMDRMultidrug resistantMTBMycobacterium tuberculosisMUTMutagenicityMWMolecular weightPHOAPercentage of human oral absorptionREPReproductive developmentRgRadius of gyrationRMSDRoot mean square deviationRMSFRoot mean square fluctuationRO5Lipinski's rule of fiveRRResponse regulatorSPStandard precisionSPGStandard precision glideTBTuberculosisTCSTwo-component regulatory systemTDRTotally drug-resistantTUMOTumorigenicityWHOWorld health organizationXDRExtensively drug-resistantXPExtra precisionCommunicated by Ramaswamy H. Sarma.

Structure-based screening and validation of potential dengue virus inhibitors through classical and QM/MM affinity estimation

The recurrent outbreaks of dengue virus around the globe represent a huge challenge for governments and public health organizations. With the rapid growth and ease of transportation, dengue disease continues to spread, placing more of the world population under constant threat. Despite decades of research efforts, no effective small molecule antivirals are available against dengue virus. With the efficacy of the recently developed vaccine to be determined, there is an urgent unmet need for small molecule dengue virus treatments. In the current study, we employed state-of-the-art molecular modelling simulations to identify novel inhibitors of the dengue virus envelope protein. The binding modes of all compounds within the conserved β-OctylGlucoside (β-OG) pocket were studied using a combination of docking, molecular dynamics simulations and binding free energy calculations. Here, we describe ten new compounds that significantly reduce production of dengue virus as determined using standard cell-based virological assays. Moreover, we present a comprehensive structural analysis of the identified hits, focusing on their electrostatic and lipophilic binding energy contributions. Finally, we highlight the effect of the desolvation penalty in limiting the activity of some of these compounds. The data presented here paves the way toward rationally designing selective and potent novel inhibitors against dengue virus.

Exploiting the unique features of Zika and Dengue proteases for inhibitor design

Zika and Dengue viruses have attracted substantial attention from researchers in light of recent outbreaks of Dengue fever and increases in cases of congenital microcephaly in areas with Zika incidence. This review summarizes the current state of knowledge about Zika and Dengue proteases. These enzymes have several interesting features: 1) NS3 serine protease requires the activating co-factor NS2B, which is anchored in the membrane of the endoplasmic reticulum; 2) NS2B displays extensive conformational dynamics; 3) NS3 is a multidomain protein with proteolytic, NTPase, RNA 5' triphosphatase and helicase activity and has many protein-protein interaction partners; 4) NS3 is autoproteolytically released from its precursor. Attempts to design tight-binding and specific active-site inhibitors are complicated by the facts that the substrate pocket of the NS2B-NS3 protease is flat and the active-site ligands are charged. The ionic character of potential active-site inhibitors negatively influences their cell permeability. Possibilities to block cis-autoprocessing of the protease precursor have recently been considered. Additionally, potential allosteric sites on NS2B-NS3 proteases have been identified and allosteric compounds have been designed to impair substrate binding and/or block the NS2B-NS3 interaction. Such compounds could be specific to viral proteases, without off-target effects on host serine proteases, and could have favorable pharmacokinetic profiles. This review discusses various groups of inhibitors of these proteases according to their mechanisms of action and chemical structures.

Computational screening of medicinal plant phytochemicals to discover potent pan-serotype inhibitors against dengue virus

Emergence of Dengue as one of the deadliest viral diseases prompts the need for development of effective therapeutic agents. Dengue virus (DV) exists in four different serotypes and infection caused by one serotype predisposes its host to another DV serotype heterotypic re-infection. We undertook virtual ligand screening (VLS) to filter compounds against DV that may inhibit inclusively all of its serotypes. Conserved non-structural DV protein targets such as NS1, NS3/NS2B and NS5, which play crucial role in viral replication, infection cycle and host interaction, were selected for screening of vital antiviral drug leads. A dataset of plant based natural antiviral derivatives was developed. Molecular docking was performed to estimate the spatial affinity of target compounds for the active sites of DV’s NS1, NS3/NS2B and NS5 proteins. The drug likeliness of the screened compounds was followed by ADMET analysis whereas the binding behaviors were further elucidated through molecular dynamics (MD) simulation experiments. VLS screened three potential compounds including Canthin-6-one 9-O-beta-glucopyranoside, Kushenol W and Kushenol K which exhibited optimal binding with all the three conserved DV proteins. This study brings forth novel scaffolds against DV serotypes to serve as lead molecules for further optimization and drug development against all DV serotypes with equal effect against multiple disease causing DV proteins. We therefore anticipate that the insights given in the current study could be regarded valuable towards exploration and development of a broad-spectrum natural anti-dengue therapy.

Evaluation of the Leaf Essential Oil from Artemisia vulgaris and Its Larvicidal and Repellent Activity against Dengue Fever Vector Aedes aegypti-An Experimental and Molecular Docking Investigation

Aedes aegypti is a mosquito vector that spreads dengue fever and yellow fever worldwide in tropical and subtropical countries. Essential oil isolated from Artemisia vulgaris is found to have larvicidal and repellent action against this vector. The dried leaves were subjected to hydrodistillation using a clevenger-type apparatus for 4 h. The isolated essential oil was analyzed by using gas chromatography-mass spectrometry, and the major insecticidal compounds were identified as α-humulene (0.72%), β-caryophyllene (0.81%), and caryophyllene oxide (15.87%). Larvicidal activity results revealed that the essential oil exposure for 24 h period against the third stage larvae was LC50 = 6.87, LC90 = 59.197 ppm and for the fourth stage larvae LC50 = 4.269, LC90 = 50.363 ppm. Highest mortality rates were observed at 24 h exposure period of third and fourth stages, and the exposed A. aegypti larvae were subjected to histo chemical studies, and the studies revealed that larvae cells got totally damaged (midgut and cortex). The repellent activity results revealed that at 50% concentration of the essential oil showed the highest repellent activity at 60 min protection time against the A. aegypti female mosquitoes. To gain further insights into the insecticidal activity, density functional theory and molecular docking calculations were performed with the active components of this essential oil as the ligand and NS3 protease domain (PDB ID: 2FOM) as a receptor. Molecular docking calculation results show that (E)-β-caryophyllene strongly binds with NS3 protease domain than (Z)-β-caryophyllene, α-humulene, and β-caryophyllene oxide and is the major active component for the insecticidal action. It primarily interacts with the receptor through hydrophobic and ionic forces and using water bridges between the amino acid residues in the binding pocket and (E)-β-caryophyllene.

Spresso: an ultrafast compound pre-screening method based on compound decomposition

Motivation

Recently, the number of available protein tertiary structures and compounds has increased. However, structure-based virtual screening is computationally expensive owing to docking simulations. Thus, methods that filter out obviously unnecessary compounds prior to computationally expensive docking simulations have been proposed. However, the calculation speed of these methods is not fast enough to evaluate ≥ 10 million compounds.

Results

In this article, we propose a novel, docking-based pre-screening protocol named Spresso (Speedy PRE-Screening method with Segmented cOmpounds). Partial structures (fragments) are common among many compounds; therefore, the number of fragment variations needed for evaluation is smaller than that of compounds. Our method increases calculation speeds by ∼200-fold compared to conventional methods.

Availability and Implementation

Spresso is written in C ++ and Python, and is available as an open-source code (http://www.bi.cs.titech.ac.jp/spresso/) under the GPLv3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

Dengue Virus and Its Inhibitors: A Brief Review

The global occurrence of viral infectious diseases poses a significant threat to human health. Dengue virus (DENV) infection is one of the most noteworthy of these infections. According to a WHO survey, approximately 400 million people are infected annually; symptoms deteriorate in approximately one percent of cases. Numerous foundational and clinical investigations on viral epidemiology, structure and function analysis, infection source and route, therapeutic targets, vaccines, and therapeutic drugs have been conducted by both academic and industrial researchers. At present, CYD-TDV or Dengvaxia^® is the only approved vaccine, but potent inhibitors are currently under development. In this review, an overview of the viral life circle and the history of DENVs is presented, and the most recently reported antiviral candidates and newly discovered promising targets are focused and summarized. We believe that these successes and failures have enabled progress in anti-DENV drug discovery and hope that our review will stimulate further innovation in this area.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.

Discovery of selective dengue virus inhibitors using combination of molecular fingerprint-based virtual screening protocols, structure-based pharmacophore model development, molecular dynamics simulations and in vitro studies

Dengue virus is a major issue of tropical and sub-tropical regions. The proliferation of virus results in immense number of deaths each year because of unavailability of on-shelf drugs. This issue necessitates the design of novel anti-Dengue drugs. The protease enzyme pathway is the critical target for drug design due to its significance in the replication, survival and other cellular activities of Dengue virus. Keeping in mind the worsening situation regarding Dengue virus, approximately eighteen million drug-like compounds from the ZINC small molecule database have been screened against Nonstructural Protein 3 (NS3) previously by our group. In this study, in order to investigate the effect of extended time of molecular dynamics (MD) simulations on structural and dynamical profiles of used complexes, simulation run time is increased from 50-ns to 100-ns for the each system. In addition, a well-known Dengue virus inhibitor (MB21) from literature is used as reference structure (positive control) to compare the proposed molecules. Post-processing MD analyses including Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations were conducted to predict binding free energies of inhibitors from derived trajectory frames of MD simulations. Identified compounds are further directed to Quantum-Polarized Ligand Docking (QPLD), molecular fingerprint-based virtual screening of another small molecule database (Otava Drug Like small molecule database), and Structure-based Pharmacophore Modeling (E-Pharmacophore). Finally, cell proliferation and cytotoxicity tests as well as pre- and post-treatment on HUH7 cells infected with DENV2 NGC strain are applied for four identified hit molecules (ZINC36681949, ZINC44921800, ZINC95518765 and ZINC39500661) to check whether these drugs inhibit DENV2 from entry and/or exit pathways. Based on cell-based Dengue quantification assays, there is no effect seen on pre-treatment of cells with these compounds indicating that the early infection processes of virus is not affected. In contrast, the post-treatment of cells with these compounds after Dengue virus infection has resulted in a significant 1 log PFU/ml reduction of the virus infectious titre.

Discovery of selective dengue virus inhibitors using combination of molecular fingerprint-based virtual screening protocols, structure-based pharmacophore model development, molecular dynamics simulations and in vitro studies

Dengue virus is a major issue of tropical and sub-tropical regions. The proliferation of virus results in immense number of deaths each year because of unavailability of on-shelf drugs. This issue necessitates the design of novel anti-Dengue drugs. The protease enzyme pathway is the critical target for drug design due to its significance in the replication, survival and other cellular activities of Dengue virus. Keeping in mind the worsening situation regarding Dengue virus, approximately eighteen million drug-like compounds from the ZINC small molecule database have been screened against Nonstructural Protein 3 (NS3) previously by our group. In this study, in order to investigate the effect of extended time of molecular dynamics (MD) simulations on structural and dynamical profiles of used complexes, simulation run time is increased from 50-ns to 100-ns for the each system. In addition, a well-known Dengue virus inhibitor (MB21) from literature is used as reference structure (positive control) to compare the proposed molecules. Post-processing MD analyses including Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations were conducted to predict binding free energies of inhibitors from derived trajectory frames of MD simulations. Identified compounds are further directed to Quantum-Polarized Ligand Docking (QPLD), molecular fingerprint-based virtual screening of another small molecule database (Otava Drug Like small molecule database), and Structure-based Pharmacophore Modeling (E-Pharmacophore). Finally, cell proliferation and cytotoxicity tests as well as pre- and post-treatment on HUH7 cells infected with DENV2 NGC strain are applied for four identified hit molecules (ZINC36681949, ZINC44921800, ZINC95518765 and ZINC39500661) to check whether these drugs inhibit DENV2 from entry and/or exit pathways. Based on cell-based Dengue quantification assays, there is no effect seen on pre-treatment of cells with these compounds indicating that the early infection processes of virus is not affected. In contrast, the post-treatment of cells with these compounds after Dengue virus infection has resulted in a significant 1logPFU/ml reduction of the virus infectious titre.

The impact of in silico screening in the discovery of novel and safer drug candidates

Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.

Discovery of Klotho peptide antagonists against Wnt3 and Wnt3a target proteins using combination of protein engineering, protein-protein docking, peptide docking and molecular dynamics simulations

The Klotho is known as lifespan enhancing protein involved in antagonizing the effect of Wnt proteins. Wnt proteins are stem cell regulators, and uninterrupted exposure of Wnt proteins to the cell can cause stem and progenitor cell senescence, which may lead to aging. Keeping in mind the importance of Klotho in Wnt signaling, in silico approaches have been applied to study the important interactions between Klotho and Wnt3 and Wnt3a (wingless-type mouse mammary tumor virus (MMTV) integration site family members 3 and 3a). The main aim of the study is to identify important residues of the Klotho that help in designing peptides which can act as Wnt antagonists. For this aim, a protein engineering study is performed for Klotho, Wnt3 and Wnt3a. During the theoretical analysis of homology models, unexpected role of number of disulfide bonds and secondary structure elements has been witnessed in case of Wnt3 and Wnt3a proteins. Different in silico experiments were carried out to observe the effect of correct number of disulfide bonds on 3D protein models. For this aim, total of 10 molecular dynamics (MD) simulations were carried out for each system. Based on the protein–protein docking simulations of selected protein models of Klotho with Wnt3 and Wnt3a, different peptides derived from Klotho have been designed. Wnt3 and Wnt3a proteins have three important domains: Index finger, N-terminal domain and a patch of ∼10 residues on the solvent exposed surface of palm domain. Protein–peptide docking of designed peptides of Klotho against three important domains of palmitoylated Wnt3 and Wnt3a yields encouraging results and leads better understanding of the Wnt protein inhibition by proposed Klotho peptides. Further in vitro studies can be carried out to verify effects of novel designed peptides as Wnt antagonists.