Virtual Ligand Screening Research Articles

Rational designing of peptide-ligand conjugates-based immunotherapy for the treatment of complicated malaria

AimsMalaria deaths occur primarily due to complicated malaria associated with the sequestration of Plasmodium falciparum-infected erythrocyte (PfIE) in the capillary microvasculature. This study aims to design peptide ligand conjugates (PLCs) for treating complicated malaria using various in silico techniques. The PLC includes a natural ligand for the Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1): expressed explicitly on the surface of PfIE, and a highly immunogenic peptide derived from the commonly used peptide vaccines in malaria-endemic countries. The ligand is predicted to prevent the sequestration of PfIE, and the peptide is predicted to eliminate PfIE from circulation by the pre-existing vaccine-induced immunity. Main methodsThe epitope identification from the vaccines and the analysis of physicochemical properties, antigenicity, allergenicity, and toxicity were performed using SVMTriP, ProtParam, VaxiJen, AllerTop, and ToxinPred servers, respectively. The high throughput virtual screening (HTVS) and drug-like properties analysis of natural compound ligands were carried out by Schrodinger-2021 software. The molecular dynamics simulations were performed through the WebGro server. Key findingsHTVS revealed three bioactive natural ligands for PfEMP1 from (NPASS) database. Three super immunogenic peptides were identified from malaria-endemic countries' commonly used peptide vaccines. Finally, Nine PLCs were designed with different combinations of peptides and ligands with the suitable non-cleavable triazole linker. SignificanceAntimalarials have been losing efficacy in a time when malaria deaths in 2020 significantly increased than in 2019. In this scenario, further research on the designed PLCs may offer some innovative immune therapeutics for complicated malaria with minimum possibilities of drug resistance.

Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model

BackgroundWith the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans (Pomphorhynchus laevis, Neoechinorhynchus agilis, Neoechinorhynchus buttnerae) from four fish species (common barbel, European eel, thinlip mullet, tambaqui).ResultsThe workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel.ConclusionsThe combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL tinyurl.com/yx72rda7.

Corrigendum to “Identification and experimental confirmation of novel cGMP efflux inhibitors by virtual ligand screening of vardenafil-analogues” [Biomed. Pharmacother. 126 (2020) 110109

Corrigendum to “Identification and experimental confirmation of novel cGMP efflux inhibitors by virtual ligand screening of vardenafil-analogues” [Biomed. Pharmacother. 126 (2020) 110109

An Application of InterCriteria Analysis Approach to Assess the AMMOS Software Platform Outcomes

The experimental procedures of drug design, proven to be time-consuming and costly, are successfully complemented with computer-aided (in silico) approaches nowadays. Virtual ligand screening (VLS) is one of the most promising approaches when searching for new hit compounds. The efficiency of VLS procedures might be improved via post-docking optimization. In the focus of this investigation is AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening) developed as multi-step structure-based procedure for efficient computational refinement of protein-ligand complexes at different levels of protein flexibility. Their performance has been assessed by the recently developed InterCriteria Analysis (ICrA), elaborated as multi-criterion decision-making approach to reveal possible relations in the behavior of pairs of criteria when multiple objects are considered. The capacity of ICrA as a supporting tool to assess the effect of applying different levels of protein flexibility in the post-docking optimization via AMMOS has been investigated and analyzed.

Repurposed antibiotic and antiviral drugs inhibit the immune evasive endoribonuclease of SARS-CoV-2 and restrict coronavirus infection in vitro

Abstract Despite the success of SARS-CoV-2 vaccines in curbing viral transmission and severe disease, infections in unvaccinated and immune deficient patients continue to drive significant morbidity and mortality globally. To address the needs of this patient population, multiple antiviral drugs must be developed to expand on the protection currently offered by convalescent plasma, monoclonal antibodies, remdesivir, and other next generation SARS-CoV-2 antivirals which may be contraindicated for some patient groups. To this end, we employed a machine-learning based virtual ligand screening algorithm, FRAGSITE, to screen a library of FDA approved compounds for binding to the SARS-CoV-2 endoribonuclease (nsp15), a structurally conserved and demonstrated coronavirus virulence factor in SARS-CoV-1, HCoV-229E, IBV, MHV, and PEDV. Using recombinant SARS-CoV-2 nsp15, we identified the inhibition of nuclease activity by 11 drugs in vitro; 6 also restrict infection by the related human OC43 coronavirus in human A549 airway epithelial cells as measured by a focus forming assay. Among these 6 candidates, pibrentasvir remained active against HCoV-OC43 at the lowest concentration (IC50 < 0.625μM). Consistent with previous findings, HCoV-OC43 infection of A549 cells did not provoke an innate response to the viral dsRNA as quantified by IFNB1 mRNA production, antiviral stress granule formation, or RNase L activation. Our preliminary results suggest that antiviral stress granule formation is partially restored by nsp15 inhibitor treatment in HCoV-OC43 infected cells, although IFNβ production and RNase L activation remain unchanged. Supported by grants from the Emory School of Medicine

Identification of acridinedione scaffolds as potential inhibitor of DENV-2 C protein: An in silico strategy to combat dengue.

Dengue is a prominent viral disease transmitted by mosquitoes to humans that affects mainly tropical and subtropical countries worldwide. The global spread of dengue virus (DENV) is mainly occurred by Aedes aegypti and Aedes albopictus mosquitoes. The dengue virus serotypes-2 (DENV-2) is a widely prevalent serotype of DENV, that causes the hemorrhagic fever and bleeding in the mucosa, which can be fatal. In the life cycle of DENV-2, a structural capsid (DENV-2 C) protein forms the nucleocapsid assembly and bind to the viral progeny RNA. For DENV-2 maturation, the nucleocapsid is a vital component. We used virtual ligand screening to filter out the best in-house synthesized acridinedione analogs (DSPD molecules) that could efficiently bind to DENV-2 C protein. The molecular docking and dynamics simulations studies were performed to analyze the effect of DSPD molecules on DENV-2 C protein after binding. Our findings showed that DSPD molecules strongly interacted with DENV-2 C protein, as evident from molecular interactions and several time-dependent molecular dynamics-driven analyses. Moreover, this study was also supported by the thermodynamic binding free energy and steered molecular dynamics simulations. Therefore, we intend to suggest that the DSPD3 molecule could be used as a potential therapeutic molecule against dengue complications as compared to the cocrystallized inhibitor ST-148. However, further studies are required to demonstrate the ability of DSPD3 to induce DENV-2 C tetramer formation.

Malaria Box-Inspired Discovery of N-Aminoalkyl-β-carboline-3-carboxamides, a Novel Orally Active Class of Antimalarials.

Virtual ligand screening of a publicly available database of antimalarial hits using a pharmacophore derived from antimalarial MMV008138 identified TCMDC-140230, a tetrahydro-β-carboline amide, as worthy of exploration. All four stereoisomers of this structure were synthesized, but none potently inhibited growth of the malaria parasite Plasmodium falciparum. Interestingly, 7e, a minor byproduct of these syntheses, proved to be potent in vitro against P. falciparum and was orally efficacious (40 mg/kg) in an in vivo mouse model of malaria.

Virtual screening and docking analysis of novel ligands for selective enhancement of tea (Camellia sinensis) flavonoids

Flavour of tea is mainly contributed by a group of polyphenols – flavonoids. However, the content of flavonoid fluctuates seasonally and is found to be higher in the first flush of tea, when compared to the second flush. This disparity in the flavonoid content, and hence taste, incurs heavy economic losses to the tea plantation industry each harvest season. For our present study, four key product-specific enzymes (PAL, FNS, FLS and ANS) of the tea-specific flavonoid pathway were selected to perform molecular docking studies with specific virtually screened allosteric modulators. Results of docking analyses showed Naringenin, 2-Morpholin-4-ium-4-ylethanesulfonate, 6-C-Glucosylquercetin, 2-Oxoglutaric acid, 3,5,7,3′,4′-pentahydroxyflavone to be capable of improving the spontaneity of the enzyme-substrate reactions in terms of docking score, RMSD values, and non-covalent interactions (H-bond,hydrophobic interaction, Π-stacking, salt bridge, etc.). Further, the evolutionary relationship of tea flavonoid pathway enzymes was constructed and compared with related taxa. The codon usage-based of tea flavonoid biosyntheticgenes indicated the non-biasness of their nucleotide composition. Overall this study will provide a direction towards putative ligand-dependent enhancement of flavonoid content, irrespective of seasonal variation.

Synthon-based ligand discovery in virtual libraries of over 11 billion compounds.

Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures1-4 and ultra-large libraries of virtual compounds5,6. However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries7, new approaches to compound screening are needed8,9. Here we introduce a modularsynthon-based approach-V-SYNTHES-to perform hierarchical structure-based screening of a REAL Spacelibrary of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold-synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novelcannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant (Ki) = 0.9 nM) and CB2/CB1 selectivity (50-200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm.

Natural triterpenoids from licorice potently inhibit SARS-CoV-2 infection

IntroductionThe COVID-19 global epidemic caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2) is a great public health emergency. Discovering antiviral drug candidates is urgent for the prevention and treatment of COVID-19. ObjectivesThis work aims to discover natural SARS-CoV-2 inhibitors from the traditional Chinese herbal medicine licorice. MethodsWe screened 125 small molecules from Glycyrrhiza uralensis Fisch. (licorice, Gan-Cao) by virtual ligand screening targeting the receptor-binding domain (RBD) of SARS-CoV-2 spike protein. Potential hit compounds were further evaluated by ELISA, SPR, luciferase assay, antiviral assay and pharmacokinetic study. ResultsThe triterpenoids licorice-saponin A3 (A3) and glycyrrhetinic acid (GA) could potently inhibit SARS-CoV-2 infection, with EC50 of 75 nM and 3.17 µM, respectively. Moreover, we reveal that A3 mainly targets the nsp7 protein, and GA binds to the spike protein RBD of SARS-CoV-2. ConclusionIn this work, we found GA and A3 from licorice potently inhibit SARS-CoV-2 infection by affecting entry and replication of the virus. Our findings indicate that these triterpenoids may contribute to the clinical efficacy of licorice for COVID-19 and could be promising candidates for antiviral drug development.

Identification and characterization of potent, selective, and efficacious inhibitors of human arylamine N-acetyltransferase 1.

Arylamine N-acetyltransferase 1 (NAT1) plays a pivotal role in the metabolism of carcinogens and is a drug target for cancer prevention and/or treatment. A protein-ligand virtual screening of 2 million chemicals was ranked for predicted binding affinity towards the inhibition of human NAT1. Sixty of the five hundred top-ranked compounds were tested experimentally for inhibition of recombinant human NAT1 and N-acetyltransferase 2 (NAT2). The most promising compound 9,10-dihydro-9,10-dioxo-1,2-anthracenediyl diethyl ester (compound 10) was found to be a potent and selective NAT1 inhibitor with an in vitro IC50 of 0.75µM. Two structural analogs of this compound were selective but less potent for inhibition of NAT1 whereas a third structural analog 1,2-dihydroxyanthraquinone (a compound 10 hydrolysis product also known as Alizarin) showed comparable potency and efficacy for human NAT1 inhibition. Compound 10 inhibited N-acetylation of the arylamine carcinogen 4-aminobiphenyl (ABP) both in vitro and in DNA repair-deficient Chinese hamster ovary (CHO) cells in situ stably expressing human NAT1 and CYP1A1. Compound 10 and Alizarin effectively inhibited NAT1 in cryopreserved human hepatocytes whereas inhibition of NAT2 was not observed. Compound 10 caused concentration-dependent reductions in DNA adduct formation and DNA double-strand breaks following metabolism of aromatic amine carcinogens beta-naphthylamine and/or ABP in CHO cells. Compound 10 inhibited proliferation and invasion in human breast cancer cells and showed selectivity towards tumorigenic versus non-tumorigenic cells. In conclusion, our study identifies potent, selective, and efficacious inhibitors of human NAT1. Alizarin's ability to inhibit NAT1 could reduce breast cancer metastasis particularly to bone.

Prediction of severe adverse events, modes of action and drug treatments for COVID-19\u2019s complications

Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19’s clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation’s molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19’s clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19’s severe adverse events and lesser ones such as loss of taste/smell.

Machine‐Assisted Preparation of a Chiral Diamine Ligand Library and In Silico Screening Using Ab Initio Structural Parameters for Heterogeneous Chiral Catalysts

AbstractA ligand library containing 31 chiral diamines was synthesized using a flow‐based semiautomatic reductive amination system. These ligands were evaluated in a continuous‐flow asymmetric 1,4‐addition reaction with a heterogeneous Ni catalyst. Based on the experimental results of ab initio DFT calculations, a prediction model for enantioselectivities was successfully constructed. Furthermore, virtual screening of possible ligands was conducted to identify promising structures, which showed good enantioselectivities in experiments.magnified image

Inhibition of ABCC5-mediated cGMP transport by progesterone, testosterone and their analogues

The biodynamics and biokinetics of sex hormones are complex. In addition to the classical steroid receptors (nuclear receptors), these hormones act through several non-genomic mechanisms. Modulation of ABC-transporters by progesterone represents a non-genomic mechanism. In the present study, we employed inside out vesicles from human erythrocytes to characterize high affinity cGMP transport by ABCC5 (member 5 of the ATP-Binding Cassette subfamily C). Progesterone and testosterone inhibited the transport with respective Ki of 1.2 ± 0.3 and 2.0 ± 0.6 μmol/L. We used virtual ligand screening (VLS) to identify analogues to progesterone and testosterone. A large number of substances were screened in silico and the 19 most promising candidates were screened in vitro. Each substance was tested for a concentration of 10 μmol/L. The range of cGMP transport reduction was 21.5% to 86.2% for progesterone analogues and 8.6% to 93.8 % for testosterone analogues. Three of the most potent test compounds (TC) of each analogue class, in addition to progesterone and testosterone, were characterized for concentrations from 1 nanomol/L to 1 mmol/L. The progesterone analogues showed following Ki-values (μmol/L): TC-08: 0.61, TC-16: 0.66 and TC-15: 9.3. The Ki-values (μmol/L) for the testosterone analogues were: TC-18: 0.10, TC-07: 0.67 andTC-05: 2.0. The present study shows that VLS may be a versatile tool in the development of membrane transport modulating agents (MTMAs).

Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach.

Optimization of lead structures is crucial for drug discovery. However, the accuracy of such a prediction using the traditional molecular docking approach remains a major concern. Our study demonstrates that the employment of quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) can improve the accuracy by and large. We select human aldose reductase at 0.66 Å, cyclin dependent kinase 2 at 2.0 Å and estrogen receptor β at 2.7 Å resolutions with active site environment ranging from highly hydrophilic to moderate to highly hydrophobic and several of their known ligands. Overall, the use of KEM-CP alongside the GoldScore resulted superior prediction than the GoldScore alone. Unlike GoldScore, the KEM-CP approach is neither environment-specific nor structural resolution dependent, which highlights its versatility. Further, the ranking of the ligands based on the KEM-CP results correlated well with that of the experimental IC50 values. This computationally inexpensive yet simple approach is expected to ease the process of virtual screening of potent ligands, and it would advance the drug discovery research.

Novel STAT3 small-molecule inhibitors identified by structure-based virtual ligand screening incorporating SH2 domain flexibility

Efforts to develop STAT3 inhibitors have focused on its SH2 domain starting with short phosphotyrosylated peptides based on STAT3 binding motifs, e.g. pY905LPQTV within gp130. Despite binding to STAT3 with high affinity, issues regarding stability, bioavailability, and membrane permeability of these peptides, as well as peptidomimetics such as CJ-887, have limited their further clinical development and led to interest in small-molecule inhibitors. Some small molecule STAT3 inhibitors, identified using structure-based virtual ligand screening (SB-VLS); while having favorable drug-like properties, suffer from weak binding affinities, possibly due to the high flexibility of the target domain. We conducted molecular dynamic (MD) simulations of the SH2 domain in complex with CJ-887, and used an averaged structure from this MD trajectory as an “induced-active site” receptor model for SB-VLS of 110,000 compounds within the SPEC database. Screening was followed by re-docking and re-scoring of the top 30% of hits, selection for hit compounds that directly interact with pY + 0 binding pocket residues R609 and S613, and testing for STAT3 targeting in vitro, which identified two lead hits with good activity and favorable drug-like properties. Unlike most small-molecule STAT3 inhibitors previously identified, which contain negatively-charged moieties that mediate binding to the pY + 0 binding pocket, these compounds are uncharged and likely will serve as better candidates for anti-STAT3 drug development. ImplicationsSB-VLS, using an averaged structure from molecular dynamics (MD) simulations of STAT3 SH2 domain in a complex with CJ-887, a known peptidomimetic binder, identify two highly potent, neutral, low-molecular weight STAT3-inhibitors with favorable drug-like properties.

Computational insights of phytochemical-driven disruption of RNA-dependent RNA polymerase-mediated replication of coronavirus: a strategic treatment plan against coronavirus disease 2019

The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised global health concerns. RNA-dependent RNA polymerase (RdRp) is the prime component of viral replication/proliferation machinery and is considered to be a potential drug target against SARS-CoV-2. The present study investigated the anti-RdRp activity of phytochemicals against SARS-CoV-2 infection. Virtual ligand screening was carried out to determine the potent compounds against RdRp. Molecular docking and an MD Simulation study were employed to evaluate the spatial affinity of selected phytochemicals for the active sites of RdRp. Structural stability of target compounds was determined using root mean square deviation computational analysis and drug-like abilities were investigated using ADMET. Bond distances between ligand and receptor were marked to predict the strength of interaction. Aloe, azadirachtin, columbin, cirsilineol, nimbiol, nimbocinol and sage exhibited the highest binding affinities and interacted with active sites of RdRp, surpassing the ability of chloroquine, lamivudine, favipiravir and remdesivir to target the same. All the natural metabolites exhibited stable conformation during MD Simulation of 101 ns at 310 K. Kinetic, potential and electrostatic energy were observed to be least in the case of natural metabolites in comparison with synthetic analogues. Deviations and fluctuations were observed to be structurally least in target phytochemicals. Physiochemical and biological properties of these compounds further validated their drug-like properties. Non-bonded distance was found to be short enough to form hydrogen bonding or hydrophobic interactions, which revealed that these target compounds can strongly bind with RdRp. The study found potential phytochemicals to disrupt the replication domain of SARS-CoV-2 by hindering RdRp. We therefore anticipate that the current findings could be considered as valuable for the development of an efficient preventive/therapeutic expedient against COVID-19.

FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening.

To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITEcomb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITEcomb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITEcomb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITEcomb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITEcomb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.

Rapid Identification of Inhibitors and Prediction of Ligand Selectivity for Multiple Proteins: Application to Protein Kinases.

Rapid identification of inhibitors for a family of proteins and prediction of ligand specificity are highly desirable for structure-based drug design. However, sequentially docking ligands into each protein target with conventional single-target docking methods is too computationally expensive to achieve these two goals, especially when the number of the targets is large. In this work, we use an efficient ensemble docking algorithm for simultaneous docking of ligands against multiple protein targets. We use protein kinases, a family of proteins that are highly important for many cellular processes and for rational drug design, as an example to demonstrate the feasibility of investigating ligand selectivity with this algorithm. Specifically, 14 human protein kinases were selected. First, native docking calculations were performed to test the ability of our energy scoring function to reproduce the experimentally determined structures of the ligand-protein kinase complexes. Next, cross-docking calculations were conducted using our ensemble docking algorithm to study ligand selectivity, based on the assumption that the native target of an inhibitor should have a more negative (i.e., favorable) energy score than the non-native targets. Staurosporine and Gleevec were studied as examples of nonselective and selective binding, respectively. Virtual ligand screening was also performed against five protein kinases that have at least seven known inhibitors. Our quantitative analysis of the results showed that the ensemble algorithm can be effective on screening for inhibitors and investigating their selectivities for multiple target proteins.

Ensemble-based high-throughput virtual screening of natural ligands using the Super Natural-II database against cell-wall protein dTDP-4-dehydrorhamnose reductase (RmlD) in Mycobacterium tuberculosis

The disease Tuberculosis (TB) is caused by a bacterium called Mycobacterium tuberculosis (Mtb). The bacterial cell-wall consists of peptidoglycan layer maintains the cellular integrity and cell viability. The main problem resides in the cell cycle of Mycobacterium tuberculosis in its quiescent form which is not targeted by any drugs hence there is an immediate need for new antibiotics to target the cell wall. The current study deals with the dTDP-4-dehydrorahmnose reductase (RmlD) which is the final enzyme in the series of cell-wall proteins of Mtb. The RmlD is a part of Carbohydrate biosynthesis has been considered as a good drug target for the novel class of antibiotics. Our study begins with the protein structure prediction, Homology studies were conducted using the Phyre2 web server. The structure is then refined and subjected to molecular dynamics simulations for 50 ns using GROMACS. The clustering analysis has been carried out and generated 41 clusters with 2 Å as the cut-off. Blind docking virtual screening was performed against RmlD protein using the Super Natural-II database with AutoDock4.0. its results helped to screen top ligands based on best binding energies. In both dockings, there are some common residues in which the ligands are interacting and forming the Hydrogen bonds such as Asp-105, Val-158, Thr-160, Gly-161, Arg-224, Arg-256. The ligand-567 giving the best results by being in the top-3 of all the clusters in both blind docking as well as the active-site docking. Hence ligand-567 can be a potential inhibitor of RmlD which can further inhibit the cell-wall synthesis of Mycobacterium tuberculosis. Communicated by Ramaswamy H. Sarma