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Showing papers on "Docking (molecular) published in 2011"


Journal ArticleDOI
TL;DR: This review presents a brief introduction of the available molecular docking methods, and their development and applications in drug discovery, and a recently developed local move Monte Carlo based approach is introduced.
Abstract: Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.

1,787 citations


Journal ArticleDOI
TL;DR: A fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field is presented.
Abstract: The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch.

1,370 citations


Journal ArticleDOI
TL;DR: MM/GBSA performs well for both binding pose predictions and binding free‐energy estimations and is efficient to re‐score the top‐hit poses produced by other less‐accurate scoring functions.
Abstract: In molecular docking, it is challenging to develop a scoring function that is accurate to conduct high-throughput screenings. Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) in molecular docking studies. Here, we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein-ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric constant 2.0, produced a Spearman's correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/GBSA performs well for both binding pose predictions and binding free-energy estimations and is efficient to re-score the top-hit poses produced by other less-accurate scoring functions.

604 citations


Journal ArticleDOI
TL;DR: This work is the first large‐scale docking evaluation that covers both aspects of docking programs, that is, predicting ligand conformation and calculating the strength of its binding, and observed the lack of universal scoring function for all types of molecules and protein families.
Abstract: Docking is one of the most commonly used techniques in drug design. It is used for both identifying correct poses of a ligand in the binding site of a protein as well as for the estimation of the strength of protein-ligand interaction. Because millions of compounds must be screened, before a suitable target for biological testing can be identified, all calculations should be done in a reasonable time frame. Thus, all programs currently in use exploit empirically based algorithms, avoiding systematic search of the conformational space. Similarly, the scoring is done using simple equations, which makes it possible to speed up the entire process. Therefore, docking results have to be verified by subsequent in vitro studies. The purpose of our work was to evaluate seven popular docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) on the extensive dataset composed of 1300 protein-ligands complexes from PDBbind 2007 database, where experimentally measured binding affinity values were also available. We compared independently the ability of proper posing [according to Root mean square deviation (or Root mean square distance) of predicted conformations versus the corresponding native one] and scoring (by calculating the correlation between docking score and ligand binding strength). To our knowledge, it is the first large-scale docking evaluation that covers both aspects of docking programs, that is, predicting ligand conformation and calculating the strength of its binding. More than 1000 protein-ligand pairs cover a wide range of different protein families and inhibitor classes. Our results clearly showed that the ligand binding conformation could be identified in most cases by using the existing software, yet we still observed the lack of universal scoring function for all types of molecules and protein families.

325 citations


Journal ArticleDOI
TL;DR: The determination of the D3 receptor structure, and a community challenge to predict it, enabled a fully prospective comparison of ligand discovery from a modeled structure versus that of the subsequently released crystal structure.
Abstract: G protein-coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the determination of the first crystal structures, interest in structure-based ligand discovery increased. Unfortunately, for most GPCRs no experimental structures are available. The determination of the D(3) receptor structure and the challenge to the community to predict it enabled a fully prospective comparison of ligand discovery from a modeled structure versus that of the subsequently released crystal structure. Over 3.3 million molecules were docked against a homology model, and 26 of the highest ranking were tested for binding. Six had affinities ranging from 0.2 to 3.1 μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compounds selected, five had affinities ranging from 0.3 to 3.0 μM. One of the new ligands from the homology model screen was optimized for affinity to 81 nM. The feasibility of docking screens against modeled GPCRs more generally is considered.

286 citations


Journal ArticleDOI
TL;DR: The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors and provides guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the G PCR universe.

274 citations


Journal ArticleDOI
25 Mar 2011-PLOS ONE
TL;DR: The results indicate that the performance of the HADDOCK-CPORT combination is competitive with ZDOCK-ZRANK, a state-of-the-art ab initio docking/scoring combination, and the original interface predictions could be further improved by interface post-prediction (contact analysis of the docking solutions).
Abstract: Background: Macromolecular complexes are the molecular machines of the cell. Knowledge at the atomic level is essential to understand and influence their function. However, their number is huge and a significant fraction is extremely difficult to study using classical structural methods such as NMR and X-ray crystallography. Therefore, the importance of large-scale computational approaches in structural biology is evident. This study combines two of these computational approaches, interface prediction and docking, to obtain atomic-level structures of protein-protein complexes, starting from their unbound components. Methodology/Principal Findings: Here we combine six interface prediction web servers into a consensus method called CPORT (Consensus Prediction Of interface Residues in Transient complexes). We show that CPORT gives more stable and reliable predictions than each of the individual predictors on its own. A protocol was developed to integrate CPORT predictions into our data-driven docking program HADDOCK. For cases where experimental information is limited, this prediction-driven docking protocol presents an alternative to ab initio docking, the docking of complexes without the use of any information. Prediction-driven docking was performed on a large and diverse set of protein-protein complexes in a blind manner. Our results indicate that the performance of the HADDOCK-CPORT combination is competitive with ZDOCKZRANK, a state-of-the-art ab initio docking/scoring combination. Finally, the original interface predictions could be further improved by interface post-prediction (contact analysis of the docking solutions). Conclusions/Significance: The current study shows that blind, prediction-driven docking using CPORT and HADDOCK is competitive with ab initio docking methods. This is encouraging since prediction-driven docking represents the absolute bottom line for data-driven docking: any additional biological knowledge will greatly improve the results obtained by prediction-driven docking alone. Finally, the fact that original interface predictions could be further improved by interface post-prediction suggests that prediction-driven docking has not yet been pushed to the limit. A web server for CPORT is freely available at http://haddock.chem.uu.nl/services/CPORT.

266 citations


Journal ArticleDOI
29 Apr 2011-PLOS ONE
TL;DR: The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.
Abstract: Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.

252 citations


Journal ArticleDOI
TL;DR: This work quantitatively predict binding energies for small molecules that bind different RNA conformations and reports the de novo discovery of six compounds that bind TAR with high affinity and inhibit its interaction with a Tat peptide in vitro.
Abstract: Current approaches used to identify protein-binding small molecules are not suited for identifying small molecules that can bind emerging RNA drug targets. By docking small molecules onto an RNA dynamic ensemble constructed by combining NMR spectroscopy and computational molecular dynamics, we virtually screened small molecules that target the entire structure landscape of the transactivation response element (TAR) from HIV type 1 (HIV-1). We quantitatively predict binding energies for small molecules that bind different RNA conformations and report the de novo discovery of six compounds that bind TAR with high affinity and inhibit its interaction with a Tat peptide in vitro (K(i) values of 710 nM-169 μM). One compound binds HIV-1 TAR with marked selectivity and inhibits Tat-mediated activation of the HIV-1 long terminal repeat by 81% in T-cell lines and HIV replication in an HIV-1 indicator cell line (IC(50) ∼23.1 μM).

229 citations


Journal ArticleDOI
TL;DR: This paper shows how the use of support vector machines (SVMs), trained by associating sets of individual energy terms retrieved from molecular docking with the known binding affinity of each compound from high-throughput screening experiments, can be used to improve the correlation between known binding affinities and those predicted by the docking program eHiTS.
Abstract: Docking scoring functions are notoriously weak predictors of binding affinity. They typically assign a common set of weights to the individual energy terms that contribute to the overall energy score; however, these weights should be gene family dependent. In addition, they incorrectly assume that individual interactions contribute toward the total binding affinity in an additive manner. In reality, noncovalent interactions often depend on one another in a nonlinear manner. In this paper, we show how the use of support vector machines (SVMs), trained by associating sets of individual energy terms retrieved from molecular docking with the known binding affinity of each compound from high-throughput screening experiments, can be used to improve the correlation between known binding affinities and those predicted by the docking program eHiTS. We construct two prediction models: a regression model trained using IC(50) values from BindingDB, and a classification model trained using active and decoy compounds from the Directory of Useful Decoys (DUD). Moreover, to address the issue of overrepresentation of negative data in high-throughput screening data sets, we have designed a multiple-planar SVM training procedure for the classification model. The increased performance that both SVMs give when compared with the original eHiTS scoring function highlights the potential for using nonlinear methods when deriving overall energy scores from their individual components. We apply the above methodology to train a new scoring function for direct inhibitors of Mycobacterium tuberculosis (M.tb) InhA. By combining ligand binding site comparison with the new scoring function, we propose that phosphodiesterase inhibitors can potentially be repurposed to target M.tb InhA. Our methodology may be applied to other gene families for which target structures and activity data are available, as demonstrated in the work presented here.

177 citations


Journal ArticleDOI
TL;DR: It is demonstrated that the action of the potent anticancer compound MKT-077 occurs through a differential interaction with Hsp70 allosteric states, and is therefore an "allosteric drug" using NMR spectroscopy.

Journal ArticleDOI
25 Oct 2011-PLOS ONE
TL;DR: The hypothesis that the chosen binders can inhibit the downstream signaling activity of Ras is confirmed and it is proposed that the predicted allosteric sites are viable targets for the development and optimization of new drugs.
Abstract: Aberrant Ras activity is a hallmark of diverse cancers and developmental diseases. Unfortunately, conventional efforts to develop effective small molecule Ras inhibitors have met with limited success. We have developed a novel multi-level computational approach to discover potential inhibitors of previously uncharacterized allosteric sites. Our approach couples bioinformatics analysis, advanced molecular simulations, ensemble docking and initial experimental testing of potential inhibitors. Molecular dynamics simulation highlighted conserved allosteric coupling of the nucleotide-binding switch region with distal regions, including loop 7 and helix 5. Bioinformatics methods identified novel transient small molecule binding pockets close to these regions and in the vicinity of the conformationally responsive switch region. Candidate binders for these pockets were selected through ensemble docking of ZINC and NCI compound libraries. Finally, cell-based assays confirmed our hypothesis that the chosen binders can inhibit the downstream signaling activity of Ras. We thus propose that the predicted allosteric sites are viable targets for the development and optimization of new drugs.

Journal ArticleDOI
TL;DR: A series of new and novel Schiff base derivatives were synthesized and investigated as potential new inhibitors of Jack bean urease and it was observed that both share the same binding mode.

Journal ArticleDOI
TL;DR: New arylhydrazone derivatives and a series of 1,5-diphenyl pyrazoles were designed and synthesized from 1-(4-chlorophenyl)-4,4, 4-trifuorobutane-1,3-dione and investigated in vivo for their anti-inflammatory activities and revealed a similar binding mode to SC-558, a selective COX-2 inhibitor.

Journal ArticleDOI
TL;DR: The very low level of in vitro HIV inhibition, in comparison to predicted EC(50) values on the basis of computational studies, during CEM-GFP screening using AZT as positive control indicated that probably the HIV RT is not the viral target and the molecules work through some different mechanism.

Journal ArticleDOI
TL;DR: Experimental data is provided suggesting that two of the most prominent natural products associated with DNA methylation inhibition, (−)-epigallocathechin-3-gallate (EGCG) and curcumin, have little or no pharmacologically relevant inhibitory activity.
Abstract: DNA methyltransferases (DNMTs) represent promising targets for the development of unique anticancer drugs. However, all DNMT inhibitors currently in clinical use are nonselective cytosine analogs with significant cytotoxic side-effects. Several natural products, covering diverse chemical classes, have indicated DNMT inhibitory activity, but these effects have yet to be systematically evaluated. In this study, we provide experimental data suggesting that two of the most prominent natural products associated with DNA methylation inhibition, (−)-epigallocathechin-3-gallate (EGCG) and curcumin, have little or no pharmacologically relevant inhibitory activity. We therefore conducted a virtual screen of a large database of natural products with a validated homology model of the catalytic domain of DNMT1. The virtual screening focused on a lead-like subset of the natural products docked with DNMT1, using three docking programs, following a multistep docking approach. Prior to docking, the lead-like subset was characterized in terms of chemical space coverage and scaffold content. Consensus hits with high predicted docking affinity for DNMT1 by all three docking programs were identified. One hit showed DNMT1 inhibitory activity in a previous study. The virtual screening hits were located within the biological-relevant chemical space of drugs, and represent potential unique DNMT inhibitors of natural origin. Validation of these virtual screening hits is warranted.

Journal ArticleDOI
TL;DR: In this paper, Δ2-isoxazoline constrained analogues of procaine/procainamide (7a-k and 8a−k) were tested and their inhibitory activity against DNA methyltransferase 1 (DNMT1) was tested.
Abstract: A series of Δ2-isoxazoline constrained analogues of procaine/procainamide (7a–k and 8a–k) were prepared and their inhibitory activity against DNA methyltransferase 1 (DNMT1) was tested. Among them, derivative 7b is far more potent in vitro (IC50 = 150 μM) than other non-nucleoside inhibitors and also exhibits a strong and dose-dependent antiproliferative effect against HCT116 human colon carcinoma cells. The binding mode of 7b with the enzyme was also investigated by means of a simple competition assay as well as of docking simulations conducted using the recently published crystallographic structure of human DNMT1. On the basis of the findings, we assessed that the mode of inhibition of 7b is consistent with a competition with the cofactor and propose it as a novel lead compound for the development of non-nucleoside DNMT inhibitors.

Journal ArticleDOI
01 Jul 2011
TL;DR: The SVM model was integrated to a free, web-based prediction platform and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.
Abstract: Motivation: Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform ( http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated. Contact: eszter.hazai@virtuadrug.com Supplementary information:Supplementary data are available at Bioinformatics online.

Journal ArticleDOI
17 Aug 2011-PLOS ONE
TL;DR: The results are consistent with previous docking studies and provide experimental support for a predicted function of TMC207 in mimicking key residues in the proton transfer chain and blocking rotary movement of subunit c during catalysis.
Abstract: Infections with Mycobacterium tuberculosis are substantially increasing on a worldwide scale and new antibiotics are urgently needed to combat concomitantly emerging drug-resistant mycobacterial strains. The diarylquinoline TMC207 is a highly promising drug candidate for treatment of tuberculosis. This compound kills M. tuberculosis by binding to a new target, mycobacterial ATP synthase. In this study we used biochemical assays and binding studies to characterize the interaction between TMC207 and ATP synthase. We show that TMC207 acts independent of the proton motive force and does not compete with protons for a common binding site. The drug is active on mycobacterial ATP synthesis at neutral and acidic pH with no significant change in affinity between pH 5.25 and pH 7.5, indicating that the protonated form of TMC207 is the active drug entity. The interaction of TMC207 with ATP synthase can be explained by a one-site binding mechanism, the drug molecule thus binds to a defined binding site on ATP synthase. TMC207 affinity for its target decreases with increasing ionic strength, suggesting that electrostatic forces play a significant role in drug binding. Our results are consistent with previous docking studies and provide experimental support for a predicted function of TMC207 in mimicking key residues in the proton transfer chain and blocking rotary movement of subunit c during catalysis. Furthermore, the high affinity of TMC207 at low proton motive force and low pH values may in part explain the exceptional ability of this compound to efficiently kill mycobacteria in different microenvironments.

Journal ArticleDOI
Juan Du1, Huijun Sun1, Lili Xi1, Jiazhong Li1, Ying Yang1, Huanxiang Liu1, Xiaojun Yao1 
TL;DR: The molecular docking combined with Prime/MM–GBSA simulation can not only be used to rapidly and accurately predict the binding‐free energy of novel Chk1 inhibitors but also provide a novel strategy for lead discovery and optimization targeting Chk 1.
Abstract: Developing chemicals that inhibit checkpoint kinase 1 (Chk1) is a promising adjuvant therapeutic to improve the efficacy and selectivity of DNA-targeting agents. Reliable prediction of binding-free energy and binding affinity of Chk1 inhibitors can provide a guide for rational drug design. In this study, multiple docking strategies and Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA) calculation were applied to predict the binding mode and free energy for a series of benzoisoquinolinones as Chk1 inhibitors. Reliable docking results were obtained using induced-fit docking and quantum mechanics/molecular mechanics (QM/MM) docking, which showed superior performance on both ligand binding pose and docking score accuracy to the rigid-receptor docking. Then, the Prime/MM-GBSA method based on the docking complex was used to predict the binding-free energy. The combined use of QM/MM docking and Prime/MM-GBSA method could give a high correlation between the predicted binding-free energy and experimentally determined pIC(50). The molecular docking combined with Prime/MM-GBSA simulation can not only be used to rapidly and accurately predict the binding-free energy of novel Chk1 inhibitors but also provide a novel strategy for lead discovery and optimization targeting Chk1. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 2800-2808, 2011

Journal ArticleDOI
TL;DR: A computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions.
Abstract: Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.

Journal ArticleDOI
TL;DR: A 5-μM docking hit has been optimized to an extraordinarily potent (55 pM) non-nucleoside inhibitor of HIV reverse transcriptase by identifying optimal substitution patterns for phenyl rings and a linker.
Abstract: A 5-μM docking hit has been optimized to an extraordinarily potent (55 pM) non-nucleoside inhibitor of HIV reverse transcriptase. Use of free energy perturbation (FEP) calculations to predict relative free energies of binding aided the optimizations by identifying optimal substitution patterns for phenyl rings and a linker. The most potent resultant catechol diethers feature terminal uracil and cyanovinylphenyl groups. A halogen bond with Pro95 likely contributes to the extreme potency of compound 42. In addition, several examples are provided illustrating failures of attempted grafting of a substructure from a very active compound onto a seemingly related scaffold to improve its activity.

Journal ArticleDOI
TL;DR: Results of biophysical mapping of the adenosine A2A receptor are presented, which identify key protein–ligand interactions and allow the shape of the site to be refined to produce a high quality three-dimensional picture of ligand binding, thereby facilitating structure based drug design.
Abstract: A new approach to generating information on ligand receptor interactions within the binding pocket of G protein-coupled receptors has been developed, called Biophysical Mapping (BPM). Starting from a stabilized receptor (StaR), minimally engineered for thermostability, additional single mutations are then added at positions that could be involved in small molecule interactions. The StaR and a panel of binding site mutants are captured onto Biacore chips to enable characterization of the binding of small molecule ligands using surface plasmon resonance (SPR) measurement. A matrix of binding data for a set of ligands versus each active site mutation is then generated, providing specific affinity and kinetic information (K(D), k(on), and k(off)) of receptor-ligand interactions. This data set, in combination with molecular modeling and docking, is used to map the small molecule binding site for each class of compounds. Taken together, the many constraints provided by these data identify key protein-ligand interactions and allow the shape of the site to be refined to produce a high quality three-dimensional picture of ligand binding, thereby facilitating structure based drug design. Results of biophysical mapping of the adenosine A(2A) receptor are presented.

Journal ArticleDOI
TL;DR: A robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures that predicts structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes withknown structures and binding affinities.
Abstract: We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well...

Journal ArticleDOI
TL;DR: SDM and automated docking predicted that maraviroc inserts deeply in CCR5 transmembrane cavity where it can occupy three different binding sites, whereas CCL3 and gp120 lie on distinct yet overlapped regions of the C CR5 extracellular loop 2, suggesting that receptor dimerization might represent a target for new CCR4 entry inhibitors.

Journal ArticleDOI
TL;DR: The small molecule binding site in the Hsp90 C-terminal domain was revealed by protease fingerprinting and photoaffinity labeling utilizing LC-MS/MS and the resulting model for the bioactive conformation of NB bound to Hsp 90α is presented herein.
Abstract: The Hsp90 chaperone machine is required for the folding, activation, and/or stabilization of more than 50 proteins directly related to malignant progression. Hsp90 contains small molecule binding sites at both its N- and C-terminal domains; however, limited structural and biochemical data regarding the C-terminal binding site is available. In this report, the small molecule binding site in the Hsp90 C-terminal domain was revealed by protease fingerprinting and photoaffinity labeling utilizing LC–MS/MS. The identified site was characterized by generation of a homology model for hHsp90α using the SAXS open structure of HtpG and docking the bioactive conformation of NB into the generated model. The resulting model for the bioactive conformation of NB bound to Hsp90α is presented herein.

Journal ArticleDOI
TL;DR: A virtual screening exercise for a purine riboswitch was conducted to probe the strengths and weaknesses of RNA-ligand docking, indicating that this approach is a promising option to explore the wealth of RNA structures for structure-based ligand design.

Journal ArticleDOI
TL;DR: A series of new 1,3, 4-oxadiazole derivatives containing 1,4-benzodioxan moiety (6a-6s) as potential telomerase inhibitors were synthesized and showed compound 6k possessed the most potent telomersase activity.

Journal ArticleDOI
TL;DR: Using fluorescence spectroscopy, it is shown that curcumin binds tubulin 32 Å away from the colchicine-binding site, and structure-activity studies suggest that the tridented nature of compound 7 is responsible for its higher affinity for tubulin compared toCurcumin.
Abstract: Although curcumin is known for its anticarcinogenic properties, the exact mechanism of its action or the identity of the target receptor is not completely understood. Studies on a series of curcumin analogues, synthesized to investigate their tubulin binding affinities and tubulin self-assembly inhibition, showed that: (i) curcumin acts as a bifunctional ligand, (ii) analogues with substitution at the diketone and acetylation of the terminal phenolic groups of curcumin are less effective, (iii) a benzylidiene derivative, compound 7, is more effective than curcumin in inhibiting tubulin self-assembly. Cell-based studies also showed compound 7 to be more effective than curcumin. Using fluorescence spectroscopy we show that curcumin binds tubulin 32 A away from the colchicine-binding site. Docking studies also suggests that the curcumin-binding site to be close to the vinblastine-binding site. Structure–activity studies suggest that the tridented nature of compound 7 is responsible for its higher affinity fo...

Journal ArticleDOI
TL;DR: A novel class of CA inhibitors, interacting with the CA isozymes I, II and IX, XII (transmembrane, tumor-associated) in a different manner, is reported here, offering the possibility to design CAIs with an interesting inhibition profile compared to the clinically used sulfonamides/sulfamates.