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Ivet Bahar - One of the best experts on this subject based on the ideXlab platform.
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pharmmaker pharmacophore modeling and hit identification based on Druggability simulations
Protein Science, 2020Co-Authors: James Krieger, Hongchun Li, Ivet BaharAbstract:: Recent years have seen progress in Druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug-like probe molecules to characterize their drug-binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer-aided drug discovery, a systematic tool encompassing multiple steps from Druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top-ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure-based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker, can be used in conjunction with other tools available in ProDy.
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Druggability simulations and x ray crystallography reveal a ligand binding site in the glua3 ampa receptor n terminal domain
Structure, 2019Co-Authors: James Krieger, B Herguedas, Javier Garcianafria, Anindita Dutta, Saher A Shaikh, Ingo H Greger, Ivet BaharAbstract:Summary Ionotropic glutamate receptors (iGluRs) mediate the majority of excitatory neurotransmission in the brain. Their dysfunction is implicated in many neurological disorders, rendering iGluRs potential drug targets. Here, we performed a systematic analysis of the Druggability of two major iGluR subfamilies, using molecular dynamics simulations in the presence of drug-like molecules. We demonstrate the applicability of Druggability simulations by faithfully identifying known agonist and modulator sites on AMPA receptors (AMPARs) and NMDA receptors. Simulations produced the expected allosteric changes of the AMPAR ligand-binding domain in response to agonist. We also identified a novel ligand-binding site specific to the GluA3 AMPAR N-terminal domain (NTD), resulting from its unique conformational flexibility that we explored further with crystal structures trapped in vastly different states. In addition to providing an in-depth analysis into iGluR NTD dynamics, our approach identifies druggable sites and permits the determination of pharmacophoric features toward novel iGluR modulators.
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Druggability assessment of allosteric proteins by dynamics simulations in presence of probe molecules
Biophysical Journal, 2013Co-Authors: Ahmet Bakan, Neysa Nevins, Ami S Lakdawala, Ivet BaharAbstract:Druggability assessment of a target protein is an important concept in drug discovery. Target flexibility and allostery poses challenges to structure based binding site identification and Druggability assessment methods. We developed a simulation-based methodology for comprehensive analysis of the dynamics and binding properties of target proteins (1). Two distinguishing features of the methodology are: (i) simulation of the target in presence of a diversity of probe molecules selected upon analyzing functional groups on approved drugs, (ii) identification of druggable sites and estimation of corresponding maximal affinities based on the geometry and energetics of bound probe clusters. The use of the methodology for a variety of targets such as PTP1B, lymphocyte function-associated antigen 1, and vertebrate kinesin-5 (Eg5) provides shows that the method correctly captures the location and maximal affinities known bindings sites. It also provides insights into the target's structural changes that would accommodate, if not promote and stabilize, drug binding (2). Notably, the ability to identify high affinity spots even in challenging cases such as PTP1B or Eg5 shows promise as a rational tool for assessing the Druggability of protein targets, and identifying novel allosteric sites for drug binding. A thorough case study of cytochrome (cyt) c Druggability provides further support to the utility of the methodology for prospective targets with limited structural data. We identified the pocket facing heme and accessible in the partially unfolded form of cyt c as a sub-nanomolar druggable site. The first ever pharmacophore model developed for cyt c based on these simulations can distinguish known heme binders and is being advantageously used for discovering new drug-like inhibitors of cyt c.1. Bakan A et al. J Chem Theory Comput. 2012 8(7):2435-47.2. Bakan A, Bahar I. PNAS 2009 106(34):14349-54.
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Druggability of ionotropic glutamate receptor n terminal domains
Biophysical Journal, 2013Co-Authors: Anindita Dutta, Ahmet Bakan, Ivet BaharAbstract:Ionotropic glutamate receptors (iGluRs) are ligand-gated ion channels that mediate excitatory neurotransmission. All distinct classes of iGluRs (AMPA, NMDA, Kainate) harbor a ligand-binding domain (LBD), and an N-terminal domain (NTD) in their extracellular region. Glutamate binding to the LBD activates the ion channel. Allosteric modulation of iGluR activity by NMDAR NTD is well established; a similar function in AMPARs is still a matter of debate. Recently, we have shown that the bilobate architecture of iGluR NTDs favors similar global motions, and facilitates easy reconfiguration between AMPAR and NMDAR dimers [1,2]. This unexpected similarity in the intrinsic dynamics of these two subfamilies hints at the allosteric potential of non-NMDAR iGluRs and has propelled the evaluation of their “Druggability”. To achieve realistic detection of ligand-binding sites and their maximal binding affinities, we performed molecular dynamics (MD) simulations of iGluR extracellular domains in the presence of drug-like probe molecules and water. First, we benchmarked this method by exploring the well-known ligand-binding landscape of GluA2 LBD. We found that binding of one probe molecule in the endogenous ligand-binding site can drive domain closure necessary for channel activation. Subsequently, we explored the ligand-binding potential of all known iGluR NTDs. Our method captures with reasonable accuracy the known binding sites on NMDAR-GluN2B for modulators like Zn2+ and phenylethanolamine compounds, and furthermore provides insights into the chemical features and compound shape that may bind with better affinity than those known compounds. Another striking result from an extensive analysis of all iGluR NTDs is the accuracy with which the probe-binding hot spots overlap with known dimer interfaces of the monomers. This opens new avenues whereby we can accurately identify/predict druggable protein-protein interfaces.1. Dutta A, et al. (2012). Structure, in press2. Sukumaran, M et al. (2011). EMBO J, 30, 972-82.
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Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules
Journal of Chemical Theory and Computation, 2012Co-Authors: Ahmet Bakan, Neysa Nevins, Ami S Lakdawala, Ivet BaharAbstract:Druggability assessment of a target protein has emerged in recent years as an important concept in hit-to-lead optimization. A reliable and physically relevant measure of Druggability would allow informed decisions on the risk of investing in a particular target. Here, we define “Druggability” as a quantitative estimate of binding sites and affinities for a potential drug acting on a specific protein target. In the present study, we describe a new methodology that successfully predicts the Druggability and maximal binding affinity for a series of challenging targets, including those that function through allosteric mechanisms. Two distinguishing features of the methodology are (i) simulation of the binding dynamics of a diversity of probe molecules selected on the basis of an analysis of approved drugs and (ii) identification of druggable sites and estimation of corresponding binding affinities on the basis of an evaluation of the geometry and energetics of bound probe clusters. The use of the methodology...
Sandor Vajda - One of the best experts on this subject based on the ideXlab platform.
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structure based Druggability assessment of anti virulence targets from pseudomonas aeruginosa
Current Protein & Peptide Science, 2019Co-Authors: Thamires Quadros Froes, Regina L Baldini, Sandor Vajda, Marcelo Santos CastilhoAbstract:: Antimicrobial resistance (AMR) represents a serious threat to health and world economy. However, the interest in antibacterial drug development decreased significantly in the last decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Despite the fact that several macromolecular targets have been explored for this goal, their Druggability is a vital piece of information that has been overlooked. This review paper sheds some light on this subject by showing how structure-based freely available in silico tools, such as PockDrug and FTMap, might be useful to design novel inhibitors of the pyocyanin biosynthesis pathway as well as improve the potency/selectivity of compounds that target the P. aeruginosa quorum sensing mechanism. The information provided by hotspots analysis, along with binding site features reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also points out that in silico Druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, the assessment of anti-virulence drug targets should be carried out by complementary methods, such as FTMap/PockDrug combination, once the consensus Druggability classification reduces the chances of wasting money and time on undruggable proteins.
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kinase atlas Druggability analysis of potential allosteric sites in kinases
Journal of Medicinal Chemistry, 2019Co-Authors: Christine Yueh, Adrian Whitty, David Hall, Justin Rettenmaier, Andrey Alekseenko, Kathryn A Porter, Krister J Barkovich, Gyorgy M Keseru, James A Wells, Sandor VajdaAbstract:The inhibition of kinases has been pursued by the pharmaceutical industry for over 20 years. While the locations of the sites that bind type II and III inhibitors at or near the adenosine 5′-tripho...
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cryptic binding sites on proteins definition detection and Druggability
Current Opinion in Chemical Biology, 2018Co-Authors: Sandor Vajda, Megan Egbert, Amanda E Wakefield, Dmitri Beglov, Adrian WhittyAbstract:Many proteins in their unbound structures lack surface pockets appropriately sized for drug binding. Hence, a variety of experimental and computational tools have been developed for the identification of cryptic sites that are not evident in the unbound protein but form upon ligand binding, and can provide tractable drug target sites. The goal of this review is to discuss the definition, detection, and Druggability of such sites, and their potential value for drug discovery. Novel methods based on molecular dynamics simulations are particularly promising and yield a large number of transient pockets, but it has been shown that only a minority of such sites are generally capable of binding ligands with substantial affinity. Based on recent studies, current methodology can be improved by combining molecular dynamics with fragment docking and machine learning approaches.
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new frontiers in Druggability
Journal of Medicinal Chemistry, 2015Co-Authors: Dima Kozakov, David R Hall, Raeanne L Napoleon, Christine Yueh, Adrian Whitty, Sandor VajdaAbstract:A powerful early approach to evaluating the Druggability of proteins involved determining the hit rate in NMR-based screening of a library of small compounds. Here, we show that a computational analog of this method, based on mapping proteins using small molecules as probes, can reliably reproduce Druggability results from NMR-based screening and can provide a more meaningful assessment in cases where the two approaches disagree. We apply the method to a large set of proteins. The results show that, because the method is based on the biophysics of binding rather than on empirical parametrization, meaningful information can be gained about classes of proteins and classes of compounds beyond those resembling validated targets and conventionally druglike ligands. In particular, the method identifies targets that, while not druggable by druglike compounds, may become druggable using compound classes such as macrocycles or other large molecules beyond the rule-of-five limit.
Alan C Cheng - One of the best experts on this subject based on the ideXlab platform.
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structure based Druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility
PLOS Computational Biology, 2014Co-Authors: Kathryn Loving, Alan C ChengAbstract:Advances reported over the last few years and the increasing availability of protein crystal structure data have greatly improved structure-based Druggability approaches. However, in practice, nearly all Druggability estimation methods are applied to protein crystal structures as rigid proteins, with protein flexibility often not directly addressed. The inclusion of protein flexibility is important in correctly identifying the Druggability of pockets that would be missed by methods based solely on the rigid crystal structure. These include cryptic pockets and flexible pockets often found at protein-protein interaction interfaces. Here, we apply an approach that uses protein modeling in concert with Druggability estimation to account for light protein backbone movement and protein side-chain flexibility in protein binding sites. We assess the advantages and limitations of this approach on widely-used protein Druggability sets. Applying the approach to all mammalian protein crystal structures in the PDB results in identification of 69 proteins with potential druggable cryptic pockets.
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structure based maximal affinity model predicts small molecule Druggability
Nature Biotechnology, 2007Co-Authors: Alan C Cheng, Ryan G Coleman, Kathleen T Smyth, Patricia Soulard, Daniel R Caffrey, Anna C Salzberg, Enoch S HuangAbstract:Lead generation is a major hurdle in small-molecule drug discovery, with an estimated 60% of projects failing from lack of lead matter or difficulty in optimizing leads for drug-like properties. It would be valuable to identify these less-druggable targets before incurring substantial expenditure and effort. Here we show that a model-based approach using basic biophysical principles yields good prediction of Druggability based solely on the crystal structure of the target binding site. We quantitatively estimate the maximal affinity achievable by a drug-like molecule, and we show that these calculated values correlate with drug discovery outcomes. We experimentally test two predictions using high-throughput screening of a diverse compound collection. The collective results highlight the utility of our approach as well as strategies for tackling difficult targets.
Anneclaude Camproux - One of the best experts on this subject based on the ideXlab platform.
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global vision of Druggability issues applications and perspectives
Drug Discovery Today, 2017Co-Authors: Hiba Abi Hussein, Colette Geneix, Michel Petitjean, Alexandre Borrel, Delphine Flatters, Anneclaude CamprouxAbstract:During the preliminary stage of a drug discovery project, the lack of Druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational Druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported Druggability prediction methods mainly based on networks, statistical pocket Druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of Druggability prediction approaches, the remaining challenges and potential new drug development perspectives.
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pockdrug server a new web server for predicting pocket Druggability on holo and apo proteins
Nucleic Acids Research, 2015Co-Authors: Hiba Abi Hussein, Colette Geneix, Michel Petitjean, Alexandre Borrel, Leslie Regad, Anneclaude CamprouxAbstract:Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. Druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket Druggability investigations represent a key step of compound clinical progression projects. Currently computational Druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose ‘PockDrug-Server’ to predict pocket Druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent Druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent Druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr.
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pockdrug a model for predicting pocket Druggability that overcomes pocket estimation uncertainties
Journal of Chemical Information and Modeling, 2015Co-Authors: Michel Petitjean, Alexandre Borrel, Anneclaude Camproux, Leslie Regad, Henri XhaardAbstract:Predicting protein Druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods’ influence on Druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 A to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket Druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact Druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5–10%) than previous studies that used an identical apo set. In conclusion, this study co...
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druggable pockets and binding site centric chemical space a paradigm shift in drug discovery
Drug Discovery Today, 2010Co-Authors: Stephanie Perot, Anneclaude Camproux, Olivier Sperandio, Maria A Miteva, Bruno O VilloutreixAbstract:Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein–ligand complexes and discuss methods that assist binding site identification, prediction of Druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.
Ruth Brenk - One of the best experts on this subject based on the ideXlab platform.
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to hit or not to hit that is the question genome wide structure based Druggability predictions for pseudomonas aeruginosa proteins
PLOS ONE, 2015Co-Authors: Aurijit Sarkar, Ruth BrenkAbstract:Pseudomonas aeruginosa is a Gram-negative bacterium known to cause opportunistic infections in immune-compromised or immunosuppressed individuals that often prove fatal. New drugs to combat this organism are therefore sought after. To this end, we subjected the gene products of predicted perturbative genes to structure-based Druggability predictions using DrugPred. Making this approach suitable for large-scale predictions required the introduction of new methods for calculation of descriptors, development of a workflow to identify suitable pockets in homologous proteins and establishment of criteria to obtain valid Druggability predictions based on homologs. We were able to identify 29 perturbative proteins of P. aeruginosa that may contain druggable pockets, including some of them with no or no drug-like inhibitors deposited in ChEMBL. These proteins form promising novel targets for drug discovery against P. aeruginosa.
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drugpred a structure based approach to predict protein Druggability developed using an extensive nonredundant data set
Journal of Chemical Information and Modeling, 2011Co-Authors: Agata Krasowski, Aurijit Sarkar, Daniel Muthas, Stefan Schmitt, Ruth BrenkAbstract:Judging if a protein is able to bind orally available molecules with high affinity, i.e. if a protein is druggable, is an important step in target assessment. In order to derive a structure-based method to predict protein Druggability, a comprehensive, nonredundant data set containing crystal structures of 71 druggable and 44 less druggable proteins was compiled by literature search and data mining. This data set was subsequently used to train a structure-based Druggability predictor (DrugPred) using partial least-squares projection to latent structures discriminant analysis (PLS-DA). DrugPred performed well in discriminating druggable from less druggable binding sites for both internal and external predictions. The method is robust against conformational changes in the binding site and outperforms previously published methods. The superior performance of DrugPred is likely due to the size and composition of the training set which, in contrast to most previously developed methods, only contains cavities t...
