1
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Pallante L, Cannariato M, Androutsos L, Zizzi EA, Bompotas A, Hada X, Grasso G, Kalogeras A, Mavroudi S, Di Benedetto G, Theofilatos K, Deriu MA. VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites. Sci Rep 2024; 14:6296. [PMID: 38491261 PMCID: PMC10943019 DOI: 10.1038/s41598-024-56893-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.
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Affiliation(s)
- Lorenzo Pallante
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Marco Cannariato
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | | | - Eric A Zizzi
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Agorakis Bompotas
- Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece
| | - Xhesika Hada
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI, 6962, Lugano-Viganello, Switzerland
| | | | - Seferina Mavroudi
- Department of Nursing, School of Health Rehabilitation Sciences, University of Patras, 265 04, Patras, Greece
| | | | | | - Marco A Deriu
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy.
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2
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PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors. Molecules 2021; 26:molecules26092452. [PMID: 33922338 PMCID: PMC8122758 DOI: 10.3390/molecules26092452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/16/2021] [Accepted: 04/16/2021] [Indexed: 02/03/2023] Open
Abstract
Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.
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3
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Shan J, Pan X, Wang X, Xiao X, Ji C. FragRep: A Web Server for Structure-Based Drug Design by Fragment Replacement. J Chem Inf Model 2020; 60:5900-5906. [PMID: 33275427 DOI: 10.1021/acs.jcim.0c00767] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The design of efficient computational tools for structure-guided ligand design is essential for the drug discovery process. We hereby present FragRep, a new web server for structure-based ligand design by fragment replacement. The input is a protein and a ligand structure, either from protein data bank or from molecular docking. Users can choose specific substructures they want to modify. The server tries to find suitable fragments that not only meet the geometric requirements of the remaining part of the ligand but also fit well with local protein environments. FragRep is a powerful computational tool for the rapid generation of ligand design ideas; either in scaffold hopping or bioisosteric replacing. The FragRep Server is freely available to researchers and can be accessed at http://xundrug.cn/fragrep.
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Affiliation(s)
- Jinwen Shan
- Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062 China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062 China
| | - Xiaolin Pan
- Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062 China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062 China
| | - Xingyu Wang
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062 China
| | - Xudong Xiao
- Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062 China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062 China
| | - Changge Ji
- Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062 China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062 China
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4
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Novel potent (dihydro)benzofuranyl piperazines as human histamine receptor ligands - Functional characterization and modeling studies on H 3 and H 4 receptors. Bioorg Med Chem 2020; 30:115924. [PMID: 33333448 DOI: 10.1016/j.bmc.2020.115924] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/28/2020] [Accepted: 12/01/2020] [Indexed: 01/18/2023]
Abstract
Histamine acts through four different receptors (H1R-H4R), the H3R and H4R being the most explored in the last years as drug targets. The H3R is a potential target to treat narcolepsy, Parkinson's disease, epilepsy, schizophrenia and several other CNS-related conditions, while H4R blockade leads to anti-inflammatory and immunomodulatory effects. Our group has been exploring the dihydrobenzofuranyl-piperazines (LINS01 series) as human H3R/H4R ligands as potential drug candidates. In the present study, a set of 12 compounds were synthesized from adequate (dihydro)benzofuran synthons through simple reactions with corresponding piperazines, giving moderate to high yields. Four compounds (1b, 1f, 1g and 1h) showed high hH3R affinity (pKi > 7), compound 1h being the most potent (pKi 8.4), and compound 1f showed the best efficiency (pKi 8.2, LE 0.53, LLE 5.85). BRET-based assays monitoring Gαi activity indicated that the compounds are potent antagonists. Only one compound (2c, pKi 7.1) presented high affinity for hH4R. In contrast to what was observed for hH3R, it showed partial agonist activity. Docking experiments indicated that bulky substituents occupy a hydrophobic pocket in hH3R, while the N-allyl group forms favorable interactions with hydrophobic residues in the TM2, 3 and 7, increasing the selectivity towards hH3R. Additionally, the importance of the indole NH in the interaction with Glu5.46 from hH4R was confirmed by the modeling results, explaining the affinity and agonistic activity of compound 2c. The data reported in this work represent important findings for the rational design of future compounds for hH3R and hH4R.
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5
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Istyastono EP, Radifar M, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS. J Chem Inf Model 2020; 60:3697-3702. [PMID: 32687350 DOI: 10.1021/acs.jcim.0c00305] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We describe here our tool named PyPLIF HIPPOS, which was newly developed to analyze the docking results of AutoDock Vina and PLANTS. Its predecessor, PyPLIF (https://github.com/radifar/pyplif), is a molecular interaction fingerprinting tool for the docking results of PLANTS, exclusively. Unlike its predecessor, PyPLIF HIPPOS speeds up the computational times by separating the reference generation and docking analysis. PyPLIF HIPPOS also offers more options compared to PyPLIF. PyPLIF HIPPOS for Linux is stored as the Supporting Information in this application note and can be accessed in GitHub (https://github.com/radifar/PyPLIF-HIPPOS). Additionally, we present here the application of the tool in a retrospective structure-based virtual screening campaign targeting neuraminidase.
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Affiliation(s)
- Enade P Istyastono
- Faculty of Pharmacy, Sanata Dharma University, Campus 3, Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282, Indonesia.,Center for Molecular Modeling (molmod.org), Sinduadi, Mlati, Sleman, Yogyakarta 55284, Indonesia
| | - Muhammad Radifar
- Center for Molecular Modeling (molmod.org), Sinduadi, Mlati, Sleman, Yogyakarta 55284, Indonesia
| | - Nunung Yuniarti
- Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada, Depok, Sleman, Yogyakarta 55281, Indonesia
| | - Vivitri D Prasasty
- Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta 12930, Indonesia
| | - Sudi Mungkasi
- Department of Mathematics, Faculty of Science and Technology, Sanata Dharma University, Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282, Indonesia
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6
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Jaiteh M, Rodríguez-Espigares I, Selent J, Carlsson J. Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity. PLoS Comput Biol 2020; 16:e1007680. [PMID: 32168319 PMCID: PMC7135368 DOI: 10.1371/journal.pcbi.1007680] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/06/2020] [Accepted: 01/23/2020] [Indexed: 12/15/2022] Open
Abstract
Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.
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Affiliation(s)
- Mariama Jaiteh
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Ismael Rodríguez-Espigares
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
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7
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Wágner G, Mocking TAM, Arimont M, Provensi G, Rani B, Silva-Marques B, Latacz G, Da Costa Pereira D, Karatzidou C, Vischer HF, Wijtmans M, Kieć-Kononowicz K, de Esch IJP, Leurs R. 4-(3-Aminoazetidin-1-yl)pyrimidin-2-amines as High-Affinity Non-imidazole Histamine H 3 Receptor Agonists with in Vivo Central Nervous System Activity. J Med Chem 2019; 62:10848-10866. [PMID: 31675226 PMCID: PMC6912857 DOI: 10.1021/acs.jmedchem.9b01462] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Indexed: 12/19/2022]
Abstract
Despite the high diversity of histamine H3 receptor (H3R) antagonist/inverse agonist structures, partial or full H3R agonists have typically been imidazole derivatives. An in-house screening campaign intriguingly afforded the non-imidazole 4-(3-azetidin-1-yl)pyrimidin-2-amine 11b as a partial H3R agonist. Here, the design, synthesis, and structure-activity relationships of 11b analogues are described. This series yields several non-imidazole full agonists with potencies varying with the alkyl substitution pattern on the basic amine following the in vitro evaluation of H3R agonism using a cyclic adenosine monophosphate response element-luciferase reporter gene assay. The key compound VUF16839 (14d) combines nanomolar on-target activity (pKi = 8.5, pEC50 = 9.5) with weak activity on cytochrome P450 enzymes and good metabolic stability. The proposed H3R binding mode of 14d indicates key interactions similar to those attained by histamine. In vivo evaluation of 14d in a social recognition test in mice revealed an amnesic effect at 5 mg/kg intraperitoneally. The excellent in vitro and in vivo pharmacological profiles and the non-imidazole structure of 14d make it a promising tool compound in H3R research.
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Affiliation(s)
- Gábor Wágner
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Tamara A. M. Mocking
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Marta Arimont
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Gustavo Provensi
- Department of Neuroscience, Psychology,
Drug Research and Child Health,
Section of Pharmacology and Toxicology and Department of Health Sciences, University of Florence, Viale G. Pieraccini 6, CAP 50139 Florence, Italy
| | - Barbara Rani
- Department of Neuroscience, Psychology,
Drug Research and Child Health,
Section of Pharmacology and Toxicology and Department of Health Sciences, University of Florence, Viale G. Pieraccini 6, CAP 50139 Florence, Italy
| | - Bruna Silva-Marques
- Department of Neuroscience, Psychology,
Drug Research and Child Health,
Section of Pharmacology and Toxicology and Department of Health Sciences, University of Florence, Viale G. Pieraccini 6, CAP 50139 Florence, Italy
- Department
of Physiotherapy, Federal University of
São Carlos, Washington
Luís, km 235, SP-310 São Carlos, Brazil
| | - Gniewomir Latacz
- Department
of Technology and Biotechnology of Drugs, Medical College, Jagiellonian University, Medyczna 9, PL 30-688 Cracow, Poland
| | - Daniel Da Costa Pereira
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Christina Karatzidou
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Henry F. Vischer
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Maikel Wijtmans
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Katarzyna Kieć-Kononowicz
- Department
of Technology and Biotechnology of Drugs, Medical College, Jagiellonian University, Medyczna 9, PL 30-688 Cracow, Poland
| | - Iwan J. P. de Esch
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Rob Leurs
- Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
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8
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Structural insights into serotonin receptor ligands polypharmacology. Eur J Med Chem 2018; 151:797-814. [DOI: 10.1016/j.ejmech.2018.04.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Revised: 04/02/2018] [Accepted: 04/03/2018] [Indexed: 02/03/2023]
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9
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Computer-Aided Drug Design Approaches to Study Key Therapeutic Targets in Alzheimer’s Disease. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/978-1-4939-7404-7_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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10
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Sirci F, Napolitano F, Pisonero-Vaquero S, Carrella D, Medina DL, di Bernardo D. Comparing structural and transcriptional drug networks reveals signatures of drug activity and toxicity in transcriptional responses. NPJ Syst Biol Appl 2017; 3:23. [PMID: 28861278 PMCID: PMC5572457 DOI: 10.1038/s41540-017-0022-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 06/27/2017] [Accepted: 07/07/2017] [Indexed: 02/07/2023] Open
Abstract
We performed an integrated analysis of drug chemical structures and drug-induced transcriptional responses. We demonstrated that a network representing three-dimensional structural similarities among 5452 compounds can be used to automatically group together drugs with similar scaffolds, physicochemical parameters and mode-of-action. We compared the structural network to a network representing transcriptional similarities among a subset of 1309 drugs for which transcriptional response were available in the Connectivity Map data set. Analysis of structurally similar, but transcriptionally different drugs sharing the same MOA enabled us to detect and remove weak and noisy transcriptional responses, greatly enhancing the reliability of transcription-based approaches to drug discovery and drug repositioning. Cardiac glycosides exhibited the strongest transcriptional responses with a significant induction of pathways related to epigenetic regulation, which suggests an epigenetic mechanism of action for these drugs. Drug classes with the weakest transcriptional responses tended to induce expression of cytochrome P450 enzymes, hinting at drug-induced drug resistance. Analysis of transcriptionally similar, but structurally different drugs with unrelated MOA, led us to the identification of a 'toxic' transcriptional signature indicative of lysosomal stress (lysosomotropism) and lipid accumulation (phospholipidosis) partially masking the target-specific transcriptional effects of these drugs. We found that this transcriptional signature is shared by 258 compounds and it is associated to the activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy. Finally, we built a predictive Random Forest model of these 258 compounds based on 128 physicochemical parameters, which should help in the early identification of potentially toxic drug candidates. Transcriptional responses to drug treatment can reveal mechanism of action and off-target effects thus enabling drug repositioning, but only if measured in the appropriate cells at clinically relevant concentrations. A team led by Diego di Bernardo and Diego Medina generated a network representing structural similarities among compounds to automatically group together drugs with similar scaffolds and MOA. By comparing the structural drug network with a transcriptional drug network based on similarities in transcriptional response, the team observed broad differences between the two. This observation led to the identification of a transcriptional signature related lysosomal stress and phospholipidosis, and a physicochemical model to identify such compounds. These results provide general guidelines to prevent erroneous conclusion when using transcriptional responses of small molecules for drug discovery and drug repositioning
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Affiliation(s)
- Francesco Sirci
- Telethon Institute of Genetics and Medicine (TIGEM), System Biology and Bioinformatics lab. and High Content Screening facility, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Francesco Napolitano
- Telethon Institute of Genetics and Medicine (TIGEM), System Biology and Bioinformatics lab. and High Content Screening facility, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Sandra Pisonero-Vaquero
- Telethon Institute of Genetics and Medicine (TIGEM), System Biology and Bioinformatics lab. and High Content Screening facility, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Diego Carrella
- Telethon Institute of Genetics and Medicine (TIGEM), System Biology and Bioinformatics lab. and High Content Screening facility, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Diego L Medina
- Telethon Institute of Genetics and Medicine (TIGEM), System Biology and Bioinformatics lab. and High Content Screening facility, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine (TIGEM), System Biology and Bioinformatics lab. and High Content Screening facility, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy.,Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
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11
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Identification of Histamine H 3 Receptor Ligands Using a New Crystal Structure Fragment-based Method. Sci Rep 2017; 7:4829. [PMID: 28684785 PMCID: PMC5500575 DOI: 10.1038/s41598-017-05058-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 05/23/2017] [Indexed: 01/14/2023] Open
Abstract
Virtual screening offers an efficient alternative to high-throughput screening in the identification of pharmacological tools and lead compounds. Virtual screening is typically based on the matching of target structures or ligand pharmacophores to commercial or in-house compound catalogues. This study provides the first proof-of-concept for our recently reported method where pharmacophores are instead constructed based on the inference of residue-ligand fragments from crystal structures. We demonstrate its unique utility for G protein-coupled receptors, which represent the largest families of human membrane proteins and drug targets. We identified five neutral antagonists and one inverse agonist for the histamine H3 receptor with potencies of 0.7-8.5 μM in a recombinant receptor cell-based inositol phosphate accumulation assay and validated their activity using a radioligand competition binding assay. H3 receptor antagonism is of large therapeutic value and our ligands could serve as starting points for further lead optimisation. The six ligands exhibit four chemical scaffolds, whereof three have high novelty in comparison to the known H3 receptor ligands in the ChEMBL database. The complete pharmacophore fragment library is freely available through the GPCR database, GPCRdb, allowing the successful application herein to be repeated for most of the 285 class A GPCR targets. The method could also easily be adapted to other protein families.
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12
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Wang PF, Qiu HY, Wang ZF, Zhang YJ, Wang ZC, Li DD, Zhu HL. Identification of novel B-RafV600E inhibitors employing FBDD strategy. Biochem Pharmacol 2017; 132:63-76. [DOI: 10.1016/j.bcp.2017.02.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Accepted: 02/28/2017] [Indexed: 01/27/2023]
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13
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Drwal MN, Jacquemard C, Perez C, Desaphy J, Kellenberger E. Do Fragments and Crystallization Additives Bind Similarly to Drug-like Ligands? J Chem Inf Model 2017; 57:1197-1209. [PMID: 28414463 DOI: 10.1021/acs.jcim.6b00769] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The success of fragment-based drug design (FBDD) hinges upon the optimization of low-molecular-weight compounds (MW < 300 Da) with weak binding affinities to lead compounds with high affinity and selectivity. Usually, structural information from fragment-protein complexes is used to develop ideas about the binding mode of similar but drug-like molecules. In this regard, crystallization additives such as cryoprotectants or buffer components, which are highly abundant in crystal structures, are frequently ignored. Thus, the aim of this study was to investigate the information present in protein complexes with fragments as well as those with additives and how they relate to the binding modes of their drug-like counterparts. We present a thorough analysis of the binding modes of crystallographic additives, fragments, and drug-like ligands bound to four diverse targets of wide interest in drug discovery and highly represented in the Protein Data Bank: cyclin-dependent kinase 2, β-secretase 1, carbonic anhydrase 2, and trypsin. We identified a total of 630 unique molecules bound to the catalytic binding sites, among them 31 additives, 222 fragments, and 377 drug-like ligands. In general, we observed that, independent of the target, protein-fragment interaction patterns are highly similar to those of drug-like ligands and mostly cover the residues crucial for binding. Crystallographic additives are also able to show conserved binding modes and recover the residues important for binding in some of the cases. Moreover, we show evidence that the information from fragments and drug-like ligands can be applied to rescore docking poses in order to improve the prediction of binding modes.
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Affiliation(s)
- Malgorzata N Drwal
- Laboratoire d'Innovation Thérapeutique UMR 7200, CNRS-Université de Strasbourg , 74 Route du Rhin, 674000 Illkirch, France
| | - Célien Jacquemard
- Laboratoire d'Innovation Thérapeutique UMR 7200, CNRS-Université de Strasbourg , 74 Route du Rhin, 674000 Illkirch, France
| | - Carlos Perez
- Eli Lilly Research Laboratories , Avenida de la Industria 30, 28108 Alcobendas, Madrid, Spain
| | - Jérémy Desaphy
- Lilly Research Laboratories, Eli Lilly and Company , Lilly Corporate Center, Indianapolis, Indiana 46285, United States
| | - Esther Kellenberger
- Laboratoire d'Innovation Thérapeutique UMR 7200, CNRS-Université de Strasbourg , 74 Route du Rhin, 674000 Illkirch, France
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14
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Bartuzi D, Kaczor AA, Targowska-Duda KM, Matosiuk D. Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors. Molecules 2017; 22:molecules22020340. [PMID: 28241450 PMCID: PMC6155844 DOI: 10.3390/molecules22020340] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 01/27/2017] [Accepted: 02/15/2017] [Indexed: 12/16/2022] Open
Abstract
The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.
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Affiliation(s)
- Damian Bartuzi
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
- School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211 Kuopio, Finland.
| | | | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
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15
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Strasser A, Wittmann HJ. Molecular Modelling Approaches for the Analysis of Histamine Receptors and Their Interaction with Ligands. Handb Exp Pharmacol 2017; 241:31-61. [PMID: 28110354 DOI: 10.1007/164_2016_113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Several experimental techniques to analyse histamine receptors are available, e.g. pharmacological characterisation of known or new compounds by different types of assays or mutagenesis studies. To obtain insights into the histamine receptors on a molecular and structural level, crystal structures have to be determined and molecular modelling studies have to be performed. It is widely accepted to generate homology models of the receptor of interest based on an appropriate crystal structure as a template and to refine the resulting models by molecular dynamic simulations. A lot of modelling techniques, e.g. docking, QSAR or interaction fingerprint methods, are used to predict binding modes of ligands and pharmacological data, e.g. affinity or even efficacy. However, within the last years, molecular dynamic simulations got more and more important: First of all, molecular dynamic simulations are very helpful to refine the binding mode of a ligand to a histamine receptor, obtained by docking studies. Furthermore, with increasing computational performance it got possible to simulate complete binding pathways of ions or ligands from the aqueous extracellular phase into the allosteric or orthosteric binding pocket of histamine receptors.
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Affiliation(s)
- Andrea Strasser
- Department of Pharmaceutical/Medicinal Chemistry II, Institute of Pharmacy, University of Regensburg, Universitäts-Str. 31, Regensburg, 93040, Germany.
| | - Hans-Joachim Wittmann
- Department of Pharmaceutical/Medicinal Chemistry II, Institute of Pharmacy, University of Regensburg, Universitäts-Str. 31, Regensburg, 93040, Germany
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16
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Buonerba F, Lepri S, Goracci L, Schindler BD, Seo SM, Kaatz GW, Cruciani G. Improved Potency of Indole-Based NorA Efflux Pump Inhibitors: From Serendipity toward Rational Design and Development. J Med Chem 2016; 60:517-523. [PMID: 27977195 DOI: 10.1021/acs.jmedchem.6b01281] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The NorA efflux pump is a potential drug target for reversal of resistance to selected antibacterial agents, and recently we described indole-based inhibitor candidates. Herein we report a second class of inhibitors derived from them but with significant differences in shape and size. In particular, compounds 13 and 14 are very potent inhibitors in that they demonstrated the lowest IC50 values (2 μM) ever observed among all indole-based compounds we have evaluated.
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Affiliation(s)
- Federica Buonerba
- Department of Chemistry, Biology and Biotechnology, University of Perugia , 06123 Perugia, Italy
| | - Susan Lepri
- Department of Chemistry, Biology and Biotechnology, University of Perugia , 06123 Perugia, Italy
| | - Laura Goracci
- Department of Chemistry, Biology and Biotechnology, University of Perugia , 06123 Perugia, Italy
| | - Bryan D Schindler
- The John D. Dingell Department of Veterans Affairs Medical Center , Detroit, Michigan 48201, United States
| | - Susan M Seo
- The John D. Dingell Department of Veterans Affairs Medical Center , Detroit, Michigan 48201, United States
| | - Glenn W Kaatz
- The John D. Dingell Department of Veterans Affairs Medical Center , Detroit, Michigan 48201, United States.,Department of Internal Medicine, Division of Infectious Diseases, Wayne State University School of Medicine , Detroit, Michigan 48201, United States
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, University of Perugia , 06123 Perugia, Italy
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17
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Kiss R, Keserű GM. Structure-based discovery and binding site analysis of histamine receptor ligands. Expert Opin Drug Discov 2016; 11:1165-1185. [DOI: 10.1080/17460441.2016.1245288] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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18
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Molecular interaction fingerprint approaches for GPCR drug discovery. Curr Opin Pharmacol 2016; 30:59-68. [PMID: 27479316 DOI: 10.1016/j.coph.2016.07.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Revised: 07/11/2016] [Accepted: 07/12/2016] [Indexed: 01/23/2023]
Abstract
Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.
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19
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Nikolic K, Mavridis L, Djikic T, Vucicevic J, Agbaba D, Yelekci K, Mitchell JBO. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies. Front Neurosci 2016; 10:265. [PMID: 27375423 PMCID: PMC4901078 DOI: 10.3389/fnins.2016.00265] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/25/2016] [Indexed: 11/13/2022] Open
Abstract
HIGHLIGHTSMany CNS targets are being explored for multi-target drug design New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds QSAR, virtual screening and docking methods increase the potential of rational drug design
The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D1-R/D2-R/5-HT2A-R/H3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.
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Affiliation(s)
- Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Lazaros Mavridis
- School of Biological and Chemical Sciences, Queen Mary University of London London, UK
| | - Teodora Djikic
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University Istanbul, Turkey
| | - Jelica Vucicevic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Danica Agbaba
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Kemal Yelekci
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University Istanbul, Turkey
| | - John B O Mitchell
- EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews St Andrews, UK
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20
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Vucicevic J, Srdic-Rajic T, Pieroni M, Laurila JMM, Perovic V, Tassini S, Azzali E, Costantino G, Glisic S, Agbaba D, Scheinin M, Nikolic K, Radi M, Veljkovic N. A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin. Bioorg Med Chem 2016; 24:3174-83. [PMID: 27265687 DOI: 10.1016/j.bmc.2016.05.043] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Revised: 05/17/2016] [Accepted: 05/20/2016] [Indexed: 10/21/2022]
Abstract
The clonidine-like central antihypertensive agent rilmenidine, which has high affinity for I1-type imidazoline receptors (I1-IR) was recently found to have cytotoxic effects on cultured cancer cell lines. However, due to its pharmacological effects resulting also from α2-adrenoceptor activation, rilmenidine cannot be considered a suitable anticancer drug candidate. Here, we report the identification of novel rilmenidine-derived compounds with anticancer potential and devoid of α2-adrenoceptor effects by means of ligand- and structure-based drug design approaches. Starting from a large virtual library, eleven compounds were selected, synthesized and submitted to biological evaluation. The most active compound 5 exhibited a cytotoxic profile similar to that of rilmenidine, but without appreciable affinity to α2-adrenoceptors. In addition, compound 5 significantly enhanced the apoptotic response to doxorubicin, and may thus represent an important tool for the development of better adjuvant chemotherapeutic strategies for doxorubicin-insensitive cancers.
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Affiliation(s)
- Jelica Vucicevic
- Institute of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - Tatjana Srdic-Rajic
- Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia
| | - Marco Pieroni
- P4T Group, Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Jonne M M Laurila
- Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Kiinamyllynkatu 10, FI-20520 Turku, Finland
| | - Vladimir Perovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, POB 522, Mihaila Petrovica Alasa 14, 11001 Belgrade, Serbia
| | - Sabrina Tassini
- P4T Group, Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Elisa Azzali
- P4T Group, Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Gabriele Costantino
- P4T Group, Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy
| | - Sanja Glisic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, POB 522, Mihaila Petrovica Alasa 14, 11001 Belgrade, Serbia
| | - Danica Agbaba
- Institute of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - Mika Scheinin
- Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Kiinamyllynkatu 10, FI-20520 Turku, Finland; Unit of Clinical Pharmacology, Turku University Hospital, Turku, Finland
| | - Katarina Nikolic
- Institute of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia.
| | - Marco Radi
- P4T Group, Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze, 27/A, 43124 Parma, Italy.
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, POB 522, Mihaila Petrovica Alasa 14, 11001 Belgrade, Serbia.
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21
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Spyrakis F, Cavasotto CN. Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. Arch Biochem Biophys 2015; 583:105-19. [DOI: 10.1016/j.abb.2015.08.002] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Revised: 08/03/2015] [Accepted: 08/03/2015] [Indexed: 01/05/2023]
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22
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GPCR crystal structures: Medicinal chemistry in the pocket. Bioorg Med Chem 2015; 23:3880-906. [DOI: 10.1016/j.bmc.2014.12.034] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Revised: 12/12/2014] [Accepted: 12/16/2014] [Indexed: 12/20/2022]
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23
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Schultes S, Kooistra AJ, Vischer HF, Nijmeijer S, Haaksma EEJ, Leurs R, de Esch IJP, de Graaf C. Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors. J Chem Inf Model 2015; 55:1030-44. [PMID: 25815783 DOI: 10.1021/ci500694c] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.
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Affiliation(s)
- Sabine Schultes
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Albert J Kooistra
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Henry F Vischer
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Saskia Nijmeijer
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Eric E J Haaksma
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Rob Leurs
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Iwan J P de Esch
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Chris de Graaf
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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24
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Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J Mol Recognit 2015; 28:581-604. [PMID: 25808539 DOI: 10.1002/jmr.2471] [Citation(s) in RCA: 159] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Revised: 01/16/2015] [Accepted: 02/05/2015] [Indexed: 12/11/2022]
Abstract
Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.
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Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Jessica Holien
- ACRF Rational Drug Discovery Centre and Structural Biology Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Victoria, 3065, Australia
| | - Paul A Ramsland
- Centre for Biomedical Research, Burnet Institute, Melbourne, Victoria, 3004, Australia.,Department of Surgery Austin Health, University of Melbourne, Melbourne, Victoria, 3084, Australia.,Department of Immunology, Monash University, Alfred Medical Research and Education Precinct, Melbourne, Victoria, 3004, Australia.,School of Biomedical Sciences, CHIRI Biosciences, Curtin University, Perth, Western Australia, 6845, Australia
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25
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Schmidt D, Bernat V, Brox R, Tschammer N, Kolb P. Identifying modulators of CXC receptors 3 and 4 with tailored selectivity using multi-target docking. ACS Chem Biol 2015; 10:715-24. [PMID: 25398025 DOI: 10.1021/cb500577j] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The G protein-coupled receptors of the C-X-C subfamily form a group among the chemokine receptors whose endogenous ligands are peptides with a common Cys-X-Cys motif. The CXC chemokine receptors 3 and 4 (CXCR3, CXCR4), which are investigated in this study, are linked to severe diseases such as cancer, multiple sclerosis, and HIV infections. Of particular interest, this receptor pair potentially forms a target for a polypharmacological drug treatment. Considering known ligands from public databases, such dual binders have not been identified yet. We therefore applied large-scale docking to the structure of CXCR4 and a homology model of CXCR3 with the goal to predict such dual binders, as well as compounds selective for either one of the receptors. Using signaling and biochemical assays, we showed that more than 50% of these predictions were correct in each category, yielding ligands with excellent binding efficiencies. These results highlight that docking is a suitable tool for the identification of ligands with tailored binding profiles to GPCRs, even when using homology models. More importantly, we present novel CXCR3-CXCR4 dual modulators that might pave the road to understanding the mechanisms of polypharmacological inhibition of these receptors.
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Affiliation(s)
| | | | - Regine Brox
- Friedrich-Alexander-University, Erlangen, Germany
| | | | - Peter Kolb
- Philipps-University, Marburg, Germany
- LOEWE Center for Synthetic Microbiology (Synmikro), Marburg, Germany
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26
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Siragusa L, Cross S, Baroni M, Goracci L, Cruciani G. BioGPS: Navigating biological space to predict polypharmacology, off-targeting, and selectivity. Proteins 2015; 83:517-32. [DOI: 10.1002/prot.24753] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Revised: 12/09/2014] [Accepted: 12/13/2014] [Indexed: 12/12/2022]
Affiliation(s)
- Lydia Siragusa
- Laboratory for Chemometrics and Molecular Modeling, Department of Chemistry, Biology and Biotechnology; University of Perugia; Perugia 06123 Italy
| | - Simon Cross
- Molecular Discovery Limited; Pinner, Middlesex, London HA5 5NE United Kingdom
| | - Massimo Baroni
- Molecular Discovery Limited; Pinner, Middlesex, London HA5 5NE United Kingdom
| | - Laura Goracci
- Laboratory for Chemometrics and Molecular Modeling, Department of Chemistry, Biology and Biotechnology; University of Perugia; Perugia 06123 Italy
| | - Gabriele Cruciani
- Laboratory for Chemometrics and Molecular Modeling, Department of Chemistry, Biology and Biotechnology; University of Perugia; Perugia 06123 Italy
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27
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Cavasotto CN, Palomba D. Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models. Chem Commun (Camb) 2015; 51:13576-94. [DOI: 10.1039/c5cc05050b] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.
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Affiliation(s)
- Claudio N. Cavasotto
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) - CONICET - Partner Institute of the Max Planck Society
- Buenos Aires
- Argentina
| | - Damián Palomba
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) - CONICET - Partner Institute of the Max Planck Society
- Buenos Aires
- Argentina
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28
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Abstract
Fragment-based drug design has proved itself as a powerful technique for increasing the sampling and diversity of chemical space and enabling the design of novel leads and compounds. Computational techniques for identifying fragments, binding sites and particularly for linking, growing, and evolving fragments play a significant role in the process. Information from ADME studies and clustering property information in the form of toxicophores and chemotypes can play a significant role in aiding the design of novel, selective fragments with good activity profiles.
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Affiliation(s)
- Rachelle J Bienstock
- Independent Researcher and Consultant, 300 Pitch Pine Lane, Chapel Hill, NC, 27514, USA,
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29
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Fidom K, Isberg V, Hauser AS, Mordalski S, Lehto T, Bojarski AJ, Gloriam DE. A new crystal structure fragment-based pharmacophore method for G protein-coupled receptors. Methods 2015; 71:104-12. [DOI: 10.1016/j.ymeth.2014.09.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 09/09/2014] [Accepted: 09/26/2014] [Indexed: 01/07/2023] Open
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30
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Istyastono EP, Kooistra AJ, Vischer HF, Kuijer M, Roumen L, Nijmeijer S, Smits RA, de Esch IJP, Leurs R, de Graaf C. Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor. MEDCHEMCOMM 2015. [DOI: 10.1039/c5md00022j] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Structure-based virtual screening using H1R- and β2R-based histamine H4R homology models identified 9 fragments with an affinity ranging from 0.14 to 6.3 μm for H4R.
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Affiliation(s)
- Enade P. Istyastono
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Albert J. Kooistra
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Henry F. Vischer
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Martien Kuijer
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Luc Roumen
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Saskia Nijmeijer
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | | | - Iwan J. P. de Esch
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Rob Leurs
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Chris de Graaf
- Division of Medicinal Chemistry
- Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
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Pappalardo M, Shachaf N, Basile L, Milardi D, Zeidan M, Raiyn J, Guccione S, Rayan A. Sequential application of ligand and structure based modeling approaches to index chemicals for their hH4R antagonism. PLoS One 2014; 9:e109340. [PMID: 25330207 PMCID: PMC4199621 DOI: 10.1371/journal.pone.0109340] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 09/10/2014] [Indexed: 02/03/2023] Open
Abstract
The human histamine H4 receptor (hH4R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH4R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH4R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH4R bioactivity. An application of the prediction model on external test set composed of more than 160 hH4R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼ 4000 chemicals highly indexed as H4R antagonists' candidates. Next, a series of 3D models of hH4R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH4R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.
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Affiliation(s)
- Matteo Pappalardo
- Department of Chemical Sciences, University of Catania, Catania, Italy
| | - Nir Shachaf
- Drug Discovery Informatics Lab, QRC-Qasemi Research Center, Al-Qasemi Academic College, Baka El-Garbiah, Israel
| | - Livia Basile
- Etnalead s.r.l., Scuola Superiore di Catania, University of Catania, Catania, Italy
| | - Danilo Milardi
- National Research Council, Institute of Biostructures and Bioimaging, Catania, Italy
| | - Mouhammed Zeidan
- Drug Discovery Informatics Lab, QRC-Qasemi Research Center, Al-Qasemi Academic College, Baka El-Garbiah, Israel
| | - Jamal Raiyn
- Drug Discovery Informatics Lab, QRC-Qasemi Research Center, Al-Qasemi Academic College, Baka El-Garbiah, Israel
| | - Salvatore Guccione
- Etnalead s.r.l., Scuola Superiore di Catania, University of Catania, Catania, Italy
- Department of Pharmaceutical Sciences, University of Catania, Catania, Italy
| | - Anwar Rayan
- Drug Discovery Informatics Lab, QRC-Qasemi Research Center, Al-Qasemi Academic College, Baka El-Garbiah, Israel
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Vass M, Ágai-Csongor É, Horti F, Keserű GM. Multiple fragment docking and linking in primary and secondary pockets of dopamine receptors. ACS Med Chem Lett 2014; 5:1010-4. [PMID: 25221658 DOI: 10.1021/ml500201u] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2014] [Accepted: 07/10/2014] [Indexed: 01/17/2023] Open
Abstract
A sequential docking methodology was applied to computationally predict starting points for fragment linking using the human dopamine D3 receptor crystal structure and a human dopamine D2 receptor homology model. Two focused fragment libraries were docked in the primary and secondary binding sites, and best fragment combinations were enumerated. Similar top scoring fragments were found for the primary site, while secondary site fragments were predicted to convey selectivity. Three linked compounds were synthesized that had 9-, 39-, and 55-fold selectivity in favor of D3 and the subtype selectivity of the compounds was assessed on a structural basis.
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Affiliation(s)
- Márton Vass
- Gedeon Richter Plc, Gyömrői
út 19-21, H-1103 Budapest, Hungary
| | | | - Ferenc Horti
- Gedeon Richter Plc, Gyömrői
út 19-21, H-1103 Budapest, Hungary
| | - György M. Keserű
- Research
Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok
körútja 2, H-1117 Budapest, Hungary
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Kooistra AJ, Kuhne S, de Esch IJP, Leurs R, de Graaf C. A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design. Br J Pharmacol 2014; 170:101-26. [PMID: 23713847 DOI: 10.1111/bph.12248] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Revised: 04/26/2013] [Accepted: 05/03/2013] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND AND PURPOSE Chemogenomics focuses on the discovery of new connections between chemical and biological space leading to the discovery of new protein targets and biologically active molecules. G-protein coupled receptors (GPCRs) are a particularly interesting protein family for chemogenomics studies because there is an overwhelming amount of ligand binding affinity data available. The increasing number of aminergic GPCR crystal structures now for the first time allows the integration of chemogenomics studies with high-resolution structural analyses of GPCR-ligand complexes. EXPERIMENTAL APPROACH In this study, we have combined ligand affinity data, receptor mutagenesis studies, and amino acid sequence analyses to high-resolution structural analyses of (hist)aminergic GPCR-ligand interactions. This integrated structural chemogenomics analysis is used to more accurately describe the molecular and structural determinants of ligand affinity and selectivity in different key binding regions of the crystallized aminergic GPCRs, and histamine receptors in particular. KEY RESULTS Our investigations highlight interesting correlations and differences between ligand similarity and ligand binding site similarity of different aminergic receptors. Apparent discrepancies can be explained by combining detailed analysis of crystallized or predicted protein-ligand binding modes, receptor mutation studies, and ligand structure-selectivity relationships that identify local differences in essential pharmacophore features in the ligand binding sites of different receptors. CONCLUSIONS AND IMPLICATIONS We have performed structural chemogenomics studies that identify links between (hist)aminergic receptor ligands and their binding sites and binding modes. This knowledge can be used to identify structure-selectivity relationships that increase our understanding of ligand binding to (hist)aminergic receptors and hence can be used in future GPCR ligand discovery and design.
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Affiliation(s)
- A J Kooistra
- Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems, VU University Amsterdam, The Netherlands
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Nijmeijer S, Vischer HF, Sirci F, Schultes S, Engelhardt H, de Graaf C, Rosethorne EM, Charlton SJ, Leurs R. Detailed analysis of biased histamine H₄ receptor signalling by JNJ 7777120 analogues. Br J Pharmacol 2014; 170:78-88. [PMID: 23351115 DOI: 10.1111/bph.12117] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Revised: 12/26/2012] [Accepted: 01/02/2013] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND AND PURPOSE The histamine H₄ receptor, originally thought to signal merely through Gαi proteins, has recently been shown to also recruit and signal via β-arrestin2. Following the discovery that the reference antagonist indolecarboxamide JNJ 7777120 appears to be a partial agonist in β-arrestin2 recruitment, we have identified additional biased hH₄R ligands that preferentially couple to Gαi or β-arrestin2 proteins. In this study, we explored ligand and receptor regions that are important for biased hH₄R signalling. EXPERIMENTAL APPROACH We evaluated a series of 48 indolecarboxamides with subtle structural differences for their ability to induce hH₄R-mediated Gαi protein signalling or β-arrestin2 recruitment. Subsequently, a Fingerprints for Ligands and Proteins three-dimensional quantitative structure-activity relationship analysis correlated intrinsic activity values with structural ligand requirements. Moreover, a hH₄R homology model was used to identify receptor regions important for biased hH₄R signalling. KEY RESULTS One indolecarboxamide (75) with a nitro substituent on position R7 of the aromatic ring displayed an equal preference for the Gαi and β-arrestin2 pathway and was classified as unbiased hH₄R ligand. The other 47 indolecarboxamides were β-arrestin2-biased agonists. Intrinsic activities of the unbiased as well as β-arrestin2-biased indolecarboxamides to induce β-arrestin2 recruitment could be correlated with different ligand features and hH₄R regions. CONCLUSION AND IMPLICATIONS Small structural modifications resulted in diverse intrinsic activities for unbiased (75) and β-arrestin2-biased indolecarboxamides. Analysis of ligand and receptor features revealed efficacy hotspots responsible for biased-β-arrestin2 recruitment. This knowledge is useful for the design of hH₄R ligands with biased intrinsic activities and aids our understanding of the mechanism of H₄R activation.
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Affiliation(s)
- S Nijmeijer
- Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems, VU University Amsterdam, The Netherlands
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Kooistra AJ, de Graaf C, Timmerman H. The receptor concept in 3D: from hypothesis and metaphor to GPCR-ligand structures. Neurochem Res 2014; 39:1850-61. [PMID: 25103230 DOI: 10.1007/s11064-014-1398-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Revised: 07/21/2014] [Accepted: 07/22/2014] [Indexed: 12/17/2022]
Abstract
The first mentioning of the word "receptor" for the structure with which a bioactive compound should react for obtaining its specific influence on a physiological system goes back to the years around 1900. The receptor concept was adapted from the lock and key theory for the enzyme substrate and blockers interactions. Through the years the concept, in the beginning rather being a metaphor, not a model, was refined and became reality in recent years. Not only the structures of receptors were elucidated, also the receptor machineries were unraveled. Following a brief historical review we will describe how the recent breakthroughs in the experimental determination of G protein-coupled receptor (GPCR) crystal structures can be complemented by computational modeling, medicinal chemistry, biochemical, and molecular pharmacological studies to obtain new insights into the molecular determinants of GPCR-ligand binding and activation. We will furthermore discuss how this information can be used for structure-based discovery of novel GPCR ligands that bind specific (allosteric) binding sites with desired effects on GPCR functional activity.
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Affiliation(s)
- Albert J Kooistra
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV, Amsterdam, The Netherlands
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Structure-based discovery of selective serotonin 5-HT(1B) receptor ligands. Structure 2014; 22:1140-1151. [PMID: 25043551 DOI: 10.1016/j.str.2014.05.017] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 05/05/2014] [Accepted: 05/27/2014] [Indexed: 01/23/2023]
Abstract
The development of safe and effective drugs relies on the discovery of selective ligands. Serotonin (5-hydroxytryptamine [5-HT]) G protein-coupled receptors are therapeutic targets for CNS disorders but are also associated with adverse drug effects. The determination of crystal structures for the 5-HT1B and 5-HT2B receptors provided an opportunity to identify subtype selective ligands using structure-based methods. From docking screens of 1.3 million compounds, 22 molecules were predicted to be selective for the 5-HT1B receptor over the 5-HT2B subtype, a requirement for safe serotonergic drugs. Nine compounds were experimentally verified as 5-HT1B-selective ligands, with up to 300-fold higher affinities for this subtype. Three of the ligands were agonists of the G protein pathway. Analysis of state-of-the-art homology models of the two 5-HT receptors revealed that the crystal structures were critical for predicting selective ligands. Our results demonstrate that structure-based screening can guide the discovery of ligands with specific selectivity profiles.
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Siragusa L, Spyrakis F, Goracci L, Cross S, Cruciani G. BioGPS: The Music for the Chemo- and Bioinformatics Walzer. Mol Inform 2014; 33:446-53. [DOI: 10.1002/minf.201400028] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Accepted: 05/19/2014] [Indexed: 01/09/2023]
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Vass M, Schmidt É, Horti F, Keserű GM. Virtual fragment screening on GPCRs: a case study on dopamine D3 and histamine H4 receptors. Eur J Med Chem 2014; 77:38-46. [PMID: 24607587 DOI: 10.1016/j.ejmech.2014.02.034] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Revised: 02/11/2014] [Accepted: 02/13/2014] [Indexed: 01/05/2023]
Abstract
Prospective structure based virtual fragment screening methodologies on two GPCR targets namely the dopamine D3 and the histamine H4 receptors with a library of 12,905 fragments were evaluated. Fragments were docked to the X-ray structure and the homology model of the D3 and H4 receptors, respectively. Representative receptor conformations for ensemble docking were obtained from molecular dynamics trajectories. In vitro confirmed hit rates ranged from 16% to 32%. Hits had high ligand efficiency (LE) values in the range of 0.31-0.74 and also acceptable lipophilic efficiency. The X-ray structure, the homology model and structural ensembles were all found suitable for docking based virtual screening of fragments against these GPCRs. However, there was little overlap among different hit sets and methodologies were thus complementary to each other.
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Affiliation(s)
- Márton Vass
- Gedeon Richter Plc, H-1475, P.O.B. 27, Budapest, Hungary
| | - Éva Schmidt
- Gedeon Richter Plc, H-1475, P.O.B. 27, Budapest, Hungary
| | - Ferenc Horti
- Gedeon Richter Plc, H-1475, P.O.B. 27, Budapest, Hungary
| | - György M Keserű
- Research Centre for Natural Sciences of the Hungarian Academy of Sciences, H-1525, P.O.B. 17, Budapest, Hungary.
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From Three-Dimensional GPCR Structure to Rational Ligand Discovery. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 796:129-57. [DOI: 10.1007/978-94-007-7423-0_7] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Medina-Franco JL, Méndez-Lucio O, Martinez-Mayorga K. The Interplay Between Molecular Modeling and Chemoinformatics to Characterize Protein–Ligand and Protein–Protein Interactions Landscapes for Drug Discovery. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 96:1-37. [DOI: 10.1016/bs.apcsb.2014.06.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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42
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Andrews SP, Brown GA, Christopher JA. Structure-Based and Fragment-Based GPCR Drug Discovery. ChemMedChem 2013; 9:256-75. [DOI: 10.1002/cmdc.201300382] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Revised: 11/15/2013] [Indexed: 01/05/2023]
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Spyrakis F, Singh R, Cozzini P, Campanini B, Salsi E, Felici P, Raboni S, Benedetti P, Cruciani G, Kellogg GE, Cook PF, Mozzarelli A. Isozyme-specific ligands for O-acetylserine sulfhydrylase, a novel antibiotic target. PLoS One 2013; 8:e77558. [PMID: 24167577 PMCID: PMC3805590 DOI: 10.1371/journal.pone.0077558] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2013] [Accepted: 09/03/2013] [Indexed: 01/06/2023] Open
Abstract
The last step of cysteine biosynthesis in bacteria and plants is catalyzed by O-acetylserine sulfhydrylase. In bacteria, two isozymes, O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B, have been identified that share similar binding sites, although the respective specific functions are still debated. O-acetylserine sulfhydrylase plays a key role in the adaptation of bacteria to the host environment, in the defense mechanisms to oxidative stress and in antibiotic resistance. Because mammals synthesize cysteine from methionine and lack O-acetylserine sulfhydrylase, the enzyme is a potential target for antimicrobials. With this aim, we first identified potential inhibitors of the two isozymes via a ligand- and structure-based in silico screening of a subset of the ZINC library using FLAP. The binding affinities of the most promising candidates were measured in vitro on purified O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B from Salmonella typhimurium by a direct method that exploits the change in the cofactor fluorescence. Two molecules were identified with dissociation constants of 3.7 and 33 µM for O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B, respectively. Because GRID analysis of the two isoenzymes indicates the presence of a few common pharmacophoric features, cross binding titrations were carried out. It was found that the best binder for O-acetylserine sulfhydrylase-B exhibits a dissociation constant of 29 µM for O-acetylserine sulfhydrylase-A, thus displaying a limited selectivity, whereas the best binder for O-acetylserine sulfhydrylase-A exhibits a dissociation constant of 50 µM for O-acetylserine sulfhydrylase-B and is thus 8-fold selective towards the former isozyme. Therefore, isoform-specific and isoform-independent ligands allow to either selectively target the isozyme that predominantly supports bacteria during infection and long-term survival or to completely block bacterial cysteine biosynthesis.
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Affiliation(s)
| | - Ratna Singh
- Department of Pharmacy, University of Parma, Parma, Italy
| | - Pietro Cozzini
- Department of Food Sciences, University of Parma, Parma, Italy
- National Institute of Biostructures and Biosystems, Rome, Italy
| | - Barbara Campanini
- Department of Pharmacy, University of Parma, Parma, Italy
- * E-mail: (BC); (AM)
| | - Enea Salsi
- Department of Pharmacy, University of Parma, Parma, Italy
| | - Paolo Felici
- Department of Pharmacy, University of Parma, Parma, Italy
| | - Samanta Raboni
- Department of Pharmacy, University of Parma, Parma, Italy
| | | | | | - Glen E. Kellogg
- Department of Medicinal Chemistry and Institute for Structural Biology and Drug Discovery, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Paul F. Cook
- Department of Biochemistry, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Andrea Mozzarelli
- Department of Pharmacy, University of Parma, Parma, Italy
- National Institute of Biostructures and Biosystems, Rome, Italy
- * E-mail: (BC); (AM)
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Chen D, Ranganathan A, IJzerman AP, Siegal G, Carlsson J. Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A(2A) adenosine receptor. J Chem Inf Model 2013; 53:2701-14. [PMID: 23971943 DOI: 10.1021/ci4003156] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Fragment-based lead discovery (FBLD) is becoming an increasingly important method in drug development. We have explored the potential to complement NMR-based biophysical screening of chemical libraries with molecular docking in FBLD against the A(2A) adenosine receptor (A(2A)AR), a drug target for inflammation and Parkinson's disease. Prior to an NMR-based screen of a fragment library against the A(2A)AR, molecular docking against a crystal structure was used to rank the same set of molecules by their predicted affinities. Molecular docking was able to predict four out of the five orthosteric ligands discovered by NMR among the top 5% of the ranked library, suggesting that structure-based methods could be used to prioritize among primary hits from biophysical screens. In addition, three fragments that were top-ranked by molecular docking, but had not been picked up by the NMR-based method, were demonstrated to be A(2A)AR ligands. While biophysical approaches for fragment screening are typically limited to a few thousand compounds, the docking screen was extended to include 328,000 commercially available fragments. Twenty-two top-ranked compounds were tested in radioligand binding assays, and 14 of these were A(2A)AR ligands with K(i) values ranging from 2 to 240 μM. Optimization of fragments was guided by molecular dynamics simulations and free energy calculations. The results illuminate strengths and weaknesses of molecular docking and demonstrate that this method can serve as a valuable complementary tool to biophysical screening in FBLD.
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Affiliation(s)
- Dan Chen
- ZoBio BV , 2300RA Leiden, The Netherlands
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Visegrády A, Keserű GM. Fragment-based lead discovery on G-protein-coupled receptors. Expert Opin Drug Discov 2013; 8:811-20. [PMID: 23621346 DOI: 10.1517/17460441.2013.794135] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION G-protein-coupled receptors (GPCRs) form one of the largest groups of potential targets for novel medications. Low druggability of many GPCR targets and inefficient sampling of chemical space in high-throughput screening expertise however often hinder discovery of drug discovery leads for GPCRs. Fragment-based drug discovery is an alternative approach to the conventional strategy and has proven its efficiency on several enzyme targets. Based on developments in biophysical screening techniques, receptor stabilization and in vitro assays, virtual and experimental fragment screening and fragment-based lead discovery recently became applicable for GPCR targets. AREAS COVERED This article provides a review of the biophysical as well as biological detection techniques suitable to study GPCRs together with their applications to screen fragment libraries and identify fragment-size ligands of cell surface receptors. The article presents several recent examples including both virtual and experimental protocols for fragment hit discovery and early hit to lead progress. EXPERT OPINION With the recent progress in biophysical detection techniques, the advantages of fragment-based drug discovery could be exploited for GPCR targets. Structural information on GPCRs will be more abundantly available for early stages of drug discovery projects, providing information on the binding process and efficiently supporting the progression of fragment hit to lead. In silico approaches in combination with biological assays can be used to address structurally challenging GPCRs and confirm biological relevance of interaction early in the drug discovery project.
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de Graaf C, Vischer HF, de Kloe GE, Kooistra AJ, Nijmeijer S, Kuijer M, Verheij MHP, England PJ, van Muijlwijk-Koezen JE, Leurs R, de Esch IJP. Small and colorful stones make beautiful mosaics: fragment-based chemogenomics. Drug Discov Today 2012; 18:323-30. [PMID: 23266367 DOI: 10.1016/j.drudis.2012.12.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2012] [Revised: 11/19/2012] [Accepted: 12/05/2012] [Indexed: 12/01/2022]
Abstract
Smaller stones with a wide variety of colors make a higher resolution mosaic. In much the same way, smaller chemical entities that are structurally diverse are better able to interrogate protein binding sites. This feature article describes the construction of a diverse fragment library and an analysis of the screening of six representative protein targets belonging to three diverse target classes (G protein-coupled receptors ADRB2, H1R, H3R, and H4R, the ligand-gated ion channel 5-HT3R, and the kinase PKA) using chemogenomics approaches. The integration of experimentally determined bioaffinity profiles across related and unrelated protein targets and chemogenomics analysis of fragment binding and protein structure allow the identification of: (i) unexpected similarities and differences in ligand binding properties, and (ii) subtle ligand affinity and selectivity cliffs. With a wealth of fragment screening data being generated in industry and academia, such approaches will contribute to a more detailed structural understanding of ligand-protein interactions.
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Affiliation(s)
- Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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