1
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Boulhissa I, Boucherit H, Chikhi A, Bensegueni A. Docking of T6361 Analogues as Potential Inhibitors of E.coli MurA Followed by ADME-Toxicity Study. Curr Drug Discov Technol 2024; 21:1-8. [PMID: 37929742 DOI: 10.2174/0115701638244582231025110143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 08/16/2023] [Accepted: 09/25/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Developing more potent antibacterial agents is one of the most important tasks of scientists in the health field due to the problem of antibiotic resistance. Among the antibiotic targets, we mention MurA (UDP-N-Acetylglucosamine Enolpyruvyl Transferase), which is a key enzyme of peptidoglycan biosynthesis of the bacterial cell wall. OBJECTIVE Our objective was to search for new inhibitors of the bacterial enzyme MurA by docking the analogues of its inhibitor T6361, a derivative of 5-sulfonoxy-anthranilic acid. METHODS 990 analogues of T6361 were docked in the first binding site of E.coli MurA (open form) using the FlexX program, and the ADME-Toxicity profile of the best ones was evaluated by SwissADME and PreADMET web servers. . RESULTS Docking results revealed two T6361 analogues to provide better energy scores than T6361, and have similar interactions with the binding site of E.coli MurA namely,3-{[2-(piperidine-1-carbonyl) phenyl]sulfamoyl}benzoic acid and 3-{[2-(pyrrolidine-1 carbonyl)phenyl]sulfamoyl}benzoic acid. Moreover, the two molecules were found to possess good pharmacokinetics and low toxicity. CONCLUSION We propose two analogues of T6361 as new potential inhibitors of MurA enzyme. Their good ADME-Toxicity profile qualifies them to reach in vitro and in vivo assays as future lead molecules.
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Affiliation(s)
- Ilham Boulhissa
- Laboratory of Applied Biochemistry, Department of Biochemistry and Cellular and Molecular Biology, Faculty of Natural and Life Sciences, University of Mentouri Brothers Constantine 1, Constantine, Algeria
| | - Hanane Boucherit
- Laboratory of Applied Biochemistry, Department of Biochemistry and Cellular and Molecular Biology, Faculty of Natural and Life Sciences, University of Mentouri Brothers Constantine 1, Constantine, Algeria
- Abdelhafid Boussouf University Center, Mila, Algeria
| | - Abdelouahab Chikhi
- Laboratory of Applied Biochemistry, Department of Biochemistry and Cellular and Molecular Biology, Faculty of Natural and Life Sciences, University of Mentouri Brothers Constantine 1, Constantine, Algeria
| | - Abderrahmane Bensegueni
- Laboratory of Applied Biochemistry, Department of Biochemistry and Cellular and Molecular Biology, Faculty of Natural and Life Sciences, University of Mentouri Brothers Constantine 1, Constantine, Algeria
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2
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Hsieh CJ, Giannakoulias S, Petersson EJ, Mach RH. Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development. Pharmaceuticals (Basel) 2023; 16:317. [PMID: 37259459 PMCID: PMC9964981 DOI: 10.3390/ph16020317] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 11/19/2023] Open
Abstract
The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).
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Affiliation(s)
- Chia-Ju Hsieh
- Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sam Giannakoulias
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - E. James Petersson
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Robert H. Mach
- Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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3
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Hua Y, Huang D, Liang L, Qian X, Dai X, Xu Y, Qiu H, Lu T, Liu H, Chen Y, Zhang Y. FSDscore: An Effective Target-focused Scoring Criterion for Virtual Screening. Mol Inform 2023; 42:e2200039. [PMID: 36372777 DOI: 10.1002/minf.202200039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 11/12/2022] [Indexed: 11/15/2022]
Abstract
Improving screening efficiency is one of the most challenging tasks of virtual screening (VS). In this work, we propose an effective target-focused scoring criterion for VS and apply it to the screening of a specific target scaffold replacement library constructed by enumeration of suitable substitution fragments and R-groups of known ligands. This criterion is based on both ligand- and structure-based scoring methods, which includes feature maps, 3D shape similarity, and the pairwise distance information between proteins and ligands (FSDscore). It is precisely due to the hybrid advantages of ligand- and structure-based approaches that FSDscore performs far better on the validation dataset than other scoring methods. We apply FSDscore to the VS of different kinase targets, MERTK (Mer tyrosine kinase) and ABL1 (tyrosine-protein kinase ABL1) in order to avoid occasionality. Finally, a VS case study shows the potential and effectiveness of our scoring criterion in drug discovery and molecular dynamics simulation further verifies its powerful ability.
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Affiliation(s)
- Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Dingfang Huang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Xu Qian
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Xiaowen Dai
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yuan Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Haodi Qiu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
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4
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Fink EA, Xu J, Hübner H, Braz JM, Seemann P, Avet C, Craik V, Weikert D, Schmidt MF, Webb CM, Tolmachova NA, Moroz YS, Huang XP, Kalyanaraman C, Gahbauer S, Chen G, Liu Z, Jacobson MP, Irwin JJ, Bouvier M, Du Y, Shoichet BK, Basbaum AI, Gmeiner P. Structure-based discovery of nonopioid analgesics acting through the α 2A-adrenergic receptor. Science 2022; 377:eabn7065. [PMID: 36173843 PMCID: PMC10360211 DOI: 10.1126/science.abn7065] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Because nonopioid analgesics are much sought after, we computationally docked more than 301 million virtual molecules against a validated pain target, the α2A-adrenergic receptor (α2AAR), seeking new α2AAR agonists chemotypes that lack the sedation conferred by known α2AAR drugs, such as dexmedetomidine. We identified 17 ligands with potencies as low as 12 nanomolar, many with partial agonism and preferential Gi and Go signaling. Experimental structures of α2AAR complexed with two of these agonists confirmed the docking predictions and templated further optimization. Several compounds, including the initial docking hit '9087 [mean effective concentration (EC50) of 52 nanomolar] and two analogs, '7075 and PS75 (EC50 4.1 and 4.8 nanomolar), exerted on-target analgesic activity in multiple in vivo pain models without sedation. These newly discovered agonists are interesting as therapeutic leads that lack the liabilities of opioids and the sedation of dexmedetomidine.
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Affiliation(s)
- Elissa A. Fink
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
- Graduate Program in Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Jun Xu
- Kobilka Institute of Innovative Drug Discovery, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Harald Hübner
- Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Joao M. Braz
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA
| | - Philipp Seemann
- Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Charlotte Avet
- Department of Biochemistry and Molecular Medicine, Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada
| | - Veronica Craik
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA
| | - Dorothee Weikert
- Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Maximilian F. Schmidt
- Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Chase M. Webb
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
- Graduate Program in Pharmaceutical Sciences and Pharmacogenomics, University of California, San Francisco, San Francisco, CA, USA
| | - Nataliya A. Tolmachova
- Enamine Ltd., 02094 Kyiv, Ukraine
- Institute of Bioorganic Chemistry and Petrochemistry, National Ukrainian Academy of Science, 02660 Kyiv, Ukraine
| | - Yurii S. Moroz
- National Taras Shevchenko University of Kyiv, 01601 Kyiv, Ukraine
- Chemspace, Riga LV-1082, Latvia
| | - Xi-Ping Huang
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Chakrapani Kalyanaraman
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Stefan Gahbauer
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Geng Chen
- Kobilka Institute of Innovative Drug Discovery, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Zheng Liu
- Kobilka Institute of Innovative Drug Discovery, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Matthew P. Jacobson
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Michel Bouvier
- Department of Biochemistry and Molecular Medicine, Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada
| | - Yang Du
- Kobilka Institute of Innovative Drug Discovery, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Allan I. Basbaum
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA
| | - Peter Gmeiner
- Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
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5
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Stein RM, Yang Y, Balius TE, O'Meara MJ, Lyu J, Young J, Tang K, Shoichet BK, Irwin JJ. Property-Unmatched Decoys in Docking Benchmarks. J Chem Inf Model 2021; 61:699-714. [PMID: 33494610 PMCID: PMC7913603 DOI: 10.1021/acs.jcim.0c00598] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.
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Affiliation(s)
- Reed M Stein
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Ying Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Trent E Balius
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., P.O. Box B, Frederick, Maryland 21702, United States
| | - Matt J O'Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Palmer Commons, 100 Washtenaw Ave. #2017, Ann Arbor, Michigan 48109, United States
| | - Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Jennifer Young
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Khanh Tang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
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6
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Jastrzębski S, Szymczak M, Pocha A, Mordalski S, Tabor J, Bojarski AJ, Podlewska S. Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening. J Chem Inf Model 2020; 60:4246-4262. [DOI: 10.1021/acs.jcim.9b01202] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Stanisław Jastrzębski
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Maciej Szymczak
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Agnieszka Pocha
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Stefan Mordalski
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
| | - Jacek Tabor
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Łojasiewicza Street, 30-348 Kraków, Poland
| | - Andrzej J. Bojarski
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Kraków, Poland
- Department of Technology and Biotechnology of Drugs, Jagiellonian University Medical College, 9 Medyczna Street, 30-688 Kraków, Poland
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7
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Guterres H, Im W. Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations. J Chem Inf Model 2020; 60:2189-2198. [PMID: 32227880 DOI: 10.1021/acs.jcim.0c00057] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in a directory of useful decoy-enhanced (DUD-E) dataset. MD trajectories are processed by evaluating ligand-binding stability using root-mean-square deviations. We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. The MD simulation demonstrates a robust performance across all seven different protein classes. In addition, some predicted ligand-binding modes are moderately refined during MD simulations. These results systematically validate the reliability of a physics-based approach to evaluate protein-ligand binding interactions.
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Affiliation(s)
- Hugo Guterres
- Departments of Biological Sciences, Chemistry, and Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, and Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Republic of Korea
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8
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Stein RM, Kang HJ, McCorvy JD, Glatfelter GC, Jones AJ, Che T, Slocum S, Huang XP, Savych O, Moroz YS, Stauch B, Johansson LC, Cherezov V, Kenakin T, Irwin JJ, Shoichet BK, Roth BL, Dubocovich ML. Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. Nature 2020; 579:609-614. [PMID: 32040955 PMCID: PMC7134359 DOI: 10.1038/s41586-020-2027-0] [Citation(s) in RCA: 174] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Accepted: 01/31/2020] [Indexed: 01/12/2023]
Affiliation(s)
- Reed M Stein
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Hye Jin Kang
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John D McCorvy
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Grant C Glatfelter
- Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo (SUNY), The State University of New York, Buffalo, NY, USA.,Designer Drug Research Unit, National Institute on Drug Abuse Intramural Research Program, Baltimore, MD, USA
| | - Anthony J Jones
- Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo (SUNY), The State University of New York, Buffalo, NY, USA
| | - Tao Che
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel Slocum
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xi-Ping Huang
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Yurii S Moroz
- National Taras Shevchenko University of Kyiv, Kiev, Ukraine.,Chemspace, Monmouth Junction, NJ, USA
| | - Benjamin Stauch
- Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.,Department of Chemistry, University of Southern California, Los Angeles, CA, USA
| | - Linda C Johansson
- Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.,Department of Chemistry, University of Southern California, Los Angeles, CA, USA
| | - Vadim Cherezov
- Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.,Department of Chemistry, University of Southern California, Los Angeles, CA, USA
| | - Terry Kenakin
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA.
| | - Bryan L Roth
- Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Margarita L Dubocovich
- Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo (SUNY), The State University of New York, Buffalo, NY, USA.
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9
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Buendia R, Kogej T, Engkvist O, Carlsson L, Linusson H, Johansson U, Toccaceli P, Ahlberg E. Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors. J Chem Inf Model 2019; 59:1230-1237. [PMID: 30726080 DOI: 10.1021/acs.jcim.8b00724] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn-ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.
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Affiliation(s)
- Ruben Buendia
- Department of Information Technology , University of Borås , SE-501 90 Borås , Sweden
| | - Thierry Kogej
- Discovery Sciences , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden
| | - Ola Engkvist
- Discovery Sciences , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden
| | - Lars Carlsson
- Discovery Sciences , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden.,Department of Computer Science, Royal Holloway , University of London , Egham , Surrey TW20 0EX , United Kingdom
| | - Henrik Linusson
- Department of Information Technology , University of Borås , SE-501 90 Borås , Sweden
| | - Ulf Johansson
- Department of Information Technology , University of Borås , SE-501 90 Borås , Sweden
| | - Paolo Toccaceli
- Department of Computer Science, Royal Holloway , University of London , Egham , Surrey TW20 0EX , United Kingdom
| | - Ernst Ahlberg
- Data Science and AI, Drug Safety & Metabolism , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden
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10
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Lyu J, Wang S, Balius TE, Singh I, Levit A, Moroz YS, O'Meara MJ, Che T, Algaa E, Tolmachova K, Tolmachev AA, Shoichet BK, Roth BL, Irwin JJ. Ultra-large library docking for discovering new chemotypes. Nature 2019; 566:224-229. [PMID: 30728502 PMCID: PMC6383769 DOI: 10.1038/s41586-019-0917-9] [Citation(s) in RCA: 522] [Impact Index Per Article: 104.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 01/04/2019] [Indexed: 11/09/2022]
Abstract
Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, China
| | - Sheng Wang
- State Key Laboratory of Molecular Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Trent E Balius
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Isha Singh
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Anat Levit
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Yurii S Moroz
- National Taras Shevchenko University of Kiev, Kiev, Ukraine
- Chemspace, Riga, Latvia
| | - Matthew J O'Meara
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Tao Che
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Enkhjargal Algaa
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA.
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA.
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
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11
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Imrie F, Bradley AR, van der Schaar M, Deane CM. Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data. J Chem Inf Model 2018; 58:2319-2330. [DOI: 10.1021/acs.jcim.8b00350] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Fergus Imrie
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, U.K
| | - Anthony R. Bradley
- Structural Genomics Consortium, University of Oxford, Oxford OX3 7DQ, U.K
- Department of Chemistry, University of Oxford, Oxford OX1 3TA, U.K
- Diamond Light Source Ltd., Didcot OX11 0DE, U.K
| | - Mihaela van der Schaar
- Department of Engineering, University of Oxford, Oxford OX1 3PJ, U.K
- Alan Turing Institute, London NW1 2DB, U.K
| | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, U.K
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12
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Passeri GI, Trisciuzzi D, Alberga D, Siragusa L, Leonetti F, Mangiatordi GF, Nicolotti O. Strategies of Virtual Screening in Medicinal Chemistry. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.
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Affiliation(s)
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Lydia Siragusa
- Molecular Discovery Ltd., Pinner, Middlesex, London, United Kingdom
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Giuseppe F. Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
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13
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Batista DC, Silva DPB, Florentino IF, Cardoso CS, Gonçalves MP, Valadares MC, Lião LM, Sanz G, Vaz BG, Costa EA, Menegatti R. Anti-inflammatory effect of a new piperazine derivative: (4-methylpiperazin-1-yl)(1-phenyl-1H-pyrazol-4-yl)methanone. Inflammopharmacology 2017; 26:217-226. [PMID: 28825161 DOI: 10.1007/s10787-017-0390-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 08/12/2017] [Indexed: 12/22/2022]
Abstract
AIMS This study investigates the anti-nociceptive and anti-inflammatory effects of new piperazine compound (LQFM182) as well as the toxicity acute in vitro. MAIN METHODS To evaluate the anti-nociceptive activity, the acetic acid-induced abdominal writhing test, tail flick test and formalin-induced pain test were used. The anti-inflammatory activity was evaluated using the models of paw oedema and pleurisy induced by carrageenan and some inflammatory parameters were evaluated, including cell migration, myeloperoxidase enzyme activity and the levels of TNF-α and IL-1β cytokines in pleural exudate. The acute oral systemic toxicity of LQFM182 in mice was evaluated through the neutral red uptake (nru) assay. KEY FINDINGS LQFM182 (50, 100 or 200 mg/kg, p.o.) decreased the number of writhings induced by acetic acid in a dose-dependent manner, and an intermediate dose (100 mg/kg, p.o.) reduced the paw licking time of animals in the second phase of the formalin test. Furthermore, LQFM182 (100 mg/kg, p.o.) reduced oedema formation at all hours of the paw oedema induced by carrageenan test and in pleurisy test reduced cell migration from the reduction of polymorphonuclear cells, myeloperoxidase enzyme activity and the levels of pro-inflammatory cytokines IL-1β and TNF-α. Therefore, it was classified in GHS category 300 < LD50 < 2000 mg/kg. SIGNIFICANCE Reduction of the TNF-α and IL-1β levels.
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Affiliation(s)
- Daniel C Batista
- Faculty of Pharmacy, Laboratory of Medicinal Pharmaceutical Chemistry, Federal University of Goiás, Goiânia, GO, Brazil
| | - Daiany P B Silva
- Department of Pharmacology, ICB, Federal University of Goiás, Campus Samambaia, 314, Goiânia, GO, 74001-970, Brazil
| | - Iziara F Florentino
- Department of Pharmacology, ICB, Federal University of Goiás, Campus Samambaia, 314, Goiânia, GO, 74001-970, Brazil
| | - Carina S Cardoso
- Department of Pharmacology, ICB, Federal University of Goiás, Campus Samambaia, 314, Goiânia, GO, 74001-970, Brazil
| | - Merita P Gonçalves
- Department of Pharmacology, ICB, Federal University of Goiás, Campus Samambaia, 314, Goiânia, GO, 74001-970, Brazil
| | - Marize C Valadares
- Laboratory of Pharmacology and Cell Toxicology, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Luciano M Lião
- Chemistry Institute, Federal University of Goias, Campus Samambaia, Goiânia, GO, Brazil
| | - Germán Sanz
- Chemistry Institute, Laboratory of Chromatography and Mass Spectrometry-LaCEM, Federal University of Goiás, Goiânia, GO, Brazil
| | - Boniek G Vaz
- Chemistry Institute, Laboratory of Chromatography and Mass Spectrometry-LaCEM, Federal University of Goiás, Goiânia, GO, Brazil
| | - Elson A Costa
- Department of Pharmacology, ICB, Federal University of Goiás, Campus Samambaia, 314, Goiânia, GO, 74001-970, Brazil
| | - Ricardo Menegatti
- Faculty of Pharmacy, Laboratory of Medicinal Pharmaceutical Chemistry, Federal University of Goiás, Goiânia, GO, Brazil.
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14
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Wójcikowski M, Ballester PJ, Siedlecki P. Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep 2017; 7:46710. [PMID: 28440302 PMCID: PMC5404222 DOI: 10.1038/srep46710] [Citation(s) in RCA: 189] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 03/23/2017] [Indexed: 12/23/2022] Open
Abstract
Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and −0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary).
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Affiliation(s)
- Maciej Wójcikowski
- Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, 02-106 Warsaw, Poland
| | - Pedro J Ballester
- Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, Marseille, F-13009, France.,CNRS, UMR7258, Marseille, F-13009, France.,Aix-Marseille University, UM 105, F-13284, Marseille, France
| | - Pawel Siedlecki
- Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, 02-106 Warsaw, Poland.,Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland
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15
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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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16
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Ngo T, Kufareva I, Coleman JL, Graham RM, Abagyan R, Smith NJ. Identifying ligands at orphan GPCRs: current status using structure-based approaches. Br J Pharmacol 2016; 173:2934-51. [PMID: 26837045 PMCID: PMC5341249 DOI: 10.1111/bph.13452] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 11/18/2015] [Accepted: 01/29/2016] [Indexed: 12/26/2022] Open
Abstract
GPCRs are the most successful pharmaceutical targets in history. Nevertheless, the pharmacology of many GPCRs remains inaccessible as their endogenous or exogenous modulators have not been discovered. Tools that explore the physiological functions and pharmacological potential of these 'orphan' GPCRs, whether they are endogenous and/or surrogate ligands, are therefore of paramount importance. Rates of receptor deorphanization determined by traditional reverse pharmacology methods have slowed, indicating a need for the development of more sophisticated and efficient ligand screening approaches. Here, we discuss the use of structure-based ligand discovery approaches to identify small molecule modulators for exploring the function of orphan GPCRs. These studies have been buoyed by the growing number of GPCR crystal structures solved in the past decade, providing a broad range of template structures for homology modelling of orphans. This review discusses the methods used to establish the appropriate signalling assays to test orphan receptor activity and provides current examples of structure-based methods used to identify ligands of orphan GPCRs. Linked Articles This article is part of a themed section on Molecular Pharmacology of G Protein-Coupled Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v173.20/issuetoc.
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Affiliation(s)
- Tony Ngo
- Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- St. Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia
| | - Irina Kufareva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - James Lj Coleman
- Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- St. Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia
| | - Robert M Graham
- Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- St. Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Nicola J Smith
- Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia.
- St. Vincent's Clinical School, University of New South Wales, Darlinghurst, NSW, Australia.
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17
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Levoin N, Labeeuw O, Billot X, Calmels T, Danvy D, Krief S, Berrebi-Bertrand I, Lecomte JM, Schwartz JC, Capet M. Discovery of nanomolar ligands with novel scaffolds for the histamine H4 receptor by virtual screening. Eur J Med Chem 2016; 125:565-572. [PMID: 27718472 DOI: 10.1016/j.ejmech.2016.09.074] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 09/21/2016] [Accepted: 09/22/2016] [Indexed: 12/29/2022]
Abstract
The involvement of histamine H4 receptor (H4R) in immune cells chemotaxis and mediator release makes it an attractive target for the treatment of inflammation disorders. A decade of medicinal chemistry efforts has led to several promising ligands, although the chemical structures described so far possesses a singular limited diversity. We report here the discovery of novel structures, belonging to completely different scaffolds. The virtual screening was planed as a two-steps process. First, using a "scout screening" methodology, we have experimentally probed the H4R ligand binding site using a small size chemical library with very diverse structures, and identified a hit that further assist us in refining a raw 3D homology model. Second, the refined 3D model was used to conduct a widened virtual screening. This two-steps strategy proved to be very successful, both in terms of structural diversity and hit rate (23%). Moreover, the hits have high affinity for the H4R, with most potent ligands in the nanomolar range.
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Affiliation(s)
- Nicolas Levoin
- Bioprojet-Biotech, 4rue du Chesnay Beauregard, 35762 Saint-Gregoire Cedex, France.
| | - Olivier Labeeuw
- Bioprojet-Biotech, 4rue du Chesnay Beauregard, 35762 Saint-Gregoire Cedex, France
| | - Xavier Billot
- Bioprojet-Biotech, 4rue du Chesnay Beauregard, 35762 Saint-Gregoire Cedex, France
| | - Thierry Calmels
- Bioprojet-Biotech, 4rue du Chesnay Beauregard, 35762 Saint-Gregoire Cedex, France
| | - Denis Danvy
- Bioprojet-Biotech, 4rue du Chesnay Beauregard, 35762 Saint-Gregoire Cedex, France
| | - Stéphane Krief
- Bioprojet-Biotech, 4rue du Chesnay Beauregard, 35762 Saint-Gregoire Cedex, France
| | | | - Jeanne-Marie Lecomte
- Bioprojet-Biotech, 4rue du Chesnay Beauregard, 35762 Saint-Gregoire Cedex, France
| | | | - Marc Capet
- Bioprojet-Biotech, 4rue du Chesnay Beauregard, 35762 Saint-Gregoire Cedex, France
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Abstract
Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.
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Affiliation(s)
- Stephani Joy Y Macalino
- National Leading Research Laboratory of Molecular Modeling and Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, and Global Top 5 Research Program, Ewha Womans University, Seoul, 120-750, Korea
| | - Vijayakumar Gosu
- National Leading Research Laboratory of Molecular Modeling and Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, and Global Top 5 Research Program, Ewha Womans University, Seoul, 120-750, Korea
| | - Sunhye Hong
- National Leading Research Laboratory of Molecular Modeling and Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, and Global Top 5 Research Program, Ewha Womans University, Seoul, 120-750, Korea
| | - Sun Choi
- National Leading Research Laboratory of Molecular Modeling and Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, and Global Top 5 Research Program, Ewha Womans University, Seoul, 120-750, Korea.
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19
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Michino M, Beuming T, Donthamsetti P, Newman AH, Javitch JA, Shi L. What can crystal structures of aminergic receptors tell us about designing subtype-selective ligands? Pharmacol Rev 2015; 67:198-213. [PMID: 25527701 DOI: 10.1124/pr.114.009944] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
G protein-coupled receptors (GPCRs) are integral membrane proteins that represent an important class of drug targets. In particular, aminergic GPCRs interact with a significant portion of drugs currently on the market. However, most drugs that target these receptors are associated with undesirable side effects, which are due in part to promiscuous interactions with close homologs of the intended target receptors. Here, based on a systematic analysis of all 37 of the currently available high-resolution crystal structures of aminergic GPCRs, we review structural elements that contribute to and can be exploited for designing subtype-selective compounds. We describe the roles of secondary binding pockets (SBPs), as well as differences in ligand entry pathways to the orthosteric binding site, in determining selectivity. In addition, using the available crystal structures, we have identified conformational changes in the SBPs that are associated with receptor activation and explore the implications of these changes for the rational development of selective ligands with tailored efficacy.
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Affiliation(s)
- Mayako Michino
- Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, New York (M.M., L.S.); Schrödinger Inc., New York, New York (T.B.); Departments of Psychiatry and Pharmacology, Columbia University College of Physicians and Surgeons, and Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, New York (P.D., J.A.J.); and Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, Maryland (A.H.N.)
| | - Thijs Beuming
- Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, New York (M.M., L.S.); Schrödinger Inc., New York, New York (T.B.); Departments of Psychiatry and Pharmacology, Columbia University College of Physicians and Surgeons, and Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, New York (P.D., J.A.J.); and Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, Maryland (A.H.N.)
| | - Prashant Donthamsetti
- Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, New York (M.M., L.S.); Schrödinger Inc., New York, New York (T.B.); Departments of Psychiatry and Pharmacology, Columbia University College of Physicians and Surgeons, and Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, New York (P.D., J.A.J.); and Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, Maryland (A.H.N.)
| | - Amy Hauck Newman
- Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, New York (M.M., L.S.); Schrödinger Inc., New York, New York (T.B.); Departments of Psychiatry and Pharmacology, Columbia University College of Physicians and Surgeons, and Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, New York (P.D., J.A.J.); and Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, Maryland (A.H.N.)
| | - Jonathan A Javitch
- Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, New York (M.M., L.S.); Schrödinger Inc., New York, New York (T.B.); Departments of Psychiatry and Pharmacology, Columbia University College of Physicians and Surgeons, and Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, New York (P.D., J.A.J.); and Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, Maryland (A.H.N.)
| | - Lei Shi
- Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, New York (M.M., L.S.); Schrödinger Inc., New York, New York (T.B.); Departments of Psychiatry and Pharmacology, Columbia University College of Physicians and Surgeons, and Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, New York (P.D., J.A.J.); and Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse-Intramural Research Program, Baltimore, Maryland (A.H.N.)
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20
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Ibrahim TM, Bauer MR, Boeckler FM. Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization. J Cheminform 2015; 7:21. [PMID: 26034510 PMCID: PMC4450982 DOI: 10.1186/s13321-015-0074-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 05/06/2015] [Indexed: 11/29/2022] Open
Abstract
Background Structure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details. Results Using a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking. Conclusions In conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule. Using DEKOIS 2.0 benchmark sets in structure-based virtual screening to probe the impact of molecular preparation and score normalization. ![]()
Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0074-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tamer M Ibrahim
- Laboratory for Molecular Design and Pharmaceutical Biophysics, Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany ; Department of Pharmaceutical Chemistry, Faculty of Pharmacy and Biotechnology, German University in Cairo, Cairo, 11835 Egypt
| | - Matthias R Bauer
- Laboratory for Molecular Design and Pharmaceutical Biophysics, Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany
| | - Frank M Boeckler
- Laboratory for Molecular Design and Pharmaceutical Biophysics, Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany
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21
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Stockert JA, Devi LA. Advancements in therapeutically targeting orphan GPCRs. Front Pharmacol 2015; 6:100. [PMID: 26005419 PMCID: PMC4424851 DOI: 10.3389/fphar.2015.00100] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Accepted: 04/21/2015] [Indexed: 11/23/2022] Open
Abstract
G-protein coupled receptors (GPCRs) are popular biological targets for drug discovery and development. To date there are more than 140 orphan GPCRs, i.e., receptors whose endogenous ligands are unknown. Traditionally orphan GPCRs have been difficult to study and the development of therapeutic compounds targeting these receptors has been extremely slow although these GPCRs are considered important targets based on their distribution and behavioral phenotype as revealed by animals lacking the receptor. Recent advances in several methods used to study orphan receptors, including protein crystallography and homology modeling are likely to be useful in the identification of therapeutics targeting these receptors. In the past 13 years, over a dozen different Class A GPCRs have been crystallized; this trend is exciting, since homology modeling of GPCRs has previously been limited by the availability of solved structures. As the number of solved GPCR structures continues to grow so does the number of templates that can be used to generate increasingly accurate models of phylogenetically related orphan GPCRs. The availability of solved structures along with the advances in using multiple templates to build models (in combination with molecular dynamics simulations that reveal structural information not provided by crystallographic data and methods for modeling hard-to-predict flexible loop regions) have improved the quality of GPCR homology models. This, in turn, has improved the success rates of virtual ligand screens that use homology models to identify potential receptor binding compounds. Experimental testing of the predicted hits and validation using traditional GPCR pharmacological approaches can be used to drive ligand-based efforts to probe orphan receptor biology as well as to define the chemotypes and chemical scaffolds important for binding. As a result of these advances, orphan GPCRs are emerging from relative obscurity as a new class of drug targets.
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Affiliation(s)
- Jennifer A Stockert
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Lakshmi A Devi
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
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22
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Kooistra AJ, Leurs R, de Esch IJP, de Graaf C. Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A β-Adrenoceptor Case Study. J Chem Inf Model 2015; 55:1045-61. [DOI: 10.1021/acs.jcim.5b00066] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Albert J. Kooistra
- Amsterdam Institute for Molecules,
Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty
of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Rob Leurs
- Amsterdam Institute for Molecules,
Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty
of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Iwan J. P. de Esch
- Amsterdam Institute for Molecules,
Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty
of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Chris de Graaf
- Amsterdam Institute for Molecules,
Medicines and Systems (AIMMS), Division of Medicinal Chemistry, Faculty
of Science, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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23
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Kumar A, Zhang KYJ. Hierarchical virtual screening approaches in small molecule drug discovery. Methods 2015; 71:26-37. [PMID: 25072167 PMCID: PMC7129923 DOI: 10.1016/j.ymeth.2014.07.007] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 07/16/2014] [Accepted: 07/17/2014] [Indexed: 02/06/2023] Open
Abstract
Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.
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Affiliation(s)
- Ashutosh Kumar
- Structural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- Structural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan.
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24
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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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25
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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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26
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Corrêa MF, dos Santos Fernandes JP. Histamine H4 receptor ligands: future applications and state of art. Chem Biol Drug Des 2014; 85:461-80. [PMID: 25228262 DOI: 10.1111/cbdd.12431] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Histamine is a chemical transmitter found practically in whole organism and exerts its effects through the interaction with H1 to H4 histaminergic receptors. Specifically, H4 receptors are found mainly in immune cells and blood-forming tissues, thus are involved in inflammatory and immune processes, as well as some actions in central nervous system. Therefore, H4 receptor ligands can have applications in the treatment of chronic inflammatory and immune diseases and may be novel therapeutic option in these conditions. Several H4 receptor ligands have been described from early 2000's until nowadays, being imidazole, indolecarboxamide, 2-aminopyrimidine, quinazoline, and quinoxaline scaffolds the most explored and discussed in this review. Moreover, several studies of molecular modeling using homology models of H4 receptor and QSAR data of the ligands are summarized. The increasing and promising therapeutic applications are leading these compounds to clinical trials, which probably will be part of the next generation of blockbuster drugs.
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Affiliation(s)
- Michelle Fidelis Corrêa
- Departamento de Ciências Exatas e da Terra, Instituto de Ciências Ambientais, Químicas e Farmacêuticas, UNIFESP, Diadema, Brazil
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27
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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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28
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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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29
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Kiss R, Jójárt B, Schmidt É, Kiss B, Keserű GM. Identification of Novel Histamine H4 Ligands by Virtual Screening on Molecular Dynamics Ensembles. Mol Inform 2014; 33:264-8. [PMID: 27485772 DOI: 10.1002/minf.201300072] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Accepted: 02/06/2014] [Indexed: 01/08/2023]
Abstract
We report the identification of novel histamine H4 receptor ligands by ensemble docking on homology model conformers derived from molecular dynamics simulations. Selected receptor models from the trajectories demonstrated superior virtual screening performance compared to the initial models. The ensemble of the best models was able to retrieve a diverse set of known H4 ligands. Prospective virtual screening against these models and subsequent in vitro experimental validation identified novel H4 ligands. Compound 3 showing highest affinity and ligand efficiency represents an interesting scaffold for further medicinal chemistry exploration.
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Affiliation(s)
- Róbert Kiss
- mcule.com Ltd. Vendel utca 15-17, B/2/6, H-1096 Budapest, Hungary
| | - Balázs Jójárt
- Department of Chemical Informatics, Faculty of Education, University of Szeged, Boldogasszony sgt. 6., H-6725, Szeged, Hungary
| | - Éva Schmidt
- Gedeon Richter Plc, Gyömrői út 19-21., H-1103, Budapest, Hungary
| | - Béla Kiss
- Gedeon Richter Plc, Gyömrői út 19-21., H-1103, Budapest, Hungary
| | - György M Keserű
- Gedeon Richter Plc, Gyömrői út 19-21., H-1103, Budapest, Hungary. .,Department of General and Analytical Chemistry, Budapest University of Technology and Economics, Szt. Gellért tér 4., H-1111, Budapest, Hungary. .,Present address: Research Centre for Natural Sciences, Hungarian Academy of Sciences, Pusztaszeri út 59-67., H-1025 Budapest, Hungary.
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30
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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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31
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Webb B, Eswar N, Fan H, Khuri N, Pieper U, Dong G, Sali A. Comparative Modeling of Drug Target Proteins☆. REFERENCE MODULE IN CHEMISTRY, MOLECULAR SCIENCES AND CHEMICAL ENGINEERING 2014. [PMCID: PMC7157477 DOI: 10.1016/b978-0-12-409547-2.11133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.
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32
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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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33
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Tarcsay A, Paragi G, Vass M, Jójárt B, Bogár F, Keserű GM. The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs. J Chem Inf Model 2013; 53:2990-9. [PMID: 24116387 DOI: 10.1021/ci400087b] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The formation of ligand-protein complexes requires simultaneous adaptation of the binding partners. In structure based virtual screening, high throughput docking approaches typically consider the ligand flexibility, but the conformational freedom of the protein is usually taken into account in a limited way. The goal of this study is to elaborate a methodology for incorporating protein flexibility to improve the virtual screening enrichments on GPCRs. Explicit-solvated molecular dynamics simulations (MD) were carried out in lipid bilayers to generate an ensemble of protein conformations for the X-ray structures and homology models of both aminergic and peptidergic GPCRs including the chemokine CXCR4, dopamine D3, histamine H4, and serotonin 5HT6 holo receptor complexes. The quality of the receptor models was assessed by enrichment studies to compare X-ray structures, homology models, and snapshots from the MD trajectory. According to our results, selected frames from the MD trajectory can outperform X-ray structures and homology models in terms of enrichment factor and AUC values. Significant changes were observed considering EF1% values: comparing the original CXCR4, D3, and H4 targets and the additional 5HT6 initial models to that of the best MD frame resulted in 0 to 6.7, 0.32 to 3.5 (10×), 13.3 to 26.7 (2×), and 0 to 14.1 improvements, respectively. It is worth noting that rank-average based ensemble evaluation calculated for different ensemble sizes could not improve the results further. We propose here that MD simulation can capture protein conformations representing the key interacting points of the receptor but less biased toward one specific chemotype. These conformations are useful for the identification of a "consensus" binding site with improved performance in virtual screening.
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Affiliation(s)
- Akos Tarcsay
- Discovery Chemistry, Gedeon Richter plc. , 19-21 Gyömrői út, H-1103 Budapest, Hungary
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34
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Coleman RG, Carchia M, Sterling T, Irwin JJ, Shoichet BK. Ligand pose and orientational sampling in molecular docking. PLoS One 2013; 8:e75992. [PMID: 24098414 PMCID: PMC3787967 DOI: 10.1371/journal.pone.0075992] [Citation(s) in RCA: 120] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2013] [Accepted: 08/13/2013] [Indexed: 12/19/2022] Open
Abstract
Molecular docking remains an important tool for structure-based screening to find new ligands and chemical probes. As docking ambitions grow to include new scoring function terms, and to address ever more targets, the reliability and extendability of the orientation sampling, and the throughput of the method, become pressing. Here we explore sampling techniques that eliminate stochastic behavior in DOCK3.6, allowing us to optimize the method for regularly variable sampling of orientations. This also enabled a focused effort to optimize the code for efficiency, with a three-fold increase in the speed of the program. This, in turn, facilitated extensive testing of the method on the 102 targets, 22,805 ligands and 1,411,214 decoys of the Directory of Useful Decoys - Enhanced (DUD-E) benchmarking set, at multiple levels of sampling. Encouragingly, we observe that as sampling increases from 50 to 500 to 2000 to 5000 to 20000 molecular orientations in the binding site (and so from about 1×1010 to 4×1010 to 1×1011 to 2×1011 to 5×1011 mean atoms scored per target, since multiple conformations are sampled per orientation), the enrichment of ligands over decoys monotonically increases for most DUD-E targets. Meanwhile, including internal electrostatics in the evaluation ligand conformational energies, and restricting aromatic hydroxyls to low energy rotamers, further improved enrichment values. Several of the strategies used here to improve the efficiency of the code are broadly applicable in the field.
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Affiliation(s)
- Ryan G. Coleman
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America
| | - Michael Carchia
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America
| | - Teague Sterling
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America
| | - John J. Irwin
- Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America
- Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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35
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Yoshikawa Y, Oishi S, Kubo T, Tanahara N, Fujii N, Furuya T. Optimized method of G-protein-coupled receptor homology modeling: its application to the discovery of novel CXCR7 ligands. J Med Chem 2013; 56:4236-51. [PMID: 23656360 DOI: 10.1021/jm400307y] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Homology modeling of G-protein-coupled seven-transmembrane receptors (GPCRs) remains a challenge despite the increasing number of released GPCR crystal structures. This challenge can be attributed to the low sequence identity and structural diversity of the ligand-binding pocket of GPCRs. We have developed an optimized GPCR structure modeling method based on multiple GPCR crystal structures. This method was designed to be applicable to distantly related receptors of known structural templates. CXC chemokine receptor (CXCR7) is a potential drug target for cancer chemotherapy. Homology modeling, docking, and virtual screening for CXCR7 were carried out using our method. The predicted docking poses of the known antagonists were different from the crystal structure of human CXCR4 with the small-molecule antagonist IT1t. Furthermore, 21 novel CXCR7 ligands with IC50 values of 1.29-11.4 μM with various scaffolds were identified by structure-based virtual screening.
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Affiliation(s)
- Yasushi Yoshikawa
- Drug Discovery Department, Research & Development Division, PharmaDesign Inc., 2-19-8 Hatchobori, Tokyo 104-0032, Japan
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36
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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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37
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Docking and MD study of histamine H4R based on the crystal structure of H1R. J Mol Graph Model 2013; 39:1-12. [DOI: 10.1016/j.jmgm.2012.10.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2012] [Revised: 10/09/2012] [Accepted: 10/13/2012] [Indexed: 01/06/2023]
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38
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Schultes S, Nijmeijer S, Engelhardt H, Kooistra AJ, Vischer HF, de Esch IJP, Haaksma EEJ, Leurs R, de Graaf C. Mapping histamine H4 receptor–ligand binding modes. MEDCHEMCOMM 2013. [DOI: 10.1039/c2md20212c] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Computational prediction of ligand binding modes in G protein-coupled receptors (GPCRs) remains a challenging task. Systematic consideration of different protein modelling templates, ligand binding poses, and ligand protonation states in extensive molecular dynamics (MD) simulation studies enabled the prediction of ligand-specific mutation effects in the histamine H4 receptor, a key player in inflammation.
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Affiliation(s)
- Sabine Schultes
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Saskia Nijmeijer
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Harald Engelhardt
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Albert J. Kooistra
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Henry F. Vischer
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Iwan J. P. de Esch
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Eric E. J. Haaksma
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Rob Leurs
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
| | - Chris de Graaf
- Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry
- Department of Pharmacochemistry
- Faculty of Exact Sciences
- VU University Amsterdam
- 1081 HV Amsterdam
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39
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Kooistra AJ, Roumen L, Leurs R, de Esch IJ, de Graaf C. From Heptahelical Bundle to Hits from the Haystack. Methods Enzymol 2013; 522:279-336. [DOI: 10.1016/b978-0-12-407865-9.00015-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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40
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Engelhardt H, de Esch IJ, Kuhn D, Smits RA, Zuiderveld OP, Dobler J, Mayer M, Lips S, Arnhof H, Scharn D, Haaksma EE, Leurs R. Detailed structure–activity relationship of indolecarboxamides as H4 receptor ligands. Eur J Med Chem 2012; 54:660-8. [DOI: 10.1016/j.ejmech.2012.06.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2012] [Revised: 06/05/2012] [Accepted: 06/09/2012] [Indexed: 10/28/2022]
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41
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Structural modelling and dynamics of proteins for insights into drug interactions. Adv Drug Deliv Rev 2012; 64:323-43. [PMID: 22155026 DOI: 10.1016/j.addr.2011.11.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Revised: 11/17/2011] [Accepted: 11/24/2011] [Indexed: 12/27/2022]
Abstract
Proteins are the workhorses of biomolecules and their function is affected by their structure and their structural rearrangements during ligand entry, ligand binding and protein-protein interactions. Hence, the knowledge of protein structure and, importantly, the dynamic behaviour of the structure are critical for understanding how the protein performs its function. The predictions of the structure and the dynamic behaviour can be performed by combinations of structure modelling and molecular dynamics simulations. The simulations also need to be sensitive to the constraints of the environment in which the protein resides. Standard computational methods now exist in this field to support the experimental effort of solving protein structures. This review presents a comprehensive overview of the basis of the calculations and the well-established computational methods used to generate and understand protein structure and function and the study of their dynamic behaviour with the reference to lung-related targets.
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Christopher F, Thangam EB, Suresh MX. A Bioinformatics Search for Selective Histamine H4 Receptor Antagonists Through Structure-Based Virtual Screening Strategies. Chem Biol Drug Des 2012; 79:749-59. [DOI: 10.1111/j.1747-0285.2012.01336.x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Nagarajan S, Skoufias DA, Kozielski F, Pae AN. Receptor–Ligand Interaction-Based Virtual Screening for Novel Eg5/Kinesin Spindle Protein Inhibitors. J Med Chem 2012; 55:2561-73. [DOI: 10.1021/jm201290v] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Shanthi Nagarajan
- Neuro-Medicine Center, Life
Sciences Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea
- School of Science, Korea University of Science and Technology, 52 Eoeun
dongYuseong-gu, Daejeon 305-333, Republic of Korea
| | - Dimitrios A. Skoufias
- Institute for Structural Biology (CEA-CNRS-UJF), 41 rue Jules Horowitz, 38027
Grenoble Cedex 1, France,
| | - Frank Kozielski
- The Beatson Institute for Cancer Research, Garscube Estate, Switchback Road,
Bearsden, Glasgow, G61 1BD, Scotland, U.K
| | - Ae Nim Pae
- Neuro-Medicine Center, Life
Sciences Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea
- School of Science, Korea University of Science and Technology, 52 Eoeun
dongYuseong-gu, Daejeon 305-333, Republic of Korea
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Abstract
Virtual ligand screening uses computation to discover new ligands of a protein by screening one or more of its structural models against a database of potential ligands. Comparative protein structure modeling extends the applicability of virtual screening beyond the atomic structures determined by X-ray crystallography or NMR spectroscopy. Here, we describe an integrated modeling and docking protocol, combining comparative modeling by MODELLER and virtual ligand screening by DOCK.
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Affiliation(s)
- Hao Fan
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco
| | - John J. Irwin
- Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California, San Francisco
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco
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Kiss R, Sándor M, Gere A, Schmidt E, Balogh GT, Kiss B, Molnár L, Lemmen C, Keseru GM. Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. J Chem Inf Model 2011; 52:233-42. [PMID: 22168379 DOI: 10.1021/ci2004972] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ligand-based approaches are particularly important in the hit identification process of drug discovery when no structural information on the target is available. Pharmacophore descriptors that use a topological representation of the ligands are usually fast enough to screen large compound libraries effectively when seeking novel lead candidates. One example of this kind is the Feature Tree descriptor, a reduced graph representation implemented in the FTrees software. In this study, we tested the screening efficiency of FTrees by both retrospective and prospective screens using known histamine H4 antagonists and serotonin transporter (SERT) inhibitors as query molecules. Our results demonstrate that FTrees can effectively find actives. Particularly when combined with a subsequent 2D fingerprint-based diversity selection, FTrees was found to be extremely effective at discovering a diverse set of scaffolds. Prospective screening of our in-house compound deck provided several novel H4 and SERT ligands that could serve as suitable starting points for further optimization.
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Affiliation(s)
- Róbert Kiss
- Gedeon Richter Plc, Gyömrői út 19-21, H-1103 Budapest, Hungary
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Fanelli F, De Benedetti PG. Update 1 of: computational modeling approaches to structure-function analysis of G protein-coupled receptors. Chem Rev 2011; 111:PR438-535. [PMID: 22165845 DOI: 10.1021/cr100437t] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Francesca Fanelli
- Dulbecco Telethon Institute, University of Modena and Reggio Emilia, via Campi 183, 41125 Modena, Italy.
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Kim SK, Fristrup P, Abrol R, Goddard WA. Structure-based prediction of subtype selectivity of histamine H3 receptor selective antagonists in clinical trials. J Chem Inf Model 2011; 51:3262-74. [PMID: 22035233 DOI: 10.1021/ci200435b] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Histamine receptors (HRs) are excellent drug targets for the treatment of diseases, such as schizophrenia, psychosis, depression, migraine, allergies, asthma, ulcers, and hypertension. Among them, the human H(3) histamine receptor (hH(3)HR) antagonists have been proposed for specific therapeutic applications, including treatment of Alzheimer's disease, attention deficit hyperactivity disorder (ADHD), epilepsy, and obesity. However, many of these drug candidates cause undesired side effects through the cross-reactivity with other histamine receptor subtypes. In order to develop improved selectivity and activity for such treatments, it would be useful to have the three-dimensional structures for all four HRs. We report here the predicted structures of four HR subtypes (H(1), H(2), H(3), and H(4)) using the GEnSeMBLE (GPCR ensemble of structures in membrane bilayer environment) Monte Carlo protocol, sampling ∼35 million combinations of helix packings to predict the 10 most stable packings for each of the four subtypes. Then we used these 10 best protein structures with the DarwinDock Monte Carlo protocol to sample ∼50 000 × 10(20) poses to predict the optimum ligand-protein structures for various agonists and antagonists. We find that E206(5.46) contributes most in binding H(3) selective agonists (5, 6, 7) in agreement with experimental mutation studies. We also find that conserved E5.46/S5.43 in both of hH(3)HR and hH(4)HR are involved in H(3)/ H(4) subtype selectivity. In addition, we find that M378(6.55) in hH(3)HR provides additional hydrophobic interactions different from hH(4)HR (the corresponding amino acid of T323(6.55) in hH(4)HR) to provide additional subtype bias. From these studies, we developed a pharmacophore model based on our predictions for known hH(3)HR selective antagonists in clinical study [ABT-239 1, GSK-189,254 2, PF-3654746 3, and BF2.649 (tiprolisant) 4] that suggests critical selectivity directing elements are: the basic proton interacting with D114(3.32), the spacer, the aromatic ring substituted with the hydrophilic or lipophilic groups interacting with lipophilic pockets in transmembranes (TMs) 3-5-6 and the aliphatic ring located in TMs 2-3-7. These 3D structures for all four HRs should help guide the rational design of novel drugs for the subtype selective antagonists and agonists with reduced side effects.
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Affiliation(s)
- Soo-Kyung Kim
- Materials and Process Simulation Center (MC139-74), California Institute of Technology, 1200 E. California Blvd., Pasadena, California 91125, USA
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de Graaf C, Kooistra AJ, Vischer HF, Katritch V, Kuijer M, Shiroishi M, Iwata S, Shimamura T, Stevens RC, de Esch IJP, Leurs R. Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor. J Med Chem 2011; 54:8195-206. [PMID: 22007643 DOI: 10.1021/jm2011589] [Citation(s) in RCA: 166] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The recent crystal structure determinations of druggable class A G protein-coupled receptors (GPCRs) have opened up excellent opportunities in structure-based ligand discovery for this pharmaceutically important protein family. We have developed and validated a customized structure-based virtual fragment screening protocol against the recently determined human histamine H(1) receptor (H(1)R) crystal structure. The method combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. The optimized in silico screening approach was successfully applied to identify a chemically diverse set of novel fragment-like (≤22 heavy atoms) H(1)R ligands with an exceptionally high hit rate of 73%. Of the 26 tested fragments, 19 compounds had affinities ranging from 10 μM to 6 nM. The current study shows the potential of in silico screening against GPCR crystal structures to explore novel, fragment-like GPCR ligand space.
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Affiliation(s)
- Chris de Graaf
- Leiden/Amsterdam Center for Drug Research, Division of Medicinal Chemistry, VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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Kolb P, Klebe G. The Golden Age of GPCR Structural Biology: Any Impact on Drug Design? Angew Chem Int Ed Engl 2011; 50:11573-5. [DOI: 10.1002/anie.201105869] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2011] [Indexed: 11/10/2022]
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50
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Kolb P, Klebe G. Das goldene Zeitalter der Strukturbiologie von GPCRs: Einflüsse auf die Wirkstoffentwicklung? Angew Chem Int Ed Engl 2011. [DOI: 10.1002/ange.201105869] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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