1
|
Díaz-Holguín A, Saarinen M, Vo DD, Sturchio A, Branzell N, Cabeza de Vaca I, Hu H, Mitjavila-Domènech N, Lindqvist A, Baranczewski P, Millan MJ, Yang Y, Carlsson J, Svenningsson P. AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1. SCIENCE ADVANCES 2024; 10:eadn1524. [PMID: 39110804 PMCID: PMC11305387 DOI: 10.1126/sciadv.adn1524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 06/28/2024] [Indexed: 08/10/2024]
Abstract
Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 μM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.
Collapse
Affiliation(s)
- Alejandro Díaz-Holguín
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Marcus Saarinen
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Duc Duy Vo
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Andrea Sturchio
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
- Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
| | - Niclas Branzell
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Israel Cabeza de Vaca
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Huabin Hu
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Núria Mitjavila-Domènech
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Annika Lindqvist
- Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden
| | - Pawel Baranczewski
- Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden
| | - Mark J. Millan
- Neuroinflammation Therapeutic Area, Institut de Recherches Servier, Centre de Recherches de Croissy, Paris, France and Institute of Neuroscience and Psychology, College of Medicine, Vet and Life Sciences, Glasgow University, Scotland, Glasgow, UK
| | - Yunting Yang
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Per Svenningsson
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| |
Collapse
|
2
|
Kenakin T. Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition. Nat Rev Drug Discov 2024; 23:626-644. [PMID: 38890494 DOI: 10.1038/s41573-024-00958-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2024] [Indexed: 06/20/2024]
Abstract
Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.
Collapse
Affiliation(s)
- Terry Kenakin
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
| |
Collapse
|
3
|
Pallante L, Cannariato M, Androutsos L, Zizzi EA, Bompotas A, Hada X, Grasso G, Kalogeras A, Mavroudi S, Di Benedetto G, Theofilatos K, Deriu MA. VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites. Sci Rep 2024; 14:6296. [PMID: 38491261 PMCID: PMC10943019 DOI: 10.1038/s41598-024-56893-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.
Collapse
Affiliation(s)
- Lorenzo Pallante
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Marco Cannariato
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | | | - Eric A Zizzi
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Agorakis Bompotas
- Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece
| | - Xhesika Hada
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI, 6962, Lugano-Viganello, Switzerland
| | | | - Seferina Mavroudi
- Department of Nursing, School of Health Rehabilitation Sciences, University of Patras, 265 04, Patras, Greece
| | | | | | - Marco A Deriu
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy.
| |
Collapse
|
4
|
Li H, Sun X, Cui W, Xu M, Dong J, Ekundayo BE, Ni D, Rao Z, Guo L, Stahlberg H, Yuan S, Vogel H. Computational drug development for membrane protein targets. Nat Biotechnol 2024; 42:229-242. [PMID: 38361054 DOI: 10.1038/s41587-023-01987-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/13/2023] [Indexed: 02/17/2024]
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
Collapse
Affiliation(s)
- Haijian Li
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Xiaolin Sun
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Wenqiang Cui
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Marc Xu
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junlin Dong
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Babatunde Edukpe Ekundayo
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zhili Rao
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Liwei Guo
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Shuguang Yuan
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
| | - Horst Vogel
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
- Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| |
Collapse
|
5
|
McNaught-Flores DA, Kooistra AJ, Chen YC, Arias-Montano JA, Panula P, Leurs R. Pharmacological Characterization of the Zebrafish (Danio Rerio) Histamine H 1 Receptor Reveals the Involvement of the Second Extracellular Loop in the Binding of Histamine. Mol Pharmacol 2024; 105:84-96. [PMID: 37977823 DOI: 10.1124/molpharm.123.000741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 10/11/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023] Open
Abstract
The zebrafish (Danio rerio) histamine H1 receptor gene (zfH1R) was cloned in 2007 and reported to be involved in fish locomotion. Yet, no detailed characterization of its pharmacology and signaling properties have so far been reported. In this study, we pharmacologically characterized the zfH1R expressed in HEK-293T cells by means of [3H]-mepyramine binding and G protein-signaling assays. The zfH1R [dissociation constant (KD), 0.7 nM] displayed similar affinity for the antagonist [3H]-mepyramine as the human histamine H1 receptor (hH1R) (KD, 1.5 nM), whereas the affinity for histamine is 100-fold higher than for the human H1R. The zfH1R couples to Gαq/11 proteins and activates several reporter genes, i.e., NFAT, NFϰB, CRE, VEGF, COX-2, SRE, and AP-1, and zfH1R-mediated signaling is prevented by the Gαq/11 inhibitor YM-254890 and the antagonist mepyramine. Molecular modeling of the zfH1R and human H1R shows that the binding pockets are identical, implying that variations along the ligand binding pathway could underly the differences in histamine affinity instead. Targeting differentially charged residues in extracellular loop 2 (ECL2) using site-directed mutagenesis revealed that Arg21045x55 is most likely involved in the binding process of histamine in zfH1R. This study aids the understanding of the pharmacological differences between H1R orthologs and the role of ECL2 in histamine binding and provides fundamental information for the understanding of the histaminergic system in the zebrafish. SIGNIFICANCE STATEMENT: The use of the zebrafish as in vivo models in neuroscience is growing exponentially, which asks for detailed characterization of the aminergic neurotransmitter systems in this model. This study is the first to pharmacologically characterize the zebrafish histamine H1 receptor after expression in HEK-293T cells. The results show a high pharmacological and functional resemblance with the human ortholog but also reveal interesting structural differences and unveils an important role of the second extracellular loop in histamine binding.
Collapse
Affiliation(s)
- Daniel A McNaught-Flores
- Amsterdam Institute for Molecules, Medicines, and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands (D.A.M.-F., A.J.K., R.L.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); Department of Anatomy, University of Helsinki, Helsinki, Finland (Y.-C.C., P.P.); and Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del IPN, Ciudad de México, México (J.-A.A.-M.)
| | - Albert J Kooistra
- Amsterdam Institute for Molecules, Medicines, and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands (D.A.M.-F., A.J.K., R.L.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); Department of Anatomy, University of Helsinki, Helsinki, Finland (Y.-C.C., P.P.); and Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del IPN, Ciudad de México, México (J.-A.A.-M.)
| | - Yu-Chia Chen
- Amsterdam Institute for Molecules, Medicines, and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands (D.A.M.-F., A.J.K., R.L.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); Department of Anatomy, University of Helsinki, Helsinki, Finland (Y.-C.C., P.P.); and Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del IPN, Ciudad de México, México (J.-A.A.-M.)
| | - Jose-Antonio Arias-Montano
- Amsterdam Institute for Molecules, Medicines, and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands (D.A.M.-F., A.J.K., R.L.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); Department of Anatomy, University of Helsinki, Helsinki, Finland (Y.-C.C., P.P.); and Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del IPN, Ciudad de México, México (J.-A.A.-M.)
| | - Pertti Panula
- Amsterdam Institute for Molecules, Medicines, and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands (D.A.M.-F., A.J.K., R.L.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); Department of Anatomy, University of Helsinki, Helsinki, Finland (Y.-C.C., P.P.); and Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del IPN, Ciudad de México, México (J.-A.A.-M.)
| | - Rob Leurs
- Amsterdam Institute for Molecules, Medicines, and Systems (AIMMS), Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands (D.A.M.-F., A.J.K., R.L.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); Department of Anatomy, University of Helsinki, Helsinki, Finland (Y.-C.C., P.P.); and Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del IPN, Ciudad de México, México (J.-A.A.-M.)
| |
Collapse
|
6
|
The chronological evolution of fluorescent GPCR probes for bioimaging. Coord Chem Rev 2023. [DOI: 10.1016/j.ccr.2023.215040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
|
7
|
Chiesa L, Kellenberger E. One class classification for the detection of β2 adrenergic receptor agonists using single-ligand dynamic interaction data. J Cheminform 2022; 14:74. [PMID: 36309734 PMCID: PMC9617447 DOI: 10.1186/s13321-022-00654-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/17/2022] [Indexed: 11/22/2022] Open
Abstract
G protein-coupled receptors are involved in many biological processes, relaying the extracellular signal inside the cell. Signaling is regulated by the interactions between receptors and their ligands, it can be stimulated by agonists, or inhibited by antagonists or inverse agonists. The development of a new drug targeting a member of this family requires to take into account the pharmacological profile of the designed ligands in order to elicit the desired response. The structure-based virtual screening of chemical libraries may prioritize a specific class of ligands by combining docking results and ligand binding information provided by crystallographic structures. The performance of the method depends on the relevance of the structural data, in particular the conformation of the targeted site, the binding mode of the reference ligand, and the approach used to compare the interactions formed by the docked ligand with those formed by the reference ligand in the crystallographic structure. Here, we propose a new method based on the conformational dynamics of a single protein–ligand reference complex to improve the biased selection of ligands with specific pharmacological properties in a structure-based virtual screening exercise. Interactions patterns between a reference agonist and the receptor, here exemplified on the β2 adrenergic receptor, were extracted from molecular dynamics simulations of the agonist/receptor complex and encoded in graphs used to train a one-class machine learning classifier. Different conditions were tested: low to high affinity agonists, varying simulation duration, considering or ignoring hydrophobic contacts, and tuning of the classifier parametrization. The best models applied to post-process raw data from retrospective virtual screening obtained by docking of test libraries effectively filtered out irrelevant poses, discarding inactive and non-agonist ligands while identifying agonists. Taken together, our results suggest that consistency of the binding mode during the simulation is a key to the success of the method.
Collapse
Affiliation(s)
- Luca Chiesa
- Laboratoire d'innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, 67400, Illkirch, France
| | - Esther Kellenberger
- Laboratoire d'innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, 67400, Illkirch, France.
| |
Collapse
|
8
|
Jiménez-Rosés M, Morgan BA, Jimenez Sigstad M, Tran TDZ, Srivastava R, Bunsuz A, Borrega-Román L, Hompluem P, Cullum SA, Harwood CR, Koers EJ, Sykes DA, Styles IB, Veprintsev DB. Combined docking and machine learning identify key molecular determinants of ligand pharmacological activity on β2 adrenoceptor. Pharmacol Res Perspect 2022; 10:e00994. [PMID: 36029004 PMCID: PMC9418666 DOI: 10.1002/prp2.994] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/21/2022] [Indexed: 11/06/2022] Open
Abstract
G protein‐coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesized that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over‐represented. We computationally docked ~2700 known β2AR ligands to multiple β2AR structures, generating ca 75 000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. We demonstrate with the application of ML methods that it is possible to identify the key interactions associated with agonism or antagonism of ligands. The most representative interactions for agonist ligands involve K972.68×67, F194ECL2, S2035.42×43, S2045.43×44, S2075.46×641, H2966.58×58, and K3057.32×31. Meanwhile, the antagonist ligands made interactions with W2866.48×48 and Y3167.43×42, both residues considered to be important in GPCR activation. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure‐activity relationship that identifies small changes to the ligands that invert their pharmacological activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.
Collapse
Affiliation(s)
- Mireia Jiménez-Rosés
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Bradley Angus Morgan
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,School of Computer Science, University of Birmingham, Birmingham, UK.,The Alan Turing Institute, London, UK.,MRC IMPACT Doctoral Training Programme, Universities of Birmingham, Leicester and Nottingham, Midlands, UK
| | - Maria Jimenez Sigstad
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Thuy Duong Zoe Tran
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,MSc Programme in Drug Discovery & Pharmaceutical Sciences, University of Nottingham, Nottingham, UK
| | - Rohini Srivastava
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,MSc Programme in Drug Discovery & Pharmaceutical Sciences, University of Nottingham, Nottingham, UK
| | - Asuman Bunsuz
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Leire Borrega-Román
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK.,Department of Pharmacology, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain.,Bioaraba, Neurofarmacología Celular y Molecular, Vitoria-Gasteiz, Spain
| | - Pattarin Hompluem
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Sean A Cullum
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK.,MRC IMPACT Doctoral Training Programme, Universities of Birmingham, Leicester and Nottingham, Midlands, UK
| | - Clare R Harwood
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK.,MRC IMPACT Doctoral Training Programme, Universities of Birmingham, Leicester and Nottingham, Midlands, UK
| | - Eline J Koers
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - David A Sykes
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Iain B Styles
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,School of Computer Science, University of Birmingham, Birmingham, UK.,The Alan Turing Institute, London, UK
| | - Dmitry B Veprintsev
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham and University of Nottingham, Midlands, UK.,Division of Physiology, Pharmacology & Neuroscience, School of Life Sciences, University of Nottingham, Nottingham, UK
| |
Collapse
|
9
|
Kok ZY, Stoddart LA, Mistry SJ, Mocking TAM, Vischer HF, Leurs R, Hill SJ, Mistry SN, Kellam B. Optimization of Peptide Linker-Based Fluorescent Ligands for the Histamine H 1 Receptor. J Med Chem 2022; 65:8258-8288. [PMID: 35734860 PMCID: PMC9234962 DOI: 10.1021/acs.jmedchem.2c00125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The histamine H1 receptor (H1R) has recently been implicated in mediating cell proliferation and cancer progression; therefore, high-affinity H1R-selective fluorescent ligands are desirable tools for further investigation of this behavior in vitro and in vivo. We previously reported a H1R fluorescent ligand, bearing a peptide-linker, based on antagonist VUF13816 and sought to further explore structure-activity relationships (SARs) around the linker, orthostere, and fluorescent moieties. Here, we report a series of high-affinity H1R fluorescent ligands varying in peptide linker composition, orthosteric targeting moiety, and fluorophore. Incorporation of a boron-dipyrromethene (BODIPY) 630/650-based fluorophore conferred high binding affinity to our H1R fluorescent ligands, remarkably overriding the linker SAR observed in corresponding unlabeled congeners. Compound 31a, both potent and subtype-selective, enabled H1R visualization using confocal microscopy at a concentration of 10 nM. Molecular docking of 31a with the human H1R predicts that the optimized peptide linker makes interactions with key residues in the receptor.
Collapse
Affiliation(s)
- Zhi Yuan Kok
- Division of Biomolecular Science and Medicinal Chemistry, School of Pharmacy, University of Nottingham Biodiscovery Institute, University Park, Nottingham NG7 2RD, U.K.,Centre of Membrane Proteins and Receptors, University of Birmingham and University of Nottingham, the Midlands, Nottingham NG7 2UH, U.K
| | - Leigh A Stoddart
- Division of Physiology, Pharmacology & Neuroscience, Medical School, School of Life Sciences, University of Nottingham, Nottingham NG7 2UH, U.K.,Centre of Membrane Proteins and Receptors, University of Birmingham and University of Nottingham, the Midlands, Nottingham NG7 2UH, U.K
| | - Sarah J Mistry
- Division of Biomolecular Science and Medicinal Chemistry, School of Pharmacy, University of Nottingham Biodiscovery Institute, University Park, Nottingham NG7 2RD, U.K.,Centre of Membrane Proteins and Receptors, University of Birmingham and University of Nottingham, the Midlands, Nottingham NG7 2UH, U.K
| | - Tamara A M Mocking
- Amsterdam Institute for Molecules, Medicines and Systems, Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelean 1083, 1083 HV Amsterdam, The Netherlands
| | - Henry F Vischer
- Amsterdam Institute for Molecules, Medicines and Systems, Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelean 1083, 1083 HV Amsterdam, The Netherlands
| | - Rob Leurs
- Amsterdam Institute for Molecules, Medicines and Systems, Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelean 1083, 1083 HV Amsterdam, The Netherlands
| | - Stephen J Hill
- Division of Physiology, Pharmacology & Neuroscience, Medical School, School of Life Sciences, University of Nottingham, Nottingham NG7 2UH, U.K.,Centre of Membrane Proteins and Receptors, University of Birmingham and University of Nottingham, the Midlands, Nottingham NG7 2UH, U.K
| | - Shailesh N Mistry
- Division of Biomolecular Science and Medicinal Chemistry, School of Pharmacy, University of Nottingham Biodiscovery Institute, University Park, Nottingham NG7 2RD, U.K
| | - Barrie Kellam
- Division of Biomolecular Science and Medicinal Chemistry, School of Pharmacy, University of Nottingham Biodiscovery Institute, University Park, Nottingham NG7 2RD, U.K.,Centre of Membrane Proteins and Receptors, University of Birmingham and University of Nottingham, the Midlands, Nottingham NG7 2UH, U.K
| |
Collapse
|
10
|
Yau MQ, Loo JSE. Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA. J Comput Aided Mol Des 2022; 36:427-441. [PMID: 35581483 DOI: 10.1007/s10822-022-00456-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 04/28/2022] [Indexed: 01/09/2023]
Abstract
The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.
Collapse
Affiliation(s)
- Mei Qian Yau
- Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylor's, 47500, Subang Jaya, Selangor, Malaysia.,School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia
| | - Jason S E Loo
- Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylor's, 47500, Subang Jaya, Selangor, Malaysia. .,School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
| |
Collapse
|
11
|
Neumann A, Attah I, Al-Hroub H, Namasivayam V, Müller CE. Discovery of P2Y 2 Receptor Antagonist Scaffolds through Virtual High-Throughput Screening. J Chem Inf Model 2022; 62:1538-1549. [DOI: 10.1021/acs.jcim.1c01235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Alexander Neumann
- PharmaCenter Bonn, Pharmaceutical Institute, Pharmaceutical Sciences Bonn (PSB), Pharmaceutical & Medicinal Chemistry, University of Bonn, 53121 Bonn, Germany
- Research Training Group 1873, University of Bonn, 53127 Bonn, Germany
| | - Isaac Attah
- PharmaCenter Bonn, Pharmaceutical Institute, Pharmaceutical Sciences Bonn (PSB), Pharmaceutical & Medicinal Chemistry, University of Bonn, 53121 Bonn, Germany
| | - Haneen Al-Hroub
- PharmaCenter Bonn, Pharmaceutical Institute, Pharmaceutical Sciences Bonn (PSB), Pharmaceutical & Medicinal Chemistry, University of Bonn, 53121 Bonn, Germany
| | - Vigneshwaran Namasivayam
- PharmaCenter Bonn, Pharmaceutical Institute, Pharmaceutical Sciences Bonn (PSB), Pharmaceutical & Medicinal Chemistry, University of Bonn, 53121 Bonn, Germany
| | - Christa E. Müller
- PharmaCenter Bonn, Pharmaceutical Institute, Pharmaceutical Sciences Bonn (PSB), Pharmaceutical & Medicinal Chemistry, University of Bonn, 53121 Bonn, Germany
- Research Training Group 1873, University of Bonn, 53127 Bonn, Germany
| |
Collapse
|
12
|
Ballante F, Kooistra AJ, Kampen S, de Graaf C, Carlsson J. Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You? Pharmacol Rev 2021; 73:527-565. [PMID: 34907092 DOI: 10.1124/pharmrev.120.000246] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.
Collapse
Affiliation(s)
- Flavio Ballante
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Albert J Kooistra
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Stefanie Kampen
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Chris de Graaf
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| |
Collapse
|
13
|
Brown AJH, Bradley SJ, Marshall FH, Brown GA, Bennett KA, Brown J, Cansfield JE, Cross DM, de Graaf C, Hudson BD, Dwomoh L, Dias JM, Errey JC, Hurrell E, Liptrot J, Mattedi G, Molloy C, Nathan PJ, Okrasa K, Osborne G, Patel JC, Pickworth M, Robertson N, Shahabi S, Bundgaard C, Phillips K, Broad LM, Goonawardena AV, Morairty SR, Browning M, Perini F, Dawson GR, Deakin JFW, Smith RT, Sexton PM, Warneck J, Vinson M, Tasker T, Tehan BG, Teobald B, Christopoulos A, Langmead CJ, Jazayeri A, Cooke RM, Rucktooa P, Congreve MS, Weir M, Tobin AB. From structure to clinic: Design of a muscarinic M1 receptor agonist with potential to treatment of Alzheimer's disease. Cell 2021; 184:5886-5901.e22. [PMID: 34822784 PMCID: PMC7616177 DOI: 10.1016/j.cell.2021.11.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 04/29/2021] [Accepted: 11/01/2021] [Indexed: 12/31/2022]
Abstract
Current therapies for Alzheimer's disease seek to correct for defective cholinergic transmission by preventing the breakdown of acetylcholine through inhibition of acetylcholinesterase, these however have limited clinical efficacy. An alternative approach is to directly activate cholinergic receptors responsible for learning and memory. The M1-muscarinic acetylcholine (M1) receptor is the target of choice but has been hampered by adverse effects. Here we aimed to design the drug properties needed for a well-tolerated M1-agonist with the potential to alleviate cognitive loss by taking a stepwise translational approach from atomic structure, cell/tissue-based assays, evaluation in preclinical species, clinical safety testing, and finally establishing activity in memory centers in humans. Through this approach, we rationally designed the optimal properties, including selectivity and partial agonism, into HTL9936-a potential candidate for the treatment of memory loss in Alzheimer's disease. More broadly, this demonstrates a strategy for targeting difficult GPCR targets from structure to clinic.
Collapse
Affiliation(s)
- Alastair J H Brown
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Sophie J Bradley
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK; The Centre for Translational Pharmacology, Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Fiona H Marshall
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Giles A Brown
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Kirstie A Bennett
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Jason Brown
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Julie E Cansfield
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - David M Cross
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK; Cross Pharma Consulting Ltd, 20-22 Wenlock Road, London, N17GU, UK
| | - Chris de Graaf
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Brian D Hudson
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Louis Dwomoh
- The Centre for Translational Pharmacology, Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - João M Dias
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - James C Errey
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Edward Hurrell
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Jan Liptrot
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Giulio Mattedi
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Colin Molloy
- The Centre for Translational Pharmacology, Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Pradeep J Nathan
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK; Brain Mapping Unit, University of Cambridge, Department of Psychiatry, Herchel Smith Building, Cambridge, CB20SZ, UK
| | - Krzysztof Okrasa
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Greg Osborne
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Jayesh C Patel
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Mark Pickworth
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Nathan Robertson
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Shahram Shahabi
- Eli Lilly & Co, Neuroscience Discovery, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Christoffer Bundgaard
- Eli Lilly & Co, Neuroscience Discovery, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK; H. Lundbeck A/S, Neuroscience Research, Ottiliavej 9, 2500 Valby, Copenhagen, Denmark
| | - Keith Phillips
- Eli Lilly & Co, Neuroscience Discovery, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Lisa M Broad
- Eli Lilly & Co, Neuroscience Discovery, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
| | - Anushka V Goonawardena
- Center for Neuroscience, Biosciences Division, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA
| | - Stephen R Morairty
- Center for Neuroscience, Biosciences Division, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA
| | - Michael Browning
- University Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX12JD, UK; P1vital, Manor house, Howbery Buisness Park, Wallingford, OX108BA, UK
| | - Francesca Perini
- Centre for Cognitive Neuroscience - Duke-NUS Medical School, 8 College Road, 169857, Singapore
| | - Gerard R Dawson
- University Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX12JD, UK
| | - John F W Deakin
- Neuroscience and Psychiatry Unit, University of Manchester, Manchester, M139PT UK
| | - Robert T Smith
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Patrick M Sexton
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences and Department of Pharmacology, Monash University, Parkville 3052, Victoria, Australia; ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Victoria, Australia
| | - Julie Warneck
- Protogenia Consulting Ltd, PO-Box 289, Ely, CB79DR, UK
| | - Mary Vinson
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Tim Tasker
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Benjamin G Tehan
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Barry Teobald
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Arthur Christopoulos
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences and Department of Pharmacology, Monash University, Parkville 3052, Victoria, Australia
| | - Christopher J Langmead
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK; Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences and Department of Pharmacology, Monash University, Parkville 3052, Victoria, Australia
| | - Ali Jazayeri
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Robert M Cooke
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Prakash Rucktooa
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Miles S Congreve
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK
| | - Malcolm Weir
- Sosei-Heptares, Steinmetz Building, Granta Park, Cambridge, CB21 6DG, UK.
| | - Andrew B Tobin
- The Centre for Translational Pharmacology, Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK.
| |
Collapse
|
14
|
Kricker JA, Page CP, Gardarsson FR, Baldursson O, Gudjonsson T, Parnham MJ. Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development. Pharmacol Rev 2021; 73:233-262. [PMID: 34716226 DOI: 10.1124/pharmrev.121.000300] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Macrolides are among the most widely prescribed broad spectrum antibacterials, particularly for respiratory infections. It is now recognized that these drugs, in particular azithromycin, also exert time-dependent immunomodulatory actions that contribute to their therapeutic benefit in both infectious and other chronic inflammatory diseases. Their increased chronic use in airway inflammation and, more recently, of azithromycin in COVID-19, however, has led to a rise in bacterial resistance. An additional crucial aspect of chronic airway inflammation, such as chronic obstructive pulmonary disease, as well as other inflammatory disorders, is the loss of epithelial barrier protection against pathogens and pollutants. In recent years, azithromycin has been shown with time to enhance the barrier properties of airway epithelial cells, an action that makes an important contribution to its therapeutic efficacy. In this article, we review the background and evidence for various immunomodulatory and time-dependent actions of macrolides on inflammatory processes and on the epithelium and highlight novel nonantibacterial macrolides that are being studied for immunomodulatory and barrier-strengthening properties to circumvent the risk of bacterial resistance that occurs with macrolide antibacterials. We also briefly review the clinical effects of macrolides in respiratory and other inflammatory diseases associated with epithelial injury and propose that the beneficial epithelial effects of nonantibacterial azithromycin derivatives in chronic inflammation, even given prophylactically, are likely to gain increasing attention in the future. SIGNIFICANCE STATEMENT: Based on its immunomodulatory properties and ability to enhance the protective role of the lung epithelium against pathogens, azithromycin has proven superior to other macrolides in treating chronic respiratory inflammation. A nonantibiotic azithromycin derivative is likely to offer prophylactic benefits against inflammation and epithelial damage of differing causes while preserving the use of macrolides as antibiotics.
Collapse
Affiliation(s)
- Jennifer A Kricker
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Clive P Page
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Fridrik Runar Gardarsson
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Olafur Baldursson
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Thorarinn Gudjonsson
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| | - Michael J Parnham
- EpiEndo Pharmaceuticals, Reykjavik, Iceland (J.A.K., C.P.P., F.R.G., O.B., T.G., M.J.P.); Stem Cell Research Unit, Biomedical Center, University of Iceland, Reykjavik, Iceland (J.A.K., T.G.); Sackler Institute of Pulmonary Pharmacology, Institute of Pharmaceutical Science, King's College London, London, United Kingdom (C.P.P.); Department of Respiratory Medicine (O.B.), Department of Laboratory Hematology (T.G.), Landspitali-University Hospital, Reykjavik, Iceland; Faculty of Biochemistry, Chemistry and Pharmacy, JW Goethe University Frankfurt am Main, Germany (M.J.P.)
| |
Collapse
|
15
|
Yin Y, Hu H, Yang Z, Xu H, Wu J. RealVS: Toward Enhancing the Precision of Top Hits in Ligand-Based Virtual Screening of Drug Leads from Large Compound Databases. J Chem Inf Model 2021; 61:4924-4939. [PMID: 34619030 DOI: 10.1021/acs.jcim.1c01021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Accurate modeling of compound bioactivities is essential for the virtual screening of drug leads. In real-world scenarios, pharmacists tend to choose from the top-k hit compounds ranked by predicted bioactivities from a large database with interest to continue wet experiments for drug discovery. Significant improvement of the precision of the top hits in ligand-based virtual screening of drug leads is more valuable than conventional schemes for accurately predicting the bioactivities of all compounds from a large database. Here, we proposed a new method, RealVS, to significantly improve the top hits' precision and learn interpretable key substructures associated with compound bioactivities. The features of RealVS involve the following points. (1) Abundant transferable information from the source domain was introduced for alleviating the insufficiency of inactive ligands associated with drug targets. (2) The adversarial domain alignment was adopted to fit the distribution of generated features of compounds from the training data set and that from the screening database for greater model generalization ability. (3) A novel objective function was proposed to simultaneously optimize the classification loss, regression loss, and adversarial loss, where most inactive ligands tend to be screened out before activity regression prediction. (4) Graph attention networks were adopted for learning key substructures associated with ligand bioactivities for better model interpretability. The results on a large number of benchmark data sets show that our method has significantly improved the precision of top hits under various k values in ligand-based virtual screening of drug leads from large compound databases, which is of great value in real-world scenarios. The web server of RealVS is freely available at noveldelta.com/RealVS for academic purposes, where virtual screening of hits from large compound databases is accessible.
Collapse
Affiliation(s)
- Yueming Yin
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Haifeng Hu
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Zhen Yang
- National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Huajian Xu
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Jiansheng Wu
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| |
Collapse
|
16
|
Hasan M, Parvez MSA, Azim KF, Imran MAS, Raihan T, Gulshan A, Muhit S, Akhand RN, Ahmed SSU, Uddin MB. Main protease inhibitors and drug surface hotspots for the treatment of COVID-19: A drug repurposing and molecular docking approach. Biomed Pharmacother 2021; 140:111742. [PMID: 34052565 PMCID: PMC8130501 DOI: 10.1016/j.biopha.2021.111742] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/07/2021] [Accepted: 05/13/2021] [Indexed: 12/14/2022] Open
Abstract
Here, drug repurposing and molecular docking were employed to screen approved MPP inhibitors and their derivatives to suggest a specific therapeutic agent for the treatment of COVID-19. The approved MPP inhibitors against HIV and HCV were prioritized, while RNA dependent RNA Polymerase (RdRp) inhibitor remdesivir including Favipiravir, alpha-ketoamide were studied as control groups. The target drug surface hotspot was also investigated through the molecular docking technique. Molecular dynamics was performed to determine the binding stability of docked complexes. Absorption, distribution, metabolism, and excretion analysis was conducted to understand the pharmacokinetics and drug-likeness of the screened MPP inhibitors. The results of the study revealed that Paritaprevir (-10.9 kcal/mol) and its analog (CID 131982844) (-16.3 kcal/mol) showed better binding affinity than the approved MPP inhibitors compared in this study, including remdesivir, Favipiravir, and alpha-ketoamide. A comparative study among the screened putative MPP inhibitors revealed that the amino acids T25, T26, H41, M49, L141, N142, G143, C145, H164, M165, E166, D187, R188, and Q189 are at potentially critical positions for being surface hotspots in the MPP of SARS-CoV-2. The top 5 predicted drugs (Paritaprevir, Glecaprevir, Nelfinavir, and Lopinavir) and the topmost analog showed conformational stability in the active site of the SARS-CoV-2 MP protein. The study also suggested that Paritaprevir and its analog (CID 131982844) might be effective against SARS-CoV-2. The current findings are limited to in silico analysis and lack in vivo efficacy testing; thus, we strongly recommend a quick assessment of Paritaprevir and its analog (CID 131982844) in a clinical trial.
Collapse
Affiliation(s)
- Mahmudul Hasan
- Department of Pharmaceuticals and Industrial Biotechnology, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Md Sorwer Alam Parvez
- Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Kazi Faizul Azim
- Department of Microbial Biotechnology, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Md Abdus Shukur Imran
- Department of Pharmaceuticals and Industrial Biotechnology, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Topu Raihan
- Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Airin Gulshan
- Faculty of Biotechnology and Genetic Engineering, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Samuel Muhit
- Department of Epidemiology and Public Health, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Rubaiat Nazneen Akhand
- Department of Biochemistry and Chemistry, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Syed Sayeem Uddin Ahmed
- Department of Epidemiology and Public Health, Sylhet Agricultural University, Sylhet 3100, Bangladesh.
| | - Md Bashir Uddin
- Department of Medicine, Sylhet Agricultural University, Sylhet 3100, Bangladesh.
| |
Collapse
|
17
|
Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, Kruger HG. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224:113705. [PMID: 34303871 DOI: 10.1016/j.ejmech.2021.113705] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/12/2021] [Accepted: 07/12/2021] [Indexed: 12/30/2022]
Abstract
Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.
Collapse
Affiliation(s)
- Victor T Sabe
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Thandokuhle Ntombela
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| | - Lindiwe A Jhamba
- HIV Pathogenesis Program, School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Glenn E M Maguire
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa; School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Thavendran Govender
- Faculty of Science and Agriculture, Department of Chemistry, University of Zululand, KwaDlangezwa, 3886, South Africa
| | - Tricia Naicker
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa
| | - Hendrik G Kruger
- Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa.
| |
Collapse
|
18
|
Wu Y, Zeng L, Zhao S. Ligands of Adrenergic Receptors: A Structural Point of View. Biomolecules 2021; 11:936. [PMID: 34202543 PMCID: PMC8301793 DOI: 10.3390/biom11070936] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/09/2021] [Accepted: 06/12/2021] [Indexed: 01/14/2023] Open
Abstract
Adrenergic receptors are G protein-coupled receptors for epinephrine and norepinephrine. They are targets of many drugs for various conditions, including treatment of hypertension, hypotension, and asthma. Adrenergic receptors are intensively studied in structural biology, displayed for binding poses of different types of ligands. Here, we summarized molecular mechanisms of ligand recognition and receptor activation exhibited by structure. We also reviewed recent advances in structure-based ligand discovery against adrenergic receptors.
Collapse
Affiliation(s)
- Yiran Wu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; (Y.W.); (L.Z.)
| | - Liting Zeng
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; (Y.W.); (L.Z.)
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; (Y.W.); (L.Z.)
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| |
Collapse
|
19
|
Discovery of Novel Allosteric Modulators Targeting an Extra-Helical Binding Site of GLP-1R Using Structure- and Ligand-Based Virtual Screening. Biomolecules 2021; 11:biom11070929. [PMID: 34201418 PMCID: PMC8301998 DOI: 10.3390/biom11070929] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/12/2021] [Accepted: 06/16/2021] [Indexed: 12/18/2022] Open
Abstract
Allosteric modulators have emerged with many potential pharmacological advantages as they do not compete the binding of agonist or antagonist to the orthosteric sites but ultimately affect downstream signaling. To identify allosteric modulators targeting an extra-helical binding site of the glucagon-like peptide-1 receptor (GLP-1R) within the membrane environment, the following two computational approaches were applied: structure-based virtual screening with consideration of lipid contacts and ligand-based virtual screening with the maintenance of specific allosteric pocket residue interactions. Verified by radiolabeled ligand binding and cAMP accumulation experiments, two negative allosteric modulators and seven positive allosteric modulators were discovered using structure-based and ligand-based virtual screening methods, respectively. The computational approach presented here could possibly be used to discover allosteric modulators of other G protein-coupled receptors.
Collapse
|
20
|
Thomas M, Smith RT, O'Boyle NM, de Graaf C, Bender A. Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform 2021; 13:39. [PMID: 33985583 PMCID: PMC8117600 DOI: 10.1186/s13321-021-00516-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/02/2021] [Indexed: 12/14/2022] Open
Abstract
Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.
Collapse
Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Robert T Smith
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Noel M O'Boyle
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Chris de Graaf
- Computational Chemistry, Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK.
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| |
Collapse
|
21
|
Mehta P, Miszta P, Filipek S. Molecular Modeling of Histamine Receptors-Recent Advances in Drug Discovery. Molecules 2021; 26:1778. [PMID: 33810008 PMCID: PMC8004658 DOI: 10.3390/molecules26061778] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/17/2021] [Accepted: 03/19/2021] [Indexed: 12/11/2022] Open
Abstract
The recent developments of fast reliable docking, virtual screening and other algorithms gave rise to discovery of many novel ligands of histamine receptors that could be used for treatment of allergic inflammatory disorders, central nervous system pathologies, pain, cancer and obesity. Furthermore, the pharmacological profiles of ligands clearly indicate that these receptors may be considered as targets not only for selective but also for multi-target drugs that could be used for treatment of complex disorders such as Alzheimer's disease. Therefore, analysis of protein-ligand recognition in the binding site of histamine receptors and also other molecular targets has become a valuable tool in drug design toolkit. This review covers the period 2014-2020 in the field of theoretical investigations of histamine receptors mostly based on molecular modeling as well as the experimental characterization of novel ligands of these receptors.
Collapse
Affiliation(s)
| | | | - Sławomir Filipek
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, Poland or (P.M.); (P.M.)
| |
Collapse
|
22
|
Guedes IA, Barreto AMS, Marinho D, Krempser E, Kuenemann MA, Sperandio O, Dardenne LE, Miteva MA. New machine learning and physics-based scoring functions for drug discovery. Sci Rep 2021; 11:3198. [PMID: 33542326 PMCID: PMC7862620 DOI: 10.1038/s41598-021-82410-1] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.
Collapse
Affiliation(s)
- Isabella A Guedes
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.,Inserm U973, Université Paris Diderot, Paris, France
| | - André M S Barreto
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil
| | - Diogo Marinho
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil
| | | | | | - Olivier Sperandio
- Inserm U973, Université Paris Diderot, Paris, France.,Structural Bioinformatics Unit, CNRS UMR3528, Institut Pasteur, 75015, Paris, France
| | - Laurent E Dardenne
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.
| | - Maria A Miteva
- Inserm U973, Université Paris Diderot, Paris, France. .,Inserm U1268 "Medicinal Chemistry and Translational Research", CiTCoM, UMR 8038, CNRS, Université de Paris, 75006, Paris, France.
| |
Collapse
|
23
|
Yang D, Zhou Q, Labroska V, Qin S, Darbalaei S, Wu Y, Yuliantie E, Xie L, Tao H, Cheng J, Liu Q, Zhao S, Shui W, Jiang Y, Wang MW. G protein-coupled receptors: structure- and function-based drug discovery. Signal Transduct Target Ther 2021; 6:7. [PMID: 33414387 PMCID: PMC7790836 DOI: 10.1038/s41392-020-00435-w] [Citation(s) in RCA: 242] [Impact Index Per Article: 80.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/30/2020] [Accepted: 12/05/2020] [Indexed: 02/08/2023] Open
Abstract
As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.
Collapse
Affiliation(s)
- Dehua Yang
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Qingtong Zhou
- School of Basic Medical Sciences, Fudan University, 200032, Shanghai, China
| | - Viktorija Labroska
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Shanshan Qin
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Sanaz Darbalaei
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Yiran Wu
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Elita Yuliantie
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Linshan Xie
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Houchao Tao
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Jianjun Cheng
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Qing Liu
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China. .,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
| | - Yi Jiang
- The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.
| | - Ming-Wei Wang
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China. .,The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China. .,School of Basic Medical Sciences, Fudan University, 200032, Shanghai, China. .,University of Chinese Academy of Sciences, 100049, Beijing, China. .,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China. .,School of Pharmacy, Fudan University, 201203, Shanghai, China.
| |
Collapse
|
24
|
Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules 2020; 25:E4723. [PMID: 33076254 PMCID: PMC7587536 DOI: 10.3390/molecules25204723] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 12/20/2022] Open
Abstract
Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.
Collapse
Affiliation(s)
- Javier Vázquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
| | - Manel López
- AB Science, Parc Scientifique de Luminy, Zone Luminy Enterprise, Case 922, 163 Av. de Luminy, 13288 Marseille, France;
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - F. Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
| |
Collapse
|
25
|
Abstract
Background:
Molecular docking is probably the most popular and profitable approach in
computer-aided drug design, being the staple technique for predicting the binding mode of bioactive
compounds and for performing receptor-based virtual screening studies. The growing attention received
by docking, as well as the need for improving its reliability in pose prediction and virtual screening
performance, has led to the development of a wide plethora of new docking algorithms and scoring
functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in
terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets.
Methods:
In this context, consensus docking approaches are taking hold in computer-aided drug design.
These computational protocols consist in docking ligands using multiple docking methods and then
comparing the binding poses predicted for the same ligand by the different methods. This analysis is
usually carried out calculating the root-mean-square deviation among the different docking results obtained
for each ligand, in order to identify the number of docking methods producing the same binding
pose.
Results:
The consensus docking approaches demonstrated to improve the quality of docking and virtual
screening results compared to the single docking methods. From a qualitative point of view, the improvement
in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a
high number of docking methods, whereas with regards to virtual screening studies, high hit rates were
obtained by prioritizing the compounds showing a high level of pose consensus.
Conclusion:
In this review, we provide an overview of the results obtained from the performance assessment
of various consensus docking protocols and we illustrate successful case studies where consensus
docking has been applied in virtual screening studies.
Collapse
Affiliation(s)
- Giulio Poli
- Department of Pharmacy, University of Pisa, Pisa, Italy
| | | |
Collapse
|
26
|
Scharf MM, Zimmermann M, Wilhelm F, Stroe R, Waldhoer M, Kolb P. A Focus on Unusual ECL2 Interactions Yields β 2 -Adrenergic Receptor Antagonists with Unprecedented Scaffolds. ChemMedChem 2020; 15:882-890. [PMID: 32301583 PMCID: PMC7318225 DOI: 10.1002/cmdc.201900715] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 03/11/2020] [Indexed: 11/15/2022]
Abstract
The binding pockets of aminergic G protein-coupled receptors are often targeted by drugs and virtual screening campaigns. In order to find ligands with unprecedented scaffolds for one of the best-investigated receptors of this subfamily, the β2 -adrenergic receptor, we conducted a docking-based screen insisting that molecules would address previously untargeted residues in extracellular loop 2. We here report the discovery of ligands with a previously undescribed coumaran-based scaffold. Furthermore, we provide an analysis of the added value that X-ray structures in different conformations deliver for such docking screens.
Collapse
Affiliation(s)
- Magdalena M. Scharf
- Department of Pharmaceutical ChemistryPhilipps-University MarburgMarbacher Weg 635037MarburgGermany
| | | | - Florian Wilhelm
- InterAx BiotechPARK innovAARE5234VilligenSwitzerland
- Department of Biosystems Science and Engineering ETHETH ZürichMattenstrasse 264058BaselSwitzerland
| | - Raimond Stroe
- InterAx BiotechPARK innovAARE5234VilligenSwitzerland
- Department of Drug Design and PharmacologyUniversity of CopenhagenUniversitetsparken 22100CopenhagenDenmark
| | | | - Peter Kolb
- Department of Pharmaceutical ChemistryPhilipps-University MarburgMarbacher Weg 635037MarburgGermany
| |
Collapse
|
27
|
Poli G, Granchi C, Rizzolio F, Tuccinardi T. Application of MM-PBSA Methods in Virtual Screening. Molecules 2020; 25:molecules25081971. [PMID: 32340232 PMCID: PMC7221544 DOI: 10.3390/molecules25081971] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 04/17/2020] [Accepted: 04/21/2020] [Indexed: 12/17/2022] Open
Abstract
Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.
Collapse
Affiliation(s)
- Giulio Poli
- Department of Pharmacy, University of Pisa, 56126 Pisa, Italy; (G.P.); (C.G.)
| | - Carlotta Granchi
- Department of Pharmacy, University of Pisa, 56126 Pisa, Italy; (G.P.); (C.G.)
| | - Flavio Rizzolio
- Department of Molecular science and Nanosystems, University Ca’ Foscari of Venice, 30170 Venice, Italy;
- Pathology unit, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy
| | - Tiziano Tuccinardi
- Department of Pharmacy, University of Pisa, 56126 Pisa, Italy; (G.P.); (C.G.)
- Correspondence: ; Tel.: +39-0502219595
| |
Collapse
|
28
|
Duan G, Ji C, Zhang JZH. Developing an effective polarizable bond method for small molecules with application to optimized molecular docking. RSC Adv 2020; 10:15530-15540. [PMID: 35495446 PMCID: PMC9052371 DOI: 10.1039/d0ra01483d] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 03/31/2020] [Indexed: 12/20/2022] Open
Abstract
Electrostatic interaction plays an essential role in protein-ligand binding. Due to the polarization effect, electrostatic interactions are largely impacted by their local environments. However, traditional force fields use fixed point charge-charge interactions to describe electrostatic interactions but is unable to include the polarization effect. The lack of the polarization effect in the force field representation can result in substantial error in biomolecular studies, such as molecular dynamics and molecular docking. Docking programs usually employ traditional force fields to estimate the binding energy between a ligand and a protein for pose selection or scoring. The intermolecular interaction energy mainly consists of van der Waals and electrostatic interaction in the force field representation. In the current study, we developed an Effective Polarizable Bond (EPB) method for small organic molecules and applied this EPB method to optimize protein-ligand docking in computational tests for a variety of protein-ligand systems. We tested the method on a set of 38 cocrystallized structures taken from the Protein Data Bank (PDB) and found that the maximum error was reduced from 7.98 Å to 2.03 Å when using EPB Dock, providing strong evidence that the use of EPB charges is important. We found that our optimized docking approach with EPB charges could improve the docking performance, sometimes dramatically, and the maximum error was reduced from 12.88 Å to 1.57 Å in Optimized Docking (in the case of 1fqx). The average RMSD decreased from 2.83 Å to 1.85 Å. Further investigations showed that the use of the EBP method could enhance intermolecular hydrogen bonding, which is a major contributing factor to improved docking performance. Developed tools for the calculation of the polarized ligand charge from a protein-ligand complex structure with the EPB method are freely available on GitHub (https://github.com/Xundrug/EPB).
Collapse
Affiliation(s)
- Guanfu Duan
- Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University Shanghai 200062 China
| | - Changge Ji
- Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University Shanghai 200062 China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai Shanghai 200062 China
| | - John Z H Zhang
- Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University Shanghai 200062 China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai Shanghai 200062 China
- Department of Chemistry, New York University NY NY 10003 USA
- Collaborative Innovation Center of Extreme Optics, Shanxi University Taiyuan Shanxi 030006 China
| |
Collapse
|
29
|
Lee YN, Reyes-Alcaraz A, Yun S, Lee CS, Hwang JI, Seong JY. Exploring the molecular structures that confer ligand selectivity for galanin type II and III receptors. PLoS One 2020; 15:e0230872. [PMID: 32231393 PMCID: PMC7108740 DOI: 10.1371/journal.pone.0230872] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/10/2020] [Indexed: 01/09/2023] Open
Abstract
Galanin receptors (GALRs) belong to the superfamily of G-protein coupled receptors. The three GALR subtypes (GALR1, GALR2, and GALR3) are activated by their endogenous ligands: spexin (SPX) and galanin (GAL). The synthetic SPX-based GALR2-specific agonist, SG2A, plays a dual role in the regulation of appetite and depression-like behaviors. Little is known, however, about the molecular interaction between GALR2 and SG2A. Using site-directed mutagenesis and domain swapping between GALR2 and GALR3, we identified residues in GALR2 that promote interaction with SG2A and residues in GALR3 that inhibit interaction with SG2A. In particular, Phe103, Phe106, and His110 in the transmembrane helix 3 (TM3) domain; Val193, Phe194, and Ser195 in the TM5 domain; and Leu273 in the extracellular loop 3 (ECL3) domain of GALR2 provide favorable interactions with the Asn5, Ala7, Phe11, and Pro13 residues of SG2A. Our results explain how SG2A achieves selective interaction with GALR2 and inhibits interaction with GALR3. The results described here can be used broadly for in silico virtual screening of small molecules for the development of GALR subtype-specific agonists and/or antagonists.
Collapse
MESH Headings
- Amino Acid Sequence
- Animals
- HEK293 Cells
- Humans
- Ligands
- Mice
- Mutation
- Protein Domains
- Receptor, Galanin, Type 2/chemistry
- Receptor, Galanin, Type 2/metabolism
- Receptor, Galanin, Type 3/chemistry
- Receptor, Galanin, Type 3/genetics
- Receptor, Galanin, Type 3/metabolism
- Substrate Specificity
Collapse
Affiliation(s)
- Yoo-Na Lee
- The GPCR laboratory, Graduate School of Biomedical Science, Korea University College of Medicine, Seoul, Republic of Korea
| | - Arfaxad Reyes-Alcaraz
- The GPCR laboratory, Graduate School of Biomedical Science, Korea University College of Medicine, Seoul, Republic of Korea
- College of Pharmacy, University of Houston, Houston, Texas, United States of America
| | - Seongsik Yun
- The GPCR laboratory, Graduate School of Biomedical Science, Korea University College of Medicine, Seoul, Republic of Korea
| | - Cheol Soon Lee
- Graduate School of Biomedical Science, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jong-Ik Hwang
- The GPCR laboratory, Graduate School of Biomedical Science, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jae Young Seong
- The GPCR laboratory, Graduate School of Biomedical Science, Korea University College of Medicine, Seoul, Republic of Korea
- * E-mail:
| |
Collapse
|
30
|
Congreve M, de Graaf C, Swain NA, Tate CG. Impact of GPCR Structures on Drug Discovery. Cell 2020; 181:81-91. [DOI: 10.1016/j.cell.2020.03.003] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 03/03/2020] [Accepted: 03/03/2020] [Indexed: 12/21/2022]
|
31
|
Dos Santos Maia M, Soares Rodrigues GC, Silva Cavalcanti AB, Scotti L, Scotti MT. Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry. Mini Rev Med Chem 2020; 20:1322-1340. [PMID: 32013847 DOI: 10.2174/1389557520666200204121129] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/31/2019] [Accepted: 11/04/2019] [Indexed: 02/08/2023]
Abstract
The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.
Collapse
Affiliation(s)
- Mayara Dos Santos Maia
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Gabriela Cristina Soares Rodrigues
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Andreza Barbosa Silva Cavalcanti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Luciana Scotti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Marcus Tullius Scotti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| |
Collapse
|
32
|
Limongelli V. Ligand binding free energy and kinetics calculation in 2020. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1455] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Vittorio Limongelli
- Faculty of Biomedical Sciences, Institute of Computational Science – Center for Computational Medicine in Cardiology Università della Svizzera italiana (USI) Lugano Switzerland
- Department of Pharmacy University of Naples “Federico II” Naples Italy
| |
Collapse
|
33
|
Zhou Q, Yang D, Wu M, Guo Y, Guo W, Zhong L, Cai X, Dai A, Jang W, Shakhnovich EI, Liu ZJ, Stevens RC, Lambert NA, Babu MM, Wang MW, Zhao S. Common activation mechanism of class A GPCRs. eLife 2019; 8:e50279. [PMID: 31855179 PMCID: PMC6954041 DOI: 10.7554/elife.50279] [Citation(s) in RCA: 305] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 12/19/2019] [Indexed: 12/26/2022] Open
Abstract
Class A G-protein-coupled receptors (GPCRs) influence virtually every aspect of human physiology. Understanding receptor activation mechanism is critical for discovering novel therapeutics since about one-third of all marketed drugs target members of this family. GPCR activation is an allosteric process that couples agonist binding to G-protein recruitment, with the hallmark outward movement of transmembrane helix 6 (TM6). However, what leads to TM6 movement and the key residue level changes of this movement remain less well understood. Here, we report a framework to quantify conformational changes. By analyzing the conformational changes in 234 structures from 45 class A GPCRs, we discovered a common GPCR activation pathway comprising of 34 residue pairs and 35 residues. The pathway unifies previous findings into a common activation mechanism and strings together the scattered key motifs such as CWxP, DRY, Na+ pocket, NPxxY and PIF, thereby directly linking the bottom of ligand-binding pocket with G-protein coupling region. Site-directed mutagenesis experiments support this proposition and reveal that rational mutations of residues in this pathway can be used to obtain receptors that are constitutively active or inactive. The common activation pathway provides the mechanistic interpretation of constitutively activating, inactivating and disease mutations. As a module responsible for activation, the common pathway allows for decoupling of the evolution of the ligand binding site and G-protein-binding region. Such an architecture might have facilitated GPCRs to emerge as a highly successful family of proteins for signal transduction in nature.
Collapse
Affiliation(s)
- Qingtong Zhou
- iHuman InstituteShanghaiTech UniversityShanghaiChina
| | - Dehua Yang
- The CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
- The National Center for Drug ScreeningShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
| | - Meng Wu
- iHuman InstituteShanghaiTech UniversityShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
| | - Yu Guo
- iHuman InstituteShanghaiTech UniversityShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
| | - Wanjing Guo
- The CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
- The National Center for Drug ScreeningShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
| | - Li Zhong
- The CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
- The National Center for Drug ScreeningShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
| | - Xiaoqing Cai
- The CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
- The National Center for Drug ScreeningShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
| | - Antao Dai
- The CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
- The National Center for Drug ScreeningShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
| | - Wonjo Jang
- Department of Pharmacology and Toxicology, Medical College of GeorgiaAugusta UniversityAugustaUnited States
| | - Eugene I Shakhnovich
- Department of Chemistry and Chemical BiologyHarvard UniversityCambridgeUnited States
| | - Zhi-Jie Liu
- iHuman InstituteShanghaiTech UniversityShanghaiChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
| | - Raymond C Stevens
- iHuman InstituteShanghaiTech UniversityShanghaiChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
| | - Nevin A Lambert
- Department of Pharmacology and Toxicology, Medical College of GeorgiaAugusta UniversityAugustaUnited States
| | - M Madan Babu
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Ming-Wei Wang
- The CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
- The National Center for Drug ScreeningShanghai Institute of Materia Medica, Chinese Academy of SciencesShanghaiChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
- School of PharmacyFudan UniversityShanghaiChina
| | - Suwen Zhao
- iHuman InstituteShanghaiTech UniversityShanghaiChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
| |
Collapse
|
34
|
Li Y, Sun Y, Song Y, Dai D, Zhao Z, Zhang Q, Zhong W, Hu LA, Ma Y, Li X, Wang R. Fragment-Based Computational Method for Designing GPCR Ligands. J Chem Inf Model 2019; 60:4339-4349. [PMID: 31652060 DOI: 10.1021/acs.jcim.9b00699] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, which is arguably the most important family of drug target. With the technology breakthroughs in X-ray crystallography and cryo-electron microscopy, more than 300 GPCR-ligand complex structures have been publicly reported since 2007, covering about 60 unique GPCRs. Such abundant structural information certainly will facilitate the structure-based drug design by targeting GPCRs. In this study, we have developed a fragment-based computational method for designing novel GPCR ligands. We first extracted the characteristic interaction patterns (CIPs) on the binding interfaces between GPCRs and their ligands. The CIPs were used as queries to search the chemical fragments derived from GPCR ligands, which were required to form similar interaction patterns with GPCR. Then, the selected chemical fragments were assembled into complete molecules by using the AutoT&T2 software. In this work, we chose β-adrenergic receptor (β-AR) and muscarinic acetylcholine receptor (mAChR) as the targets to validate this method. Based on the designs suggested by our method, samples of 63 compounds were purchased and tested in a cell-based functional assay. A total of 15 and 22 compounds were identified as active antagonists for β2-AR and mAChR M1, respectively. Molecular dynamics simulations and binding free energy analysis were performed to explore the key interactions (e.g., hydrogen bonds and π-π interactions) between those active compounds and their target GPCRs. In summary, our work presents a useful approach to the de novo design of GPCR ligands based on the relevant 3D structural information.
Collapse
Affiliation(s)
- Yan Li
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People's Republic of China.,Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yaping Sun
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Yunpeng Song
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Dongcheng Dai
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Zhixiong Zhao
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People's Republic of China
| | - Qing Zhang
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Wenge Zhong
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Liaoyuan A Hu
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Yingli Ma
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Xun Li
- Amgen Asia R&D Center, Amgen Biopharmaceutical R&D (Shanghai) Co., Ltd., Shanghai 201210, People's Republic of China
| | - Renxiao Wang
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People's Republic of China.,Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.,Shanxi Key Laboratory of Innovative Drugs for the Treatment of Serious Diseases Basing on Chronic Inflammation, College of Traditional Chinese Medicine, Shanxi University of Chinese Medicine, Taiyuan, Shanxi 030619, People's Republic of China
| |
Collapse
|
35
|
da Silva Rocha SF, Olanda CG, Fokoue HH, Sant'Anna CM. Virtual Screening Techniques in Drug Discovery: Review and Recent Applications. Curr Top Med Chem 2019; 19:1751-1767. [DOI: 10.2174/1568026619666190816101948] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 06/21/2019] [Accepted: 07/29/2019] [Indexed: 11/22/2022]
Abstract
The discovery of bioactive molecules is an expensive and time-consuming process and new
strategies are continuously searched for in order to optimize this process. Virtual Screening (VS) is one
of the recent strategies that has been explored for the identification of candidate bioactive molecules.
The number of new techniques and software that can be applied in this strategy has grown considerably
in recent years, so, before their use, it is necessary to understand the basics an also the limitations behind
each one to get the most out of them. It is also necessary to assess the real contributions of this strategy
so that more significant progress can be made in the future. In this context, this review aims to discuss
some important points related to VS, including the use of virtual ligand and biotarget libraries, structurebased
and ligand-based VS techniques, as well as to present recent cases where this strategy was successfully
applied.
Collapse
Affiliation(s)
- Sheisi F.L. da Silva Rocha
- Programa de Pos-Graduacao em Quimica, Instituto de Quimica, Universidade Federal Rural do Rio de Janeiro, Seropedica, Brazil
| | - Carolina G. Olanda
- Programa de Pos-Graduacao em Quimica, Instituto de Quimica, Universidade Federal Rural do Rio de Janeiro, Seropedica, Brazil
| | - Harold H. Fokoue
- Laboratorio de Avaliacao e Síntese de Substancias Bioativas (LASSBio), Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Carlos M.R. Sant'Anna
- Programa de Pos-Graduacao em Quimica, Instituto de Quimica, Universidade Federal Rural do Rio de Janeiro, Seropedica, Brazil
| |
Collapse
|
36
|
Russo Spena C, De Stefano L, Poli G, Granchi C, El Boustani M, Ecca F, Grassi G, Grassi M, Canzonieri V, Giordano A, Tuccinardi T, Caligiuri I, Rizzolio F. Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects. J Cell Physiol 2019; 234:15708-15716. [PMID: 30697729 DOI: 10.1002/jcp.28224] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 01/10/2019] [Indexed: 02/06/2023]
Abstract
Peptidyl-prolyl cis-trans isomerase, NIMA-interacting 1 (PIN1) is a peptidyl-prolyl isomerase that binds phospho-Ser/Thr-Pro motifs in proteins and catalyzes the cis-trans isomerization of proline peptide bonds. PIN1 is overexpressed in several cancers including high-grade serous ovarian cancer. Since few therapies are effective against this cancer, PIN1 could be a therapeutic target but effective PIN1 inhibitors are lacking. To identify molecules with in vivo inhibitory effects on PIN1, we used consensus docking to model existing PIN1-ligand X-ray structures and to screen a chemical database for candidate inhibitors. Ten molecules were selected and tested in cellular assays, leading to the identification of VS10 that bound and inhibited PIN1. VS10 treatment reduced the viability of ovarian cancer cell lines by inducing proteasomal PIN1 degradation, without effects on PIN1 transcription, and also reduced the levels of downstream targets β-catenin, cyclin D1, and pSer473-Akt. VS10 is a selective PIN1 inhibitor that may offer new opportunities for treating PIN1-overexpressing tumors.
Collapse
Affiliation(s)
- Concetta Russo Spena
- Pathology Unit, IRCCS CRO Aviano-National Cancer Institute, Aviano, Italy.,Doctoral School in Chemistry, University of Trieste, Trieste, Italy
| | - Lucia De Stefano
- Pathology Unit, IRCCS CRO Aviano-National Cancer Institute, Aviano, Italy.,Doctoral School in Chemistry, University of Trieste, Trieste, Italy
| | - Giulio Poli
- Department of Pharmacy, University of Pisa, Pisa, Italy
| | | | - Maguie El Boustani
- Pathology Unit, IRCCS CRO Aviano-National Cancer Institute, Aviano, Italy.,Doctoral School in Molecular Biomedicine, University of Trieste, Trieste, Italy
| | - Fabrizio Ecca
- Experimental and Clinical Pharmacology Unit, IRCCS CRO Aviano-National Cancer Institute, Aviano, Italy
| | - Gabriele Grassi
- Department of Life Sciences, Cattinara University Hospital, University of Trieste, Trieste, Italy
| | - Mario Grassi
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Vincenzo Canzonieri
- Pathology Unit, IRCCS CRO Aviano-National Cancer Institute, Aviano, Italy.,Center for Biotechnology, Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, Pennsylvania.,Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Antonio Giordano
- Center for Biotechnology, Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, Pennsylvania
| | - Tiziano Tuccinardi
- Department of Pharmacy, University of Pisa, Pisa, Italy.,Center for Biotechnology, Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, Pennsylvania
| | - Isabella Caligiuri
- Pathology Unit, IRCCS CRO Aviano-National Cancer Institute, Aviano, Italy
| | - Flavio Rizzolio
- Pathology Unit, IRCCS CRO Aviano-National Cancer Institute, Aviano, Italy.,Center for Biotechnology, Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, Pennsylvania.,Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Venezia-Mestre, Italy
| |
Collapse
|
37
|
de Heuvel E, Singh AK, Boronat P, Kooistra AJ, van der Meer T, Sadek P, Blaazer AR, Shaner NC, Bindels DS, Caljon G, Maes L, Sterk GJ, Siderius M, Oberholzer M, de Esch IJ, Brown DG, Leurs R. Alkynamide phthalazinones as a new class of TbrPDEB1 inhibitors (Part 2). Bioorg Med Chem 2019; 27:4013-4029. [DOI: 10.1016/j.bmc.2019.06.026] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Revised: 06/12/2019] [Accepted: 06/14/2019] [Indexed: 01/27/2023]
|
38
|
Wang D, Cui C, Ding X, Xiong Z, Zheng M, Luo X, Jiang H, Chen K. Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods. Front Pharmacol 2019; 10:924. [PMID: 31507420 PMCID: PMC6713720 DOI: 10.3389/fphar.2019.00924] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 07/22/2019] [Indexed: 01/29/2023] Open
Abstract
Scoring functions play an important role in structure-based virtual screening. It has been widely accepted that target-specific scoring functions (TSSFs) may achieve better performance compared with universal scoring functions in actual drug research and development processes. A method that can effectively construct TSSFs will be of great value to drug design and discovery. In this work, we proposed a deep learning–based model named DeepScore to achieve this goal. DeepScore adopted the form of PMF scoring function to calculate protein–ligand binding affinity. However, different from PMF scoring function, in DeepScore, the score for each protein–ligand atom pair was calculated using a feedforward neural network. Our model significantly outperformed Glide Gscore on validation data set DUD-E. The average ROC-AUC on 102 targets was 0.98. We also combined Gscore and DeepScore together using a consensus method and put forward a consensus model named DeepScoreCS. The comparison results showed that DeepScore outperformed other machine learning–based TSSFs building methods. Furthermore, we presented a strategy to visualize the prediction of DeepScore. All of these results clearly demonstrated that DeepScore would be a useful model in constructing TSSFs and represented a novel way incorporating deep learning and drug design.
Collapse
Affiliation(s)
- Dingyan Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,College of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Chen Cui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,College of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyu Ding
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,College of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Zhaoping Xiong
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| |
Collapse
|
39
|
Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs). Curr Opin Struct Biol 2019; 55:147-153. [PMID: 31102980 DOI: 10.1016/j.sbi.2019.03.015] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/06/2019] [Accepted: 03/11/2019] [Indexed: 12/11/2022]
Abstract
Demand for novel GPCR modulators is increasing as the association between the GPCR signaling pathway and numerous diseases such as cancers, psychological and metabolic disorders continues to be established. In silico structure-based drug design (SBDD) offers an outlet where researchers could exploit the accumulating structural information of GPCR to expedite the process of drug discovery. The coupling of structure-based approaches such as virtual screening and molecular docking with molecular dynamics and/or Monte Carlo simulation aids in reflecting the dynamics of proteins in nature into previously static docking studies, thus enhancing the accuracy of rationally designed ligands. This review will highlight recent computational strategies that incorporate protein flexibility into SBDD of GPCR-targeted ligands.
Collapse
|
40
|
Chatterjee D, Kaur G, Muradia S, Singh B, Agrewala JN. ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity. Sci Rep 2019; 9:3092. [PMID: 30816123 PMCID: PMC6395627 DOI: 10.1038/s41598-018-36179-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 11/15/2018] [Indexed: 10/31/2022] Open
Abstract
Host directed therapies to boost immunity against infection are gaining considerable impetus following the observation that use of antibiotics has become a continuous source for the emergence of drug resistant strains of pathogens. Receptors expressed by the cells of immune system play a cardinal role in initiating sequence of events necessary to ameliorate many morbid conditions. Although, ligands for the immune receptors are available; but their use is limited due to complex structure, synthesis and cost-effectiveness. Virtual screening (VS) is an integral part of chemoinformatics and computer-aided drug design (CADD) and aims to streamline the process of drug discovery. ImmtorLig_DB is a repertoire of 5000 novel small molecules, screened from ZINC database and ranked using structure based virtual screening (SBVS) against 25 immune receptors which play a pivotal role in defending and initiating the activation of immune system. Consequently, in the current study, small molecules were screened by docking on the essential domains present on the receptors expressed by cells of immune system. The screened molecules exhibited efficacious binding to immune receptors, and indicated a possibility of discovering novel small molecules. Other features of ImmtorLig_DB include information about availability, clustering analysis, and estimation of absorption, distribution, metabolism, and excretion (ADME) properties of the screened small molecules. Structural comparisons indicate that predicted small molecules may be considered novel. Further, this repertoire is available via a searchable graphical user interface (GUI) through http://bioinfo.imtech.res.in/bvs/immtor/ .
Collapse
Affiliation(s)
| | - Gurkirat Kaur
- CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Shilpa Muradia
- CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Balvinder Singh
- CSIR-Institute of Microbial Technology, Chandigarh, 160036, India.
| | | |
Collapse
|
41
|
|
42
|
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.
Collapse
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.
| |
Collapse
|
43
|
Vass M, Podlewska S, de Esch IJP, Bojarski AJ, Leurs R, Kooistra AJ, de Graaf C. Aminergic GPCR-Ligand Interactions: A Chemical and Structural Map of Receptor Mutation Data. J Med Chem 2018; 62:3784-3839. [PMID: 30351004 DOI: 10.1021/acs.jmedchem.8b00836] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The aminergic family of G protein-coupled receptors (GPCRs) plays an important role in various diseases and represents a major drug discovery target class. Structure determination of all major aminergic subfamilies has enabled structure-based ligand design for these receptors. Site-directed mutagenesis data provides an invaluable complementary source of information for elucidating the structural determinants of binding of different ligand chemotypes. The current study provides a comparative analysis of 6692 mutation data points on 34 aminergic GPCR subtypes, covering the chemical space of 540 unique ligands from mutagenesis experiments and information from experimentally determined structures of 52 distinct aminergic receptor-ligand complexes. The integrated analysis enables detailed investigation of structural receptor-ligand interactions and assessment of the transferability of combined binding mode and mutation data across ligand chemotypes and receptor subtypes. An overview is provided of the possibilities and limitations of using mutation data to guide the design of novel aminergic receptor ligands.
Collapse
Affiliation(s)
- Márton Vass
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS) , VU University Amsterdam , 1081HZ Amsterdam , The Netherlands
| | - Sabina Podlewska
- Department of Medicinal Chemistry, Institute of Pharmacology , Polish Academy of Sciences , Smętna 12 , PL31-343 Kraków , Poland
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS) , VU University Amsterdam , 1081HZ Amsterdam , The Netherlands
| | - Andrzej J Bojarski
- Department of Medicinal Chemistry, Institute of Pharmacology , Polish Academy of Sciences , Smętna 12 , PL31-343 Kraków , Poland
| | - Rob Leurs
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS) , VU University Amsterdam , 1081HZ Amsterdam , The Netherlands
| | - Albert J Kooistra
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS) , VU University Amsterdam , 1081HZ Amsterdam , The Netherlands.,Department of Drug Design and Pharmacology , University of Copenhagen , Universitetsparken 2 , 2100 Copenhagen , Denmark
| | - Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS) , VU University Amsterdam , 1081HZ Amsterdam , The Netherlands.,Sosei Heptares , Steinmetz Building, Granta Park, Great Abington , Cambridge CB21 6DG , U.K
| |
Collapse
|
44
|
Miszta P, Jakowiecki J, Rutkowska E, Turant M, Latek D, Filipek S. Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists. Methods Mol Biol 2018; 1705:265-296. [PMID: 29188567 DOI: 10.1007/978-1-4939-7465-8_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Predicting the functional preferences of the ligands was always a highly demanding task, much harder that predicting whether a ligand can bind to the receptor. This is because of significant similarities of agonists, antagonists and inverse agonists which are binding usually in the same binding site of the receptor and only small structural changes can push receptor toward a particular activation state. For G protein-coupled receptors, due to a large progress in crystallization techniques and also in receptor thermal stabilization, it was possible to obtain a large number of high-quality structures of complexes of these receptors with agonists and non-agonists. Additionally, the long-time-scale molecular dynamics simulations revealed how the activation processes of GPCRs can take place. Using both theoretical and experimental knowledge it was possible to employ many clever and sophisticated methods which can help to differentiate agonists and non-agonists, so one can interconvert them in search of the optimal drug.
Collapse
Affiliation(s)
- Przemysław Miszta
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Jakub Jakowiecki
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Ewelina Rutkowska
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Maria Turant
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Dorota Latek
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Sławomir Filipek
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland.
| |
Collapse
|
45
|
Breakthrough in GPCR Crystallography and Its Impact on Computer-Aided Drug Design. Methods Mol Biol 2018; 1705:45-72. [PMID: 29188558 DOI: 10.1007/978-1-4939-7465-8_3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Recent crystallographic structures of G protein-coupled receptors (GPCRs) have greatly advanced our understanding of the recognition of their diverse agonist and antagonist ligands. We illustrate here how this applies to A2A adenosine receptors (ARs) and to P2Y1 and P2Y12 receptors (P2YRs) for ADP. These X-ray structures have impacted the medicinal chemistry aimed at discovering new ligands for these two receptor families, including receptors that have not yet been crystallized but are closely related to the known structures. In this Chapter, we discuss recent structure-based drug design projects that led to the discovery of: (a) novel A3AR agonists based on a highly rigidified (N)-methanocarba scaffold for the treatment of chronic neuropathic pain and other conditions, (b) fluorescent probes of the ARs and P2Y14R, as chemical tools for structural probing of these GPCRs and for improving assay capabilities, and (c) new more drug-like antagonists of the inflammation-related P2Y14R. We also describe the computationally enabled molecular recognition of positive (for A3AR) and negative (P2Y1R) allosteric modulators that in some cases are shown to be consistent with structure-activity relationship (SAR) data. Thus, computational modeling has become an essential tool for the design of purine receptor ligands.
Collapse
|
46
|
Structural insights into serotonin receptor ligands polypharmacology. Eur J Med Chem 2018; 151:797-814. [DOI: 10.1016/j.ejmech.2018.04.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Revised: 04/02/2018] [Accepted: 04/03/2018] [Indexed: 02/03/2023]
|
47
|
In-silico guided discovery of novel CCR9 antagonists. J Comput Aided Mol Des 2018; 32:573-582. [DOI: 10.1007/s10822-018-0113-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 03/19/2018] [Indexed: 12/15/2022]
|
48
|
Kooistra AJ, Vass M, McGuire R, Leurs R, de Esch IJP, Vriend G, Verhoeven S, de Graaf C. 3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery. ChemMedChem 2018; 13:614-626. [PMID: 29337438 PMCID: PMC5900740 DOI: 10.1002/cmdc.201700754] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 01/11/2018] [Indexed: 01/06/2023]
Abstract
eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.
Collapse
Affiliation(s)
- Albert J. Kooistra
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Márton Vass
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Ross McGuire
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
- BioAxis Research, Pivot ParkOssThe Netherlands
| | - Rob Leurs
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Iwan J. P. de Esch
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
| | | | - Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| |
Collapse
|
49
|
Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S, Choi S. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Front Pharmacol 2018; 9:128. [PMID: 29593527 PMCID: PMC5854945 DOI: 10.3389/fphar.2018.00128] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 02/06/2018] [Indexed: 01/14/2023] Open
Abstract
The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
Collapse
Affiliation(s)
| | | | | | | | | | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| |
Collapse
|
50
|
Wang Z, Kang Y, Li D, Sun H, Dong X, Yao X, Xu L, Chang S, Li Y, Hou T. Benchmark Study Based on 2P2I DB to Gain Insights into the Discovery of Small-Molecule PPI Inhibitors. J Phys Chem B 2018; 122:2544-2555. [PMID: 29420886 DOI: 10.1021/acs.jpcb.7b12658] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein-protein interactions (PPIs) have been regarded as novel and highly promising drug targets in drug discovery. Numerous new experimental techniques and computational approaches have been developed to assist the design of PPI modulators during the past two decades. However, identification and optimization of small-molecule inhibitors targeting PPIs is still a particularly challenging task due to the "undruggable" profiles of PPI interfaces. Nowadays, in silico screening, especially docking-based virtual screening, has emerged as an effective method to complement experimental high-throughput screening in identifying novel and potent small-molecule PPI inhibitors. Here, on the basis of the 2P2IDB database, we explored the structural features of the known small-molecule PPI inhibitors and analyzed the characteristics of the PPI binding pockets. More importantly, we evaluated the sampling power and screening power of six popular docking programs for PPI targets. Our results indicate that the chlorinated conjugate group and amidelike linkage are two types of privileged fragments of PPI inhibitors; the average druggability of the binding sites of the PPI targets in 2P2IDB is slightly worse than that of traditional ones; both academic and commercial docking programs exhibit an acceptable accuracy on pose prediction for PPI inhibitors, but their screening powers for identifying PPI inhibitors are still not satisfactory. It is expected that our work can provide valuable guidance on the construction of PPI-focused library, the determination of druggable PPI binding pocket, and the selection of docking program for the screening of small-molecule PPI inhibitors.
Collapse
Affiliation(s)
- Zhe Wang
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Yu Kang
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Dan Li
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Huiyong Sun
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Xiaowu Dong
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health , Macau University of Science and Technology , Avenida Wai Long , Taipa , Macau (SAR) , China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering , Jiangsu University of Technology , Changzhou 213001 , China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering , Jiangsu University of Technology , Changzhou 213001 , China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM) , Soochow University , Suzhou , Jiangsu 215123 , China
| | - Tingjun Hou
- College of Pharmaceutical Sciences , Zhejiang University , Hangzhou , Zhejiang 310058 , China
| |
Collapse
|