Identification of Novel TRPC5 Inhibitors by Pharmacophore-Based and Structure-Based Approaches

Canonical transient receptor potential-5 (TRPC5), which belongs to the subfamily of transient receptor potential (TRP) channels, is a non-selective cation channel mainly expressed in the central nervous system and shows more restricted expression in the periphery. TRPC5 plays a crucial role in human physiology and pathology, for instance, anxiety, depression, epilepsy, pain, memory and chronic kidney disease (CKD). However, due to lack of the effective and selective inhibitors, its physiological and pathological mechanism remains so far unknown. It is therefore pivotal to identify potential TRPC5 inhibitors. We have applied ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) methods. The pharmacophore models of TRPC5 antagonists generated by using the HypoGen and HipHop algorithms were used as a query model for the screening of potential inhibitors against the Specs database. The resultant hits from LBVS were further screened by SBVS. SBVS was carried out based on the homology model generation of human TRPC5, binding site identification, molecular dynamics optimization and molecular docking studies. In our systematic screening approaches, we have identified 7 hits compounds with comparable dock score after Lipinski and Veber rules, ADMET, PAINS analysis, cluster analysis, and similarity analysis. In conclusion, the current research provides novel backbones for the new-generation of TRPC5 inhibitors.

[1]  Michael M. Mysinger,et al.  Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking , 2012, Journal of medicinal chemistry.

[2]  D. Eisenberg,et al.  VERIFY3D: assessment of protein models with three-dimensional profiles. , 1997, Methods in enzymology.

[3]  Antonio Riccio,et al.  mRNA distribution analysis of human TRPC family in CNS and peripheral tissues. , 2002, Brain research. Molecular brain research.

[4]  Li Zhang,et al.  Pharmacophore modeling, molecular docking and molecular dynamics simulations toward identifying lead compounds for Chk1 , 2018, Comput. Biol. Chem..

[5]  Andrew Smellie,et al.  Identification of Common Functional Configurations Among Molecules , 1996, J. Chem. Inf. Comput. Sci..

[6]  David E. Clapham,et al.  TRP channels as cellular sensors , 2003, Nature.

[7]  F. Hobbs,et al.  Global Prevalence of Chronic Kidney Disease – A Systematic Review and Meta-Analysis , 2016, PloS one.

[8]  Thomas Stützle,et al.  Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS , 2009, J. Chem. Inf. Model..

[9]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[10]  Antti Pertovaara,et al.  Regulation of neuropathic pain behavior by amygdaloid TRPC4/C5 channels , 2015, Neuroscience Letters.

[11]  Marc Freichel,et al.  Canonical Transient Receptor Channel 5 (TRPC5) and TRPC1/4 Contribute to Seizure and Excitotoxicity by Distinct Cellular Mechanisms , 2013, Molecular Pharmacology.

[12]  Jing Li,et al.  Natural and synthetic flavonoid modulation of TRPC5 channels , 2016, British journal of pharmacology.

[13]  Mona Singh,et al.  Predicting functionally important residues from sequence conservation , 2007, Bioinform..

[14]  Alexander Tropsha,et al.  Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods. , 2004, Journal of molecular graphics & modelling.

[15]  Craig Montell,et al.  A unified nomenclature for the superfamily of TRP cation channels. , 2002, Molecular cell.

[16]  M. Peyton,et al.  Mouse trp2, the homologue of the human trpc2 pseudogene, encodes mTrp2, a store depletion-activated capacitative Ca2+ entry channel. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Sarvesh Paliwal,et al.  Ligand-based drug design studies using predictive pharmacophore model generation on 4H-1,2,4-triazoles as AT1 receptor antagonists , 2011, Medicinal Chemistry Research.

[18]  Daniel Kuhn,et al.  DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment , 2012, Bioinform..

[19]  Wenjun Guo,et al.  Structure of the receptor-activated human TRPC6 and TRPC3 ion channels , 2018, Cell Research.

[20]  Charles E. McCulloch,et al.  CHRONIC KIDNEY DISEASE AND THE RISKS OF DEATH, CARDIOVASCULAR EVENTS, AND HOSPITALIZATION , 2004 .

[21]  Y. Kurogi,et al.  Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. , 2001, Current medicinal chemistry.

[22]  D. Eisenberg,et al.  Assessment of protein models with three-dimensional profiles , 1992, Nature.

[23]  Craig Montell,et al.  The TRP Superfamily of Cation Channels , 2005, Science's STKE.

[24]  Manfred J. Sippl,et al.  Thirty years of environmental health research--and growing. , 1996, Nucleic Acids Res..

[25]  J. Baell,et al.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. , 2010, Journal of medicinal chemistry.

[26]  C. Hopkins,et al.  Design, synthesis and characterization of novel N-heterocyclic-1-benzyl-1H-benzo[d]imidazole-2-amines as selective TRPC5 inhibitors leading to the identification of the selective compound, AC1903. , 2019, Bioorganic & medicinal chemistry letters.

[27]  D. Clapham,et al.  An introduction to TRP channels. , 2006, Annual review of physiology.

[28]  V. Khedkar,et al.  Pharmacophore model prediction, 3D-QSAR and molecular docking studies on vinyl sulfones targeting Nrf2-mediated gene transcription intended for anti-Parkinson drug design , 2016, Journal of biomolecular structure & dynamics.

[29]  Hussein N. Rubaiy,et al.  Remarkable Progress with Small-Molecule Modulation of TRPC1/4/5 Channels: Implications for Understanding the Channels in Health and Disease , 2018, Cells.

[30]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[31]  Rodrigo Lopez,et al.  Programmatic access to bioinformatics tools from EMBL-EBI update: 2017 , 2017, Nucleic Acids Res..

[32]  Hussein N. Rubaiy,et al.  Pico145 - powerful new tool for TRPC1/4/5 channels , 2017, Channels.

[33]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[34]  Thierry Langer,et al.  Pharmacophore Modeling and in Silico Screening for New P450 19 (Aromatase) Inhibitors , 2006, J. Chem. Inf. Model..

[35]  David J Beech,et al.  In pursuit of small molecule chemistry for calcium‐permeable non‐selective TRPC channels – mirage or pot of gold? , 2013, British journal of pharmacology.

[36]  Georg Rast,et al.  Treatment with HC-070, a potent inhibitor of TRPC4 and TRPC5, leads to anxiolytic and antidepressant effects in mice , 2018, PloS one.

[37]  Marvin Johnson,et al.  Concepts and applications of molecular similarity , 1990 .

[38]  Madhu Chopra,et al.  Molecular modeling study on chemically diverse series of cyclooxygenase-2 selective inhibitors: generation of predictive pharmacophore model using Catalyst , 2008, Journal of molecular modeling.

[39]  Craig W Lindsley,et al.  Inhibition of the TRPC5 ion channel protects the kidney filter. , 2013, The Journal of clinical investigation.

[40]  Andreas Draguhn,et al.  Heteromeric channels formed by TRPC1, TRPC4 and TRPC5 define hippocampal synaptic transmission and working memory , 2017, The EMBO journal.

[41]  Herbert Waldmann,et al.  Picomolar, selective, and subtype-specific small-molecule inhibition of TRPC1/4/5 channels , 2017, The Journal of Biological Chemistry.

[42]  Elisabeth Guichard,et al.  Use of catalyst in a 3D-QSAR study of the interactions between flavor compounds and beta-lactoglobulin. , 2003, Journal of agricultural and food chemistry.

[43]  Mohd Athar,et al.  Identification of Mycobacterium tuberculosis enoyl-acyl carrier protein reductase inhibitors: A combined in-silico and in-vitro analysis. , 2017, Journal of molecular graphics & modelling.

[44]  Johann Gasteiger,et al.  Prediction of proton magnetic resonance shifts: The dependence on hydrogen charges obtained by iterative partial equalization of orbital electronegativity , 1981 .

[45]  Yossef Kliger,et al.  Improving Classical Substructure-Based Virtual Screening to Handle Extrapolation Challenges , 2012, J. Chem. Inf. Model..

[46]  Maolin Yu,et al.  Discovery of a Potent and Selective TRPC5 Inhibitor, Efficacious in a Focal Segmental Glomerulosclerosis Model. , 2019, ACS medicinal chemistry letters.

[47]  Michael Schaefer,et al.  Clemizole Hydrochloride Is a Novel and Potent Inhibitor of Transient Receptor Potential Channel TRPC5 , 2014, Molecular Pharmacology.

[48]  Herbert Waldmann,et al.  Na+ entry through heteromeric TRPC4/C1 channels mediates (−)Englerin A-induced cytotoxicity in synovial sarcoma cells , 2017, Scientific Reports.

[49]  Gerrit Groenhof,et al.  GROMACS: Fast, flexible, and free , 2005, J. Comput. Chem..

[50]  J. Thornton,et al.  AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR , 1996, Journal of biomolecular NMR.

[51]  Meng Wu,et al.  Identification and optimization of 2‐aminobenzimidazole derivatives as novel inhibitors of TRPC4 and TRPC5 channels , 2015, British journal of pharmacology.

[52]  Michael X. Zhu,et al.  Acute Treatment with a Novel TRPC4/C5 Channel Inhibitor Produces Antidepressant and Anxiolytic-Like Effects in Mice , 2015, PloS one.

[53]  John J. Irwin,et al.  ZINC 15 – Ligand Discovery for Everyone , 2015, J. Chem. Inf. Model..