Review Article

Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection

Author(s): Surovi Saikia, Manobjyoti Bordoloi* and Rajeev Sarmah

Volume 20, Issue 5, 2019

Page: [522 - 539] Pages: 18

DOI: 10.2174/1389450120666181105152439

Price: $65

Abstract

The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including “off-target” effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.

Keywords: G-protein coupled receptors, drug target, clinical trials, receptor proteins, drug discovery and disease.

Graphical Abstract
[1]
Rask-Andersen M, Masuram S, Schiӧth HB. The druggable genome: evaluation of drug targets in clinical trials suggests major shifts in molecular class and indication. Annu Rev Pharmacol Toxicol 2014; 54: 9-26.
[2]
Santos R, Ursu O, Gaulton A, et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov 2017; 16: 19-34.
[3]
Huang XP, Karpiak J, Kroeze WK, et al. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. Nature 2015; 527: 477-83.
[4]
Sriram K, Insel PAG. Protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol Pharmacol 2018; 93: 251-8.
[5]
Wang W, Qiao Y, Li Z. New Insights into Modes of GPCR Activation. Trends Pharmacol Sci 2018; 39: 367-86.
[6]
Chou KC, Forsén S. Graphical rules for enzyme-catalyzed rate laws. Biochem J 1980; 187: 829-35.
[7]
Kezdy FJ, Reusser F. Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal Biochem 1994; 221: 217-30.
[8]
Althaus IW, Chou JJ, Gonzales AJ, et al. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J Biol Chem 1993; 268: 6119-24.
[9]
Althaus IW, Chou JJ, Gonzales AJ, et al. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem 1993; 268: 14875-80.
[10]
Althaus IW, Chou JJ, Gonzales AJ, et al. Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry 1993; 32: 6548-54.
[11]
Elrod DW. Bioinformatical analysis of G-protein-coupled receptors. J Proteome Res 2002; 1: 429-33.
[12]
Elrod DW. A study on the correlation of G-protein-coupled receptor types with amino acid composition. Protein Eng 2002; 15: 713-5.
[13]
Chou KC. Prediction of G-protein-coupled receptor classes. J Proteome Res 2005; 4: 1413-8.
[14]
Chou KC. Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. J Proteome Res 2005; 4: 1681-6.
[15]
Qiu JD, Huang JH, Liang RP, Lu XQ. Prediction of G-protein-coupled receptor classes based on the concept of Chou’s pseudo amino acid composition: an approach from discrete wavelet transform. Anal Biochem 2009; 390: 68-73.
[16]
Gu Q, Ding YS, Zhang TL. Prediction of g-protein-coupled receptor classes in low homology using chou’s pseudo amino acid composition with approximate entropy and hydrophobicity patterns. Protein Pept Lett 2010; 17: 559-67.
[17]
Xiao X, Wang P. GPCR-2L: Predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Mol Biosyst 2011; 7: 911-9.
[18]
Xiao X, Lin WZ. Recent advances in predicting G-protein coupled receptor classification. Curr Bioinform 2012; 7: 132-42.
[19]
Zia-ur-Rehman. Khan A. Identifying GPCRs and their types with chou’s pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix. Protein Pept Lett 2012; 19: 890-903.
[20]
Xie HL, Fu L, Nie XD. Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou’s PseAAC. Protein Eng Des Sel 2013; 26: 735-42.
[21]
Tiwari AK. Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into Chou’s general PseAAC. Comput Methods Programs Biomed 2016; 134: 197-213.
[22]
Congreve M, Langmead CJ, Mason JS, Marshall FH. Progress in structure based drug design for G protein-coupled receptors. J Med Chem 2011; 54: 4283-311.
[23]
Hutchings CJ, Koglin M, Olson WC, Marshall FH. Opportunities for therapeutic antibodies directed at G-protein-coupled receptors. Nat Rev Drug Discov 2017; 16: 787-810.
[24]
Sexton PM, Christopoulos A. To Bind or Not to Bind: Unravelling GPCR Polypharmacology. Cell 2018; 172: 636-8.
[25]
Hauser AS, Chavali S, Masuho I, et al. Pharmacogenomics of GPCR Drug Targets. Cell 2018; 172: 41-54.
[26]
Sultana J, Cutroneo P. Trifiro’G. Clinical and economic burden of adverse drug reactions. J Pharmacol Pharmacother 2013; 4: S73-7.
[27]
The IDG Knowledge Management Center. 2016.Unexplored opportunities in the druggable human genomeNature Reviews https://www.nature.com/nrd/ posters/ druggablegenome/index.html
[28]
Hauser AS, Attwood MM, Rask-Andersen M, Schiöth HB, Gloriam DE. Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov 2017; 16: 829-42.
[29]
Chen W, Feng PM, Lin H. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 2013; 41: e68.
[30]
Feng PM, Chen W, Lin H. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal Biochem 2013; 442: 118-25.
[31]
Chen W, Ding H, Feng P, Lin H, Chou KC. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 2016; 7: 16895-909.
[32]
Chou KC, Jones D, Heinrikson RL. Prediction of the tertiary structure and substrate binding site of caspase-8. FEBS Lett 1997; 419: 49-54.
[33]
Chou KC, Tomasselli AG, Heinrikson RL. Prediction of the Tertiary Structure of a Caspase-9/Inhibitor Complex. FEBS Lett 2000; 470: 249-56.
[34]
Chou KC. Insights from modelling three-dimensional structures of the human potassium and sodium channels. J Proteome Res 2004; 3: 856-61.
[35]
Chou KC. Insights from modelling the tertiary structure of BACE2. J Proteome Res 2004; 3: 1069-72.
[36]
Chou KC. Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor. Biochem Biophys Res Commun 2004; 319: 433-8.
[37]
Chou KC. Insights from modeling the 3D structure of DNA-CBF3b complex. J Proteome Res 2005; 4: 1657-60.
[38]
Wang SQ, Du QS, Chou KC. Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases. Biochem Biophys Res Commun 2007; 354: 634-40.
[39]
Chou KC. Review: Structural bioinformatics and its impact to biomedical science. Curr Med Chem 2004; 11: 2105-34.
[40]
Zhou GP, Huang RB. The pH-Triggered Conversion of the PrP(c) to PrP(sc.). Curr Top Med Chem 2013; 13: 1152-63.
[41]
Zhou GP. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J Theor Biol 2011; 284: 142-8.
[42]
Chen W, Feng P, Ding H, Lin H, Chou KC. iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition. Anal Biochem 2015; 490: 26-33.
[43]
Xu Y, Ding J, Wu LY, Chou KC. iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS One 2013; 8: e55844.
[44]
Chen W, Feng PM, Lin H, Chou KC. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BioMed Res Int 2014; 2014: 623149.
[45]
Xu Y, Wen X, Wen LS, Wu LY, Deng NY, Chou KC. iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One 2014; 9: e105018.
[46]
Jia J, Liu Z, Xiao X, Liu B, Chou KC. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal Biochem 2016; 497: 48-56.
[47]
Jia J, Liu Z, Xiao X, Liu B, Chou KC. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J Theor Biol 2016; 394: 223-30.
[48]
Jia J, Liu Z, Xiao X, Liu B, Chou KC. iCar-PseCp: identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget 2016; 7: 34558-70.
[49]
Qiu WR, Sun BQ, Xiao X, Xu ZC, Chou KC. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget 2016; 7: 44310-21.
[50]
Liu Z, Xiao X, Yu DJ. Jia J, Qiu WR, Chou KC. pRNAm-PC: Predicting N-methyladenosine sites in RNA sequences via physical-chemical properties. Anal Biochem 2016; 497: 60-7.
[51]
Liu Z, Xiao X, Qiu WR, Chou KC. iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem 2015; 474: 69-77.
[52]
Liu Z, Xiao X, Qiu WR, Chou KC. Benchmark data for identifying DNA methylation sites via pseudo trinucleotide composition. Data Brief 2015; 4: 87-9.
[53]
Xiao X, Wang P, Lin WZ, Jia JH, Chou KC. iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem 2013; 436: 168-77.
[54]
Wang P, Hu L, Liu G, et al. Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PLoS One 2011; 6: e18476.
[55]
Cheng X, Zhao SG, Lin WZ, Xiao X, Chou KC. pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinform 2017; 33: 3524-31.
[56]
Michino M, Beuming T, Donthamsetti P, et al. What can crystal structures of aminergic receptors tell us about designing subtype-selective ligands? Pharmacol Rev 2015; 67: 198-213.
[57]
Bock A, Mohr K. Dualsteric GPCR targeting and functional selectivity: the paradigmatic M2 muscarinic acetylcholine receptor. Drug Discov Today Technol 2013; 10: e245-52.
[58]
Langmead CJ, Watson J, Reavill C. Muscarinic acetylcholine receptors as CNS drug targets. Pharmacol Ther 2008; 117: 232-43.
[59]
Melancon BJ, Tarr JC, Panarese JD, Wood MR, Lindsley CW. Allosteric modulation of the M1 muscarinic acetylcholine receptor: improving cognition and a potential treatment for schizophrenia and Alzheimer’s disease. Drug Discov Today 2013; 18: 1185-99.
[60]
Davis AA, Fritz JJ, Wess J, Lah JJ, Levey AI. Deletion of M1 muscarinic acetylcholine receptors increases amyloid pathology in vitro and in vivo. J Neurosci 2010; 30: 4190-6.
[61]
Spindel ER. Muscarinic receptor agonists and antagonists: effects on cancer. Handb Exp Pharmacol 2012; 451-68.
[62]
Magnon C, Hall SJ, Lin J, et al. Autonomic nerve development contributes to prostate cancer progression. Sci 2013; 341(6142): 1236361.
[63]
Bodick NC, Offen WW, Levey AI, et al. Effects of xanomeline, a selective muscarinic receptor agonist, on cognitive function and behavioral symptoms in Alzheimer disease. Arch Neurol 1997; 54: 465-73.
[64]
Shekhar A, Potter WZ, Lightfoot J, et al. Selective muscarinic receptor agonist xanomeline as a novel treatment approach for schizophrenia. Am J Psychiatry 2008; 165: 1033-9.
[65]
Thomsen M, Craig W, Lindsley P, et al. Contribution of both M1 and M4 receptors to muscarinic agonist-mediated attenuation of the cocaine discriminative stimulus in mice. Psychopharmacol 2012; 220: 673-85.
[66]
Kruse AC, Kobilka BK, Gautam D, et al. Muscarinic acetylcholine receptors: novel opportunities for drug development. Nat Rev Drug Discov 2014; 13: 549-60.
[67]
Ahles A, Engelhardt S. Polymorphic variants of adrenoceptors: pharmacology, physiology, and role in disease. Pharmacol Rev 2014; 66: 598-637.
[68]
Rosskopf D, Michel MC. Pharmacogenomics of G protein-coupled receptor ligands in cardiovascular medicine. Pharmacol Rev 2008; 60: 513-35.
[69]
Leucht S, Cipriani A, Spineli L, et al. Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet 2013; 382: 951-62.
[70]
Knaus AE, Muthig V, Schickinger S, et al. Alpha2-adrenoceptor subtypes--unexpected functions for receptors and ligands derived from gene-targeted mouse models. Neurochem Int 2007; 51: 277-81.
[71]
Gilsbach R, Hein L. Are the pharmacology and physiology of a2 adrenoceptors determined by a2-heteroreceptors and autoreceptors respectively? Br J Pharmacol 2012; 165: 90-102.
[72]
Small KM, Wagoner LE, Levin AM, et al. Synergistic polymorphisms of beta1- and alpha2C-adrenergic receptors and the risk of congestive heart failure. N Engl J Med 2002; 347: 1135-42.
[73]
La Rosée K, Huntgeburth M, Rosenkranz S, Böhm M, Schnabel P. The Arg389Gly beta1-adrenoceptor gene polymorphism determines contractile response to catecholamines. Pharmacogenetics 2004; 14: 711-6.
[74]
Clément K, Vaisse C, Manning BS, et al. Genetic variation in the beta 3-adrenergic receptor and an increased capacity to gain weight in patients with morbid obesity. N Engl J Med 1995; 333: 352-4.
[75]
Butini S, Nikolic K, Kassel S, et al. Polypharmacology of dopamine receptor ligands. Prog Neurobiol 2016; 142: 68-103.
[76]
Gurevich EV, Gainetdinov RR, Gurevich VV. G protein-coupled receptor kinases as regulators of dopamine receptor functions. Pharmacol Res 2016; 111: 1-16.
[77]
Pascoli V, Cahill E, Bellivier F, Caboche J, Vanhoutte P. Extracellular signal- regulated protein kinases 1 and 2 activation by addictive drugs: A signal toward pathological adaptation. Biol Psychiatry 2016; 76: 917-26.
[78]
Boyd KN, Mailman RB. Dopamine receptor signaling and current and future antipsychotic drugs. Handb Exp Pharmacol 2012; 53-86.
[79]
Haas HL, Sergeeva OA, Selbach O. Histamine in the nervous system. Physiol Rev 2008; 88: 1183-241.
[80]
Passani MB, Lin JS, Hancock A, Crochet S, Blandina P. The histamine H3 receptor as a novel therapeutic target for cognitive and sleep disorders. Trends Pharmacol Sci 2004; 25: 618-25.
[81]
Stahl SM. Selective histamine H1 antagonism: Novel hypnotic and pharmacologic actions challenge classical notions of antihistamines. CNS Spectr 2008; 13: 1027-38.
[82]
Frandsen IO, Boesgaard MW, Fidom K, et al. Identification of histamine h3 receptor ligands using a new crystal structure fragment-based method. Sci Rep 2017; 7: 4829.
[83]
Medhurst AD, Atkins AR, Beresford IJ, et al. GSK189254, a novel H3 receptor antagonist that binds to histamine H3 receptors in Alzheimer’s disease brain and improves cognitive performance in preclinical models. J Pharmacol Exp Ther 2007; 321: 1032-45.
[84]
Tiligada E, Kyriakidis K, Chazot PL, Passani MB. Histamine pharmacology and new CNS drug targets. CNS Neurosci Ther 2011; 17: 620-8.
[85]
Leurs R, Chazot PL, Shenton FC, Lim HD, de Esch IJ. Molecular and biochemical pharmacology of the histamine H4 receptor. Br J Pharmacol 2009; 157: 14-23.
[86]
Tiligada E, Zampeli E, Sander K, Stark H. Histamine H3 and H4 receptors as novel drug targets. Expert Opin Investig Drugs 2009; 18: 1519-31.
[87]
Leurs R, Chazot PL, Shenton FC, Lim HD, de Esch IJ. Molecular and biochemical pharmacology of the histamine H4 receptor. Br J Pharmacol 2009; 157: 14-23.
[88]
Krumm BE, Grisshammer R. Peptide ligand recognition by G protein-coupled receptors. Front Pharmacol 2015; 16: 6-48.
[89]
White JF, Noinaj N, Shibata Y, et al. Structure of the agonist-bound neurotensin receptor. Nature 2012; 490: 508-13.
[90]
Law PY, Loh HH. Regulation of opioid receptor activities. J Pharmacol Exp Ther 1999; 289: 607-24.
[91]
Mollereau C, Parmentier M, Mailleux P, et al. ORL1, a novel member of the opioid receptor family: cloning, functional expression and localization. FEBS Lett 1994; 341: 33-8.
[92]
Fenalti G, Giguere PM, Katritch V, et al. Molecular control of δ-opioid receptor signalling. Nature 2014; 13(506): 191-6.
[93]
Singh KD, Karnik SS. Angiotensin receptors: structure, function, signaling and clinical applications. J Cell Signal 2016; 1
[http://dx.doi.org/10.4172/jcs.1000111]
[94]
Zhang H, Unal H, Gati C, et al. Structure of the Angiotensin receptor revealed by serial femtosecond crystallography. Cell 2015; 161: 833-44.
[95]
Zhang H, Unal H, Desnoyer R, et al. structural basis for ligand recognition and functional selectivity at angiotensin receptor. J Biol Chem 2015; 290: 29127-39.
[96]
Duron E, Hanon O. Antihypertensive treatments, cognitive decline, and dementia. J Alzheimers Dis 2010; 20: 903-14.
[97]
Smith MT, Wyse BD, Edwards SR. Small molecule angiotensin II type 2 receptor (AT2R) antagonists as novel analgesics for neuropathic pain: comparative pharmacokinetics, radioligand binding, and efficacy in rats. Pain Med 2013; 14: 692-705.
[98]
Kemp BA, Howell NL, Gildea JJ, et al. AT2 receptor activation induces natriuresis and lowers blood pressure. Circ Res 2014; 115: 388-99.
[99]
Cavanagh PC, Dunk C, Pampillo M, et al. Gonadotropin-releasing hormone-regulated chemokine expression in human placentation. Am J Physiol Cell Physiol 2009; 297: C17-27.
[100]
Debruyne FM. Gonadotropin-releasing hormone antagonist in the management of prostate cancer. Rev Urol 2004; 6: S25-32.
[101]
Doehn C, Jocham D. Technology evaluation: abarelix, Praecis Pharmaceuticals. Curr Opin Mol Ther 2000; 2: 579-85.
[102]
Steinberg M. Degarelix: a gonadotropin-releasing hormone antagonist for the management of prostate cancer. Clin Ther 2009; (31Pt 2): 2312-31.
[103]
Tomera K, Gleason D, Gittelman M, et al. The gonadotropin-releasing hormone antagonist abarelix depot versus luteinizing hormone releasing hormone agonists leuprolide or goserelin: initial results of endocrinological and biochemical efficacies in patients with prostate cancer. J Urol 2001; 165: 1585-9.
[104]
Broqua P, Riviere PJ, Conn PM, et al. Pharmacological profile of a new, potent, and long-acting gonadotropin-releasing hormone antagonist: Degarelix. J Pharmacol Exp Ther 2002; 301: 95-102.
[105]
Samant MP, Hong DJ, Croston G, Rivier C, Rivier J. Novel gonadotropin-releasing hormone antagonists with substitutions at position 5. Biopolymers 2005; 80: 386-91.
[106]
Oberyé JJ, Mannaerts BM, Kleijn HJ, Timmer CJ. Pharmacokinetic and pharmacodynamic characteristics of ganirelix (Antagon/Orgalutran). I. Absolute bioavailability of 0.25 mg of ganirelix after a single subcutaneous injection in healthy female volunteers. Fertil Steril 1999; 72: 1001-5.
[107]
Gruber CW, Muttenthaler M, Freissmuth M. Ligand-based peptide design and combinatorial peptide libraries to target G protein-coupled receptors. Curr Pharm Des 2010; 16: 3071-88.
[108]
Gruber CW, Koehbach J, Muttenthaler M. Exploring bioactive peptides from natural sources for oxytocin and vasopressin drug discovery. Future Med Chem 2012; 4: 1791-8.
[109]
Arrowsmith S, Wray S. Oxytocin: its mechanism of action and receptor signalling in the myometrium. J Neuroendocrinol 2014; 26: 356-69.
[110]
Manning M, Stoev S, Chini B, et al. Peptide and non-peptide agonists and antagonists for the vasopressin and oxytocin V1a, V1b, V2 and OT receptors: research tools and potential therapeutic agents. Prog Brain Res 2008; 170: 473-512.
[111]
Di Giglio MG. Muttenthaler M2, Harpsøe K, et al. Development of a human vasopressin V1a-receptor antagonist from an evolutionary- related insect neuropeptide. Sci Rep 2017; 1; 7: 41002.
[112]
Boheler KR, Gundry RL. Concise review: Cell surface n-linked glycoproteins as potential stem cell markers and drug targets. Stem Cells Transl Med 2017; 6: 131-8.
[113]
Yin H, Flynn AD. Drugging membrane protein interactions. Annu Rev Biomed Eng 2016; 18: 51-76.
[114]
Kropp EM, Oleson BJ, Broniowska KA, et al. Inhibition of an NAD+ salvage pathway provides efficient and selective toxicity to human pluripotent stem cells. Stem Cells Transl Med 2015; 4: 483-93.
[115]
Brüser A, Schulz A, Rothemund S, et al. The activation mechanism of glycoprotein hormone receptors with implications in the cause and therapy of endocrine diseases. J Biol Chem 2016; 291: 508-20.
[116]
Smyth EM, Grosser T, Wang M, Yu Y, FitzGerald GA. Prostanoids in health and disease. J Lipid Res 2009; 50(Suppl.): S423-8.
[117]
Harizi H. The immunobiology of prostanoid receptor signaling in connecting innate and adaptive immunity. BioMed Res Int 2013.
[http://dx.doi.org/10.1155/2013/683405]
[118]
Harmar AJ. Family-B G-protein-coupled receptors. Genome Biol 2001; 2: 3013.
[119]
Knop FK, Vilsbøll T, Holst JJ. Incretin-based therapy of type 2 diabetes mellitus. Curr Protein Pept Sci 2009; 10: 46-55.
[120]
Hornby PJ, Moore BA. The therapeutic potential of targeting the glucagon-like peptide-2 receptor in gastrointestinal disease. Expert Opin Ther Targets 2011; 15: 637-46.
[121]
de Paula FJ, Rosen CJ. Back to the future: revisiting parathyroid hormone and calcitonin control of bone remodeling. Horm Metab Res 2010; 42: 299-306.
[122]
White CM, Ji S, Cai H, Maudsley S, Martin B. Therapeutic potential of vasoactive intestinal peptide and its receptors in neurological disorders. CNS Neurol Disord Drug Targets 2010; 9: 661-6.
[123]
Stengel A, Taché Y. Corticotropin-releasing factor signaling and visceral response to stress. Exp Biol Med 2010; 235: 1168-78.
[124]
Campbell RM, Bongers J, Felix AM. Rational design, synthesis, and biological evaluation of novel growth hormone releasing factor analogues. Biopolymers 1995; 37: 67-88.
[125]
Ding WQ, Cheng ZJ, McElhiney J, Kuntz SM, Miller LJ. Silencing of secretin receptor function by dimerization with a misspliced variant secretin receptor in ductal pancreatic adenocarcinoma. Cancer Res 2002; 62: 5223-9.
[126]
Miller LJ, Sexton PM, Dong M, Harikumar KG. The class B G-protein-coupled GLP-1 receptor: an important target for the treatment of type-2 diabetes mellitus. Int J Obes Suppl 2014; 4: S9-S13.
[127]
Mayo KE, Miller LJ, Bataille D, et al. International Union of Pharmacology. XXXV. The glucagon receptor family. Pharmacol Rev 2003; 55: 167-94.
[128]
Dunphy JL, Taylor RG, Fuller PJ. Tissue distribution of rat glucagon receptor and GLP-1 receptor gene expression. Mol Cell Endocrinol 1998; 141: 179-86.
[129]
Moens K, Flamez D, Van Schravendijk C, et al. Dual glucagon recognition by pancreatic beta-cells via glucagon and glucagon-like peptide 1 receptors. Diabetes 1998; 47: 66-72.
[130]
Rondard P, Goudet C, Kniazeff J, Pin JP, Prezeau L. The complexity of their activation mechanism opens new possiblities for the modulation of mGlu and GABAB class C G protein-coupled receptors. Neuropharmacology 2011; 60: 82-92.
[131]
Pin JP, Galvez T, Prézeau L. Evolution, structure, and activation mechanism of family 3/C G-protein-coupled receptors. Pharmacol Ther 2003; 98: 325-54.
[132]
Urwyler S. Allosteric modulation of family C G-protein-coupled receptors: from molecular insights to therapeutic perspectives. Pharmacol Rev 2011; 63: 59-126.
[133]
Bettler B, Tiao JY. Molecular diversity, trafficking and subcellular localization of GABAB receptors. Pharmacol Ther 2006; 110: 533-43.
[134]
Chun L, Zhang WH, Liu JF. Structure and ligand recognition of class C GPCRs. Acta Pharmacol Sin 2012; 33: 312-23.
[135]
Deal C. Future therapeutic targets in osteoporosis. Curr Opin Rheumatol 2009; 21: 380-5.
[136]
Niswender CM, Conn PJ. Metabotropic glutamate receptors: physiology, pharmacology, and disease. Annu Rev Pharmacol Toxicol 2010; 50: 295-322.
[137]
Lewis JL, Bonner J, Modrell M, et al. Reiterated Wnt signaling during zebrafish neural crest development. Development 2004; 131: 1299-308.
[138]
Galon-Tilleman H, Yang H, Bednarek MA, et al. Apelin-36 Modulates Blood Glucose and Body Weight Independently of Canonical APJ Receptor Signaling. J Biol Chem 2017; 292: 1925-33.
[139]
Chng SC, Ho L, Tian J, et al. ELABELA: a hormone essential for heart development signals via the apelin receptor. Dev Cell 2013; 27: 672-80.
[140]
Bai B, Cai X, Jiang Y, Karteris E, Chen J. Heterodimerization of apelin receptor and neurotensin receptor 1 induces phosphorylation of ERK1/2 and cell proliferation via Gαq-mediated mechanism. J Cell Mol Med 2014; 18: 2071-81.
[141]
Pols TW, Noriega LG, Nomura M, Auwerx J, Schoonjans K. The bile acid membrane receptor TGR5 as an emerging target in metabolism and inflammation. J Hepatol 2011; 54: 1263-72.
[142]
Keitel V, Cupisti K, Ullmer C, et al. The membrane-bound bile acid receptor TGR5 is localized in the epithelium of human gallbladders. Hepatology 2009; 50: 861-70.
[143]
McClanahan T, Koseoglu S, Smith K, et al. Identification of overexpression of orphan G protein-coupled receptor GPR49 in human colon and ovarian primary tumors. Cancer Biol Ther 2006; 5: 419-26.
[144]
Gong X, Carmon KS, Lin Q, et al. LGR6 is a high affinity receptor of R-spondins and potentially functions as atumor suppressor. PLoS One 2012; 7: e37137.
[145]
Dijksterhuis JP, Petersen J, Schulte G. WNT/Frizzled signalling: receptor-ligand selectivity with focus on FZD-G protein signalling and its physiological relevance: IUPHAR Review 3. Br J Pharmacol 2014; 171: 1195-209.
[146]
Date Y, Kojima M, Hosoda H, et al. Ghrelin, a novel growth hormone-releasing acylated peptide, is synthesized in a distinct endocrine cell type in the gastrointestinal tracts of rats and humans. Endocrinology 2000; 141: 4255-61.
[147]
Callaghan B, Furness JB. Novel and conventional receptors for ghrelin, desacyl-ghrelin, and pharmacologically related compounds. Pharmacol Rev 2014; 66: 984-1001.
[148]
Steinhoff MS, von Mentzer B, Geppetti P, Pothoulakis C, Bunnett NW. Tachykinins and their receptors: contributions to physiological control and the mechanisms of disease. Physiol Rev 2014; 94: 265-301.
[149]
Ferland DJ, Watts SW. Chemerin: A comprehensive review elucidating the need for cardiovascular research. Pharmacol Res 2015; 99: 351-61.
[150]
Mohammad S. Role of free fatty acid receptor 2 (ffar2) in the regulation of metabolic homeostasis. Curr Drug Targets 2015; 16: 771-5.
[151]
Villa SR, Priyadarshini M, Fuller MH, et al. Loss of Free Fatty Acid Receptor 2 leads to impaired islet mass and beta cell survival. Sci Rep 2016; 6: 28159.
[152]
Kihara Y, Mizuno H, Chun J. Lysophospholipid receptors in drug discovery. Exp Cell Res 2015; 333: 171-7.
[153]
Alves M, Beamer E, Engel T. The metabotropic purinergic p2y receptor family as novel drug target in epilepsy. Front Pharmacol 2018; 9: 193.
[154]
Koizumi S, Shigemoto-Mogami Y, Nasu-Tada K, et al. UDP acting at P2Y6 receptors is a mediator of microglial phagocytosis. Nature 2007; 446: 1091-5.
[155]
Steculorum SM, Timper K, Engström Ruud L, et al. inhibition of p2y6 signaling in agrp neurons reduces food intake and improves systemic insulin sensitivity in obesity. Cell Reports 2017; 18: 1587-97.
[156]
Yang X, Lou Y, Liu G, et al. Microglia P2Y6 receptor is related to Parkinson’s disease through neuroinflammatory process. J Neuroinflammation 2017; 14: 38.
[157]
Morri M, Sanchez-Romero I, Tichy AM, et al. Optical functionalization of human Class A orphan G-protein-coupled receptors. Nat Commun 2018; 9: 1950.
[158]
Liu Y, Zhang Q, Chen LH, et al. Design and synthesis of 2-alkylpyrimidine-4,6-diol and 6-alkylpyridine-2,4-diol as potent gpr84 agonists. ACS Med Chem Lett 2016; 7: 579-83.
[159]
Dudley DT, Summerfelt RM. Regulated expression of angiotensin II (AT2) binding sites in R3T3 cells. Regul Pept 1993; 44: 199-206.
[160]
Karnik SS, Unal H, Kemp JR, et al. International union of basic and clinical pharmacology. XCIX. Angiotensin Receptors: Interpreters of pathophysiological angiotensinergic stimuli. Pharmacol Rev 2015; 67: 754-819.
[161]
Manthey HD, Woodruff TM, Taylor SM, Monk PN. Complement component 5a (C5a). Int J Biochem Cell Biol 2009; 41: 2114-7.
[162]
Köhl J. Self, non-self, and danger: a complementary view. Adv Exp Med Biol 2006; 586: 71-94.
[163]
Yue Y, Yin L, Weizhen Z. The growth hormone secretagogue receptor: its intracellular signaling and regulation. Int J Mol Sci 2014; 15: 4837-55.
[164]
Ghigo E, Broglio F, Arvat E, et al. Ghrelin: More than a natural GH secretagogue and/or an orexigenic factor. Clin Endocrinol 2005; 62: 1-17.
[165]
Novoselova TV, Chan LF, Clark AJL. Pathophysiology of melanocortin receptors and their accessory proteins. Best Pract Res Clin Endocrinol Metab 2018; 32: 93-106.
[166]
Mountjoy KG, Robbins LS, Mortrud MT, Cone RD. The cloning of a family of genes that encode the melanocortin receptors. Sci 1992; 257: 1248-51.
[167]
Valverde P, Healy E. Jackson, Rees JL, Thody AJ. Variants of the melanocyte-stimulating hormone receptor gene are associated with red hair and fair skin in humans. Nat Genet 1995; 11: 328-30.
[168]
O’Rahilly S, Yeo GSH, Farooqi IS. Melanocortin receptors weigh in. Nat Med 2004; 10: 351-2.
[169]
Friedman JM, Halaas JL. Leptin and the regulation of body weight in mammals. Nature 1998; 395: 763-70.
[170]
Toll L, Bruchas MR, Calo’ G, Cox BM, Zaveri NT. Nociceptin/orphanin fq receptor structure, signaling, ligands, functions, and interactions with opioid systems. Pharmacol Rev 2016; 68: 419-57.
[171]
Bathgate RAD, Kocan M, Scott DJ, et al. The relaxin receptor as a therapeutic target - perspectives from evolution and drug targeting. Pharmacol Ther 2018.
[http://dx.doi.org/10.1016/j.pharmthera.2018.02.008]
[172]
Deen M, Correnti E, Kamm K, et al. Blocking CGRP in migraine patients - a review of pros and cons. J Headache Pain 2017; 18: 96.
[173]
Voss T, Lipton RB, Dodick DW, et al. A phase IIb randomized, doubleblind, placebo-controlled trial of ubrogepant for the acute treatment of migraine. Cephalalgia 2016; 36: 887-98.
[174]
Delgado M, Ganea D. Vasoactive intestinal peptide: a neuropeptide with pleiotropic immune functions. Amino Acids 2013; 45: 25-39.
[175]
Ganea D, Hooper KM, Kong W. The Neuropeptide Vip: Direct Effects on Immune Cells and Involvement in Inflammatory and Autoimmune Diseases. Acta Physiol (Oxf) 2015; 213: 442-52.
[176]
Gozes I, Glowa J, Brenneman DE, et al. Learning and sexual deficiencies in transgenic mice carrying a chimeric vasoactive intestinal peptide gene. J Mol Neurosci 1993; 4: 185-93.
[177]
Niswender CM, Conn PJ. Metabotropic glutamate receptors: physiology, pharmacology, and disease. Annu Rev Pharmacol Toxicol 2010; 50: 295-322.
[178]
Oprea TI, Bologa CG, Brunak S, et al. Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 2018; 17: 317-32.
[179]
Wacker D, Stevens RC, Roth BL. How ligands illuminate GPCR molecular pharmacology. Cell 2017; 170: 414-27.
[180]
Jacobson KA, Costanzi S, Paoletta S. Computational studies to predict or explain G protein coupled receptor polypharmacology. Trends Pharmacol Sci 2014; 35: 658-63.
[181]
Baldus M. GPCR-Lock and key become flexible. Nat Chem Biol 2018; 14: 201-2.
[182]
Samadishadlou M, Farshbaf M, Annabi N, et al. Magnetic carbon nanotubes: preparation, physical properties, and applications in biomedicine. Artif Cells Nanomed Biotechnol 2018; 46: 1314-30.
[183]
Wolfram J, Zhu M, Yang Y, et al. Safety of nanoparticles in medicine. Curr Drug Targets 2015; 16: 1671-81.
[184]
Bobo D, Robinson KJ, Islam J, Thurecht KJ, Corrie SR. nanoparticle-based medicines: a review of fda-approved materials and clinical trials to date. Pharm Res 2016; 33: 2373-87.
[185]
Accardo A, Aloj L, Aurilio M, Morelli G, Tesauro D. Receptor binding peptides for target-selective delivery of nanoparticles encapsulated drugs. Int J Nanomedicine 2014; 9: 1537-57.
[186]
Sadat SM, Saeidnia S, Nazarali AJ, Haddadi A. Nano-pharmaceutical formulations for targeted drug delivery againstHER2 in breast cancer. Curr Cancer Drug Targets 2015; 15: 71-86.
[187]
Goudarzi M, Mir N, Mousavi-Kamazani M, Bagheri S, Salavati-Niasari M. Biosynthesis and characterization of silver nanoparticles prepared from two novel natural precursors by facile thermal decomposition methods. Sci Rep 2016; 6: 32539.
[188]
Tamuly C, Hazarika M, Bordoloi M. Bio-derived size/shape controllable gold nanoparticles and its antimicrobial activity. J Bionanosci 2015; 9: 1-5.
[189]
Dutta PP, Bordoloi M, Gogoi K, et al. Antimalarial silver and gold nanoparticles: Green synthesis, characterization and in vitro study. Biomed Pharmacother 2017; 91: 567-80.
[190]
Roohani-Esfahani SI, Nouri-Khorasani S, Lu ZF, et al. Modification of porous calcium phosphate surfaces with different geometries of bioactive glass nanoparticles. Mater Sci Eng C 2012; 32: 830-9.
[191]
Mohandes F, Salavati-Niasari M, Fathi M, et al. Hydroxyapatite nanocrystals: simple preparation, characterization and formation mechanism. Mater Sci Eng C Mater Biol Appl 2014; 45: 29-36.
[192]
Mohandes F, Salavati-Niasari M. Particle size and shape modification of hydroxyapatite nanostructures synthesized via a complexing agent-assisted route. Mater Sci Eng C Mater Biol Appl 2014; 40: 288-98.
[193]
Pachuta-Stec A, Rzymowska J, Mazur L, et al. Synthesis, structure elucidation and antitumour activity of N-substituted amides of 3-(3-ethylthio-1,2,4-triazol-5-yl)propenoic acid. Eur J Med Chem 2009; 44: 3788-93.
[194]
Cassee FR, van Balen EC, Singh C, et al. Exposure, health and ecological effects review of engineered nanoscale cerium and cerium oxide associated with its use as a fuel additive. Crit Rev Toxicol 2011; 41: 213-29.
[195]
Youn YS, Bae YH. Perspectives on the past, present, and future of cancer nanomedicine. Adv Drug Deliv Rev 2018; 130: 3-11.
[196]
Lin WZ, Xiao X. GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis. Protein Eng Des Sel 2009; 22: 699-705.
[197]
Xiao X, Wang P. GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J Comput Chem 2009; 30: 1414-23.
[198]
Xiao X, Lin WZ. Recent advances in predicting G-protein coupled receptor classification. Curr Bioinform 2012; 7: 132-42.
[199]
Shen HB. Recent advances in developing web-servers for predicting protein attributes. Nat Sci 2009; 1: 63-92.
[200]
Chou KC. Impacts of bioinformatics to medicinal chemistry. Med Chem 2015; 11: 218-34.
[201]
Xiao X, Min JL, Wang P, Chou KC. iGPCR-Drug: A web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One 2013; 8: e72234.
[202]
Lin WZ, Xiao X. Wenxiang: a web-server for drawing wenxiang diagrams. Nat Sci 2011; 3: 862-5.
[203]
Chen W, Lei TY, Jin DC, Lin H, Chou KC. PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition. Anal Biochem 2014; 456: 53-60.
[204]
Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol Biosyst 2015; 11: 2620-34.
[205]
Liu B, Liu F, Wang X, et al. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 2015; 43: W65-71.
[206]
Liu B, Wu H, Chou KC. Pse-in-One 2.0: An improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein Sequences. Nat Sci 2017; 9: 67-91.
[207]
Cheng X, Zhao SG, Xiao X, Chou KC. iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics 2017; 33: 341-6.
[208]
Cheng X, Zhao SG, Xiao X, Chou KC. iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget 2017; 8: 58494-503.
[209]
Cheng X, Xiao X, Chou KC. pLoc-mPlant: predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC. Mol Biosyst 2017; 13: 1722-7.
[210]
Cheng X, Xiao X, Chou KC. pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene (Erratum: ibid., 2018, Vol.644, 156-156) 2017; 628: 315-21.
[211]
Cheng X, Xiao X, Chou KC. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 2018; 34: 1448-56.
[212]
Cheng X, Zhao SG, Lin WZ, Xiao X, Chou KC. pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics 2017; 33: 3524-31.
[213]
Xiao X, Cheng X, Su S, Mao Q, Chou KC. pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat Sci 2017; 9: 331-49.
[214]
Cheng X, Xiao X, Chou KC. pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics 2017.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.002]
[215]
Cheng X, Xiao X, Chou KC. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 2018; 110: 50-8.
[216]
Chou KC. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr Top Med Chem 2017; 17: 2337-58.
[217]
Chou KC. Prediction of protein cellular attributes using pseudo amino acid composition PROTEINS: Structure, Function, and Genetics (Erratum: ibid, 2001, Vol44, 60) 2001; 43: 246-55
[218]
Chou KC. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr Proteomics 2009; 6: 262-74.
[219]
Chen W, Feng P, Ding H, Lin H, Chou KC. Using deformation energy to analyze nucleosome positioning in genomes. Genomics 2016; 107: 69-75.
[220]
Ding H, Deng EZ, Yuan LF, et al. iCTX-Type: A sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Res Int 2014; 286419.
[221]
Lin H, Deng EZ, Ding H, Chen W, Chou KC. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res 2014; 42: 12961-72.
[222]
Zhang CJ, Tang H, Li WC, et al. iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget 2016; 7: 69783-93.
[223]
Feng P, Yang H, Ding H, et al. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 2018.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005]
[224]
Xu Y, Shao XJ, Wu LY, et al. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 2013; 1: e171.
[225]
Qiu WR, Jiang SY, Xu ZC, Xiao X, Chou KC. iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget 2017; 8: 41178-88.
[226]
Chen W, Tang H, Ye J, Lin H, Chou KC. iRNA-PseU: Identifying RNA pseudouridine sites. Mol Ther - Nuc Acids 2016; 5: e332.
[227]
Chen W, Feng P, Yang H, et al. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget 2017; 8: 4208-17.
[228]
Chen W, Feng P, Yang H, et al. iRNA-3typeA: identifying 3-types of modification at RNA’s adenosine sites. Mol Ther - Nuc Acids 2018; 11: 468-74.
[229]
Su ZD, Huang Y, Zhang ZY, et al. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics 2018.
[http://dx.doi.org/10.1093/bioinformatics/bty508]
[230]
Yang H, Qiu WR, Liu G, et al. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int J Biol Sci 2018; 14: 883-91.
[231]
Xiao X, Min JL, Lin WZ, et al. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via the benchmark dataset optimization approach. J Biomol Struct Dyn 2015; 33: 2221-33.
[232]
Xiao X, Min JL, Wang P, Chou KC. Predict drug-protein interaction in cellular networking. Curr Top Med Chem 2013; 13: 1707-12.
[233]
Chou KC, Zhang CT, Maggiora GM. Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth. Biopolymers 1994; 34: 143-53.
[234]
Chou KC. Some remarks on predicting multi-label attributes in molecular biosystems. Mol Biosyst 2013; 9: 1092-100.
[235]
Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J Theor Biol 2011; 273: 236-47.
[236]
Jia J, Liu Z, Xiao X, Liu B, Chou KC. iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J Theor Biol 2015; 377: 47-56.
[237]
Liu B, Wang S, Long R, Chou KC. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics 2017; 33: 35-41.

Rights & Permissions Print Export Cite as
© 2024 Bentham Science Publishers | Privacy Policy