Review Article

Understanding Membrane Protein Drug Targets in Computational Perspective

Author(s): Jianting Gong, Yongbing Chen, Feng Pu, Pingping Sun, Fei He, Li Zhang, Yanwen Li*, Zhiqiang Ma* and Han Wang*

Volume 20, Issue 5, 2019

Page: [551 - 564] Pages: 14

DOI: 10.2174/1389450120666181204164721

Price: $65

Abstract

Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.

Keywords: Membrane protein, drug targets, drug discovery, computational biology, machine learning, biological networks.

Graphical Abstract

[1]
Almeida JG, Preto AJ, Koukos PI, Bonvin A, Moreira IS. Membrane proteins structures: A review on computational modeling tools. Biochim Biophys Acta 2017; 1859(10): 2021-39.
[2]
Gromiha MM, Ou YY. Bioinformatics approaches for functional annotation of membrane proteins. Brief Bioinform 2014; 15(2): 155-68.
[3]
Uhlen M, Fagerberg L, Hallstrom BM, et al. Proteomics. Tissue-based map of the human proteome. Sci 2015; 347(6220): 1260419.
[4]
Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001; 305(3): 567-80.
[5]
Koopmans T, Gosens R. Revisiting asthma therapeutics: focus on WNT signal transduction. Drug Discov Today 2018; 23(1): 49-62.
[6]
Roth BL, Sheffler DJ, Kroeze WK. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat Rev Drug Discov 2004; 3(4): 353-9.
[7]
Richards JL, Yap YA, McLeod KH, Mackay CR, Marino E. Dietary metabolites and the gut microbiota: an alternative approach to control inflammatory and autoimmune diseases. Clin Transl Immunology 2016; 5(5): e82.
[8]
Sloop KW, Emmerson PJ, Statnick MA, Willard FS. The current state of GPCR-based drug discovery to treat metabolic disease. Br J Pharmacol 2018; 175(21): 4060-71.
[9]
Wallin E, von Heijne G. Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci 1998; 7(4): 1029-38.
[10]
Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nat Rev Drug Discov 2006; 5(12): 993-6.
[11]
Dolgin E. The greatest hits of the human genome. Nature 2017; 551(7681): 427-31.
[12]
Zou Q, Li X, Jiang Y, Zhao Y, Wang G. BinMemPredict: a web server and software for predicting membrane protein types. Curr Proteomics 2013; 10(1): 2-9.
[13]
Wen M, Zhang Z, Niu S, et al. Deep-learning-based drug-target interaction prediction. J Proteome Res 2017; 16(4): 1401-9.
[14]
Bai XC, McMullan G, Scheres SHW. How cryo-EM is revolutionizing structural biology. Trends Biochem Sci 2015; 40(1): 49-57.
[15]
Miao JW, Ishikawa T, Robinson IK, Murnane MM. Beyond crystallography: Diffractive imaging using coherent x-ray light sources. Sci 2015; 348(6234): 530-5.
[16]
Miao Y, Cross TA. Solid state NMR and protein-protein interactions in membranes. Curr Opin Struct Biol 2013; 23(6): 919-28.
[17]
Yin H, Flynn AD. Drugging membrane protein interactions. Annu Rev Biomed Eng 2016; 18: 51-76.
[18]
Chen YC, Tolbert R, Aronov AM, et al. Prediction of protein pairs sharing common active ligands using protein sequence, structure, and ligand similarity. J Chem Inf Model 2016; 56(9): 1734-45.
[19]
Papadatos G, Overington JP. The ChEMBL database: a taster for medicinal chemists. Future Med Chem 2014; 6(4): 361-4.
[20]
Gilson MK, Liu T, Baitaluk M, et al. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 2016; 44(D1): D1045-53.
[21]
Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 2018; 46(D1): D1074-82.
[22]
Li YH, Yu CY, Li XX, et al. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res 2018; 46(D1): D1121-7.
[23]
Tanabe M, Kanehisa M. Using the KEGG database resourceCurrent protocols in bioinformatics / editoral board, Andreas D Baxevanis [et al] 2012;Chapter 1:Unit1 12 .
[24]
Pandy-Szekeres G, Munk C, Tsonkov TM, et al. GPCRdb in 2018: adding GPCR structure models and ligands. Nucleic Acids Res 2018; 46(D1): D440-6.
[25]
Saier MH Jr, Reddy VS, Tsu BV, et al. The transporter classification database (tcdb): recent advances. Nucleic Acids Res 2016; 44(D1): D372-9.
[26]
Ito J, Ikeda K, Yamada K, Mizuguchi K, Tomii K. PoSSuM v.2.0: data update and a new function for investigating ligand analogs and target proteins of small-molecule drugs. Nucleic Acids Res 2015; 43(Database issue): D392-8.
[27]
Rask-Andersen M, Almen MS, Schioth HB. Trends in the exploitation of novel drug targets. Nat Rev Drug Discov 2011; 10(8): 579-90.
[28]
The Uni Prot C. UniProt: the universal protein knowledgebase. Nucleic Acids Res 2017; 45(D1): D158-69.
[29]
Overington JP, Al-Lazikani B, Hopkins AL. Opinion-How many drug targets are there? Nat Rev Drug Discov 2006; 5(12): 993-6.
[30]
Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov 2002; 1(9): 727-30.
[31]
Rask-Andersen M, Masuram S, Schioth 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.
[32]
Lin Y, Mehta S, Kucuk-McGinty H, et al. Drug target ontology to classify and integrate drug discovery data. J Biomed Semantics 2017; 8(1): 50.
[33]
Topiol S. Current and future challenges in gpcr drug discovery. Methods Mol Biol 2018; 1705: 1-21.
[34]
Hauser AS, Attwood MM, Rask-Andersen M, Schioth HB, Gloriam DE. Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov 2017; 16(12): 829-42.
[35]
Andrews SP, Brown GA, Christopher JA. Structure-based and fragment-based gpcr drug discovery. ChemMedChem 2014; 9(2): 256-75.
[36]
Salon JA, Lodowski DT, Palczewski K. The significance of G protein-coupled receptor crystallography for drug discovery. Pharmacol Rev 2011; 63(4): 901-37.
[37]
Fredriksson R, Lagerstrom MC, Lundin LG, Schioth HB. The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Mol Pharmacol 2003; 63(6): 1256-72.
[38]
Sexton PM, Christopoulos A. To bind or not to bind: unravelling gpcr polypharmacology. Cell 2018; 172(4): 636-8.
[39]
Kakarala KK, Jamil K. Sequence-structure based phylogeny of GPCR Class A Rhodopsin receptors. Mol Phylogenet Evol 2014; 74: 66-96.
[40]
Thomsen W, Frazer J, Unett D. Functional assays for screening GPCR targets. Curr Opin Biotechnol 2005; 16(6): 655-65.
[41]
Lee Y, Basith S, Choi S. Recent advances in structure-based drug design targeting class a g protein-coupled receptors utilizing crystal structures and computational simulations. J Med Chem 2018; 61(1): 1-46.
[42]
Alexander SP, Christopoulos A, Davenport AP, et al. The concise guide to pharmacology 2017/18: G protein-coupled receptors. Br J Pharmacol 2017; 174(Suppl. 1): S17-S129.
[43]
Dorsam RT, Gutkind JS. G-protein-coupled receptors and cancer. Nat Rev Cancer 2007; 7(2): 79-94.
[44]
Yu FX, Zhao B, Panupinthu N, et al. Regulation of the Hippo-YAP pathway by G-protein-coupled receptor signaling. Cell 2012; 150(4): 780-91.
[45]
Yu FX, Zhang Y, Park HW, et al. Protein kinase A activates the Hippo pathway to modulate cell proliferation and differentiation. Genes Dev 2013; 27(11): 1223-32.
[46]
Tao Y, Cai F, Shan L, et al. The Hippo signaling pathway: an emerging anti-cancer drug target. Discov Med 2017; 24(130): 7-18.
[47]
Harvey KF, Zhang X, Thomas DM. The Hippo pathway and human cancer. Nat Rev Cancer 2013; 13(4): 246-57.
[48]
Zhou X, Wang Z, Huang W, Lei QY. G protein-coupled receptors: bridging the gap from the extracellular signals to the Hippo pathway. Acta Biochim Biophys Sin (Shanghai) 2015; 47(1): 10-5.
[49]
Baidya M, Dwivedi H, Shukla AK. Frozen in action: cryo-EM structure of a GPCR-G-protein complex. Nat Struct Mol Biol 2017; 24(6): 500-2.
[50]
Liang YL, Khoshouei M, Radjainia M, et al. Phase-plate cryo-EM structure of a class B GPCR-G-protein complex. Nature 2017; 546(7656): 118-23.
[51]
Wu H, Wang K, Lu L, et al. Deep conditional random field approach to transmembrane topology prediction and application to gpcr three-dimensional structure modeling IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 2017; 14(5): 1106-14
[52]
Shen H, Chou JJ. MemBrain: improving the accuracy of predicting transmembrane helices. PLoS One 2008; 3(6): e2399.
[53]
Chen SA, Ou YY, Lee TY, Gromiha MM. Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties. Bioinformatics 2011; 27(15): 2062-7.
[54]
Saier MH Jr, Tran CV, Barabote RD. TCDB: the Transporter Classification Database for membrane transport protein analyses and information. Nucleic Acids Res 2006; 34: D181-6.
[55]
Tarling EJ, de Aguiar Vallim TQ, Edwards PA. Role of ABC transporters in lipid transport and human disease. Trends Endocrinol Metab 2013; 24(7): 342-50.
[56]
Jordheim LP, Durantel D, Zoulim F, Dumontet C. Advances in the development of nucleoside and nucleotide analogues for cancer and viral diseases. Nat Rev Drug Discov 2013; 12(6): 447-64.
[57]
Minuesa G, Huber-Ruano I, Pastor-Anglada M, et al. Drug uptake transporters in antiretroviral therapy. Pharmacol Ther 2011; 132(3): 268-79.
[58]
Pastor-Anglada M, Perez-Torras S. Nucleoside transporter proteins as biomarkers of drug responsiveness and drug targets. Front Pharmacol 2015; 6.
[59]
Tahlan K, Wilson R, Kastrinsky DB, et al. SQ109 targets MmpL3, a membrane transporter of trehalose monomycolate involved in mycolic acid donation to the cell wall core of Mycobacterium tuberculosis. Antimicrob Agents Chemother 2012; 56(4): 1797-809.
[60]
Gasser PJ, Daws LC. Editorial for the special issue: Monoamine transporters in health and disease. J Chem Neuroanat 2017; 83-84: 1-2.
[61]
Zhao YW, Su ZD, Yang W, et al. IonchanPred 2.0: A tool to predict ion channels and their types. Int J Mol Sci 2017; 18(9)
[62]
Liu WX, Deng EZ, Chen W, Lin H. Identifying the subfamilies of voltage-gated potassium channels using feature selection technique. Int J Mol Sci 2014; 15(7): 12940-51.
[63]
Chen W, Lin H. Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine. Comput Biol Med 2012; 42(4): 504-7.
[64]
Lin H, Ding H. Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J Theor Biol 2011; 269(1): 64-9.
[65]
Miranda WE, Ngo VA, Perissinotti LL, Noskov SY. Computational membrane biophysics: From ion channel interactions with drugs to cellular function. Biochim Biophys Acta 2017; 1865(11 Pt B): 1643-53.
[66]
Chou KC. Insights from modeling three-dimensional structures of the human potassium and sodium channels. J Proteome Res 2004; 3(4): 856-61.
[67]
Dunlop J, Bowlby M, Peri R, Vasilyev D, Arias R. High-throughput electrophysiology: an emerging paradigm for ion-channel screening and physiology. Nat Rev Drug Discov 2008; 7(4): 358-68.
[68]
Huang C, Zhang R, Chen Z, et al. Predict potential drug targets from the ion channel proteins based on SVM. J Theor Biol 2010; 262(4): 750-6.
[69]
Kutzner C, Kopfer DA, Machtens JP, et al. Insights into the function of ion channels by computational electrophysiology simulations. Bba-Biomembranes 2016; 1858(7): 1741-52.
[70]
Imbrici P, Liantonio A, Camerino GM, et al. Therapeutic approaches to genetic ion channelopathies and perspectives in drug discovery. Front Pharmacol 2016; 7: 121.
[71]
Konstantopoulou A, Tsikrikas S, Asvestas D, Korantzopoulos P, Letsas KP. Mechanisms of drug-induced proarrhythmia in clinical practice. World J Cardiol 2013; 5(6): 175-85.
[72]
Abstracts of papers at the seventieth annual meeting of the society of general physiologistS: Genetic and animal models for ion channel function in physiology and disease. J Gen Physiol 2016; 148(2): 183.
[73]
Gaulton A, Bellis LJ, Bento AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 2012; 40: D1100-7.
[74]
Barneh F, Jafari M, Mirzaie M. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database. Brief Bioinform 2016; 17(6): 1070-80.
[75]
[76]
Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 2014; 42(Database issue): D1091-7.
[77]
Knox C, Law V, Jewison T, et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res 2011; 39: D1035-41.
[78]
Wishart DS, Knox C, Guo AC, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 2008; 36: D901-6.
[79]
Wishart DS, Knox C, Guo AC, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 2006; 34: D668-72.
[80]
Yang H, Qin C, Li YH, et al. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res 2016; 44(D1): D1069-74.
[81]
Qin C, Zhang C, Zhu F, et al. Therapeutic target database update 2014: a resource for targeted therapeutics. Nucleic Acids Res 2014; 42: D1118-23.
[82]
Zhu F, Shi Z, Qin C, et al. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res 2012; 40: D1128-36.
[83]
Zhu F, Han B, Kumar P, et al. Update of TTD: Therapeutic target database. Nucleic Acids Res 2010; 38: D787-91.
[84]
Chen X, Ji ZL, Chen YZ. TTD: Therapeutic target database. Nucleic Acids Res 2002; 30(1): 412-5.
[85]
Munk C, Isberg V, Mordalski S, et al. GPCRdb: the G protein-coupled receptor database - an introduction. Br J Pharmacol 2016; 173(14): 2195-207.
[86]
Molloy C. Drug discovery tomorrow: how to Catapult ourselves into the future. Drug Discov Today 2018; 23(1): 1-3.
[87]
Cucurull-Sanchez L, Spink KG, Moschos SA. Relevance of systems pharmacology in drug discovery. Drug Discov Today 2012; 17(13-14): 665-70.
[88]
Berger SI, Iyengar R. Role of systems pharmacology in understanding drug adverse events. Wiley Interdiscip Rev Syst Biol Med 2011; 3(2): 129-35.
[89]
Tanrikulu Y, Kruger B, Proschak E. The holistic integration of virtual screening in drug discovery. Drug Discov Today 2013; 18(7-8): 358-64.
[90]
Kraemer O, Hazemann I, Podjarny AD, Klebe G. Virtual screening for inhibitors of human aldose reductase. Proteins 2004; 55(4): 814-23.
[91]
Garland SL. Are GPCRs still a source of new targets? J Biomol Screen 2013; 18(9): 947-66.
[92]
Ripphausen P, Nisius B, Bajorath J. State-of-the-art in ligand-based virtual screening. Drug Discov Today 2011; 16(9-10): 372-6.
[93]
Sun H. Pharmacophore-based virtual screening. Curr Med Chem 2008; 15(10): 1018-24.
[94]
Cross JB. Methods for virtual screening of gpcr targets: Approaches and Challenges. Methods Mol Biol 2018; 1705: 233-64.
[95]
Bock JR, Gough DA. Virtual screen for ligands of orphan G protein-coupled receptors. J Chem Inf Model 2005; 45(5): 1402-14.
[96]
Jacob L, Hoffmann B, Stoven V, Vert JP. Virtual screening of GPCRs: An in silico chemogenomics approach. BMC Bioinformatics 2008; 9: 363.
[97]
Hawkins PCD, Stahl G. Ligand-based methods in GPCR computer-aided drug design. Methods Mol Biol 2018; 1705: 365-74.
[98]
Zhang R, Xie X. Tools for GPCR drug discovery. Acta Pharmacol Sin 2012; 33(3): 372-84.
[99]
Yarnitzky T, Levit A, Niv MY. Homology modeling of G-protein-coupled receptors with X-ray structures on the rise. Curr Opin Drug Discov Devel 2010; 13(3): 317-25.
[100]
Mobarec JC, Sanchez R, Filizola M. Modern homology modeling of G-protein coupled receptors: which structural template to use? J Med Chem 2009; 52(16): 5207-16.
[101]
Rosenbaum DM, Rasmussen SG, Kobilka BK. The structure and function of G-protein-coupled receptors. Nature 2009; 459(7245): 356-63.
[102]
Ananthan S, Zhang W, Hobrath JV. Recent advances in structure-based virtual screening of G-protein coupled receptors. AAPS J 2009; 11(1): 178-85.
[103]
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004; 3(11): 935-49.
[104]
Ngo T, Kufareva I, Coleman JLJ, et al. Identifying ligands at orphan GPCRs: current status using structure-based approaches. Br J Pharmacol 2016; 173(20): 2934-51.
[105]
Hanson MA, Stevens RC. Discovery of new GPCR biology: one receptor structure at a time. Structure 2009; 17(1): 8-14.
[106]
Rasmussen SG, Choi HJ, Rosenbaum DM, et al. Crystal structure of the human beta2 adrenergic G-protein-coupled receptor. Nature 2007; 450(7168): 383-7.
[107]
Jaakola VP, Griffith MT, Hanson MA, et al. The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Sci 2008; 322(5905): 1211-7.
[108]
Wu B, Chien EY, Mol CD, et al. Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide antagonists. Sci 2010; 330(6007): 1066-71.
[109]
Shimamura T, Shiroishi M, Weyand S, et al. Structure of the human histamine H1 receptor complex with doxepin. Nature 2011; 475(7354): 65-70.
[110]
Chien EYT, Liu W, Zhao QA, et al. Structure of the human dopamine d3 receptor in complex with a d2/d3 selective antagonist. Sci 2010; 330(6007): 1091-5.
[111]
Rodriguez D, Ranganathan A, Carlsson J. Discovery of GPCR ligands by molecular docking screening: novel opportunities provided by crystal structures. Curr Top Med Chem 2015; 15(24): 2484-503.
[112]
Kooistra AJ, Vischer HF, McNaught-Flores D, et al. Function-specific virtual screening for GPCR ligands using a combined scoring method. Sci Rep 2016; 6.
[113]
Radestock S, Weil T, Renner S. Homology model-based virtual screening for GPCR ligands using docking and target-biased scoring. J Chem Inf Model 2008; 48(5): 1104-17.
[114]
Christopher JA, Aves SJ, Bennett KA, et al. Fragment and structure-based drug discovery for a class C GPCR: Discovery of the mglu5 negative allosteric modulator HTL14242 (3-Chloro-5-[6-(5-fluoropyridin-2-yl)pyrimidin-4-yl]benzonitrile). J Med Chem 2015; 58(16): 6653-64.
[115]
Boezio B, Audouze K, Ducrot P, Taboureau O. Network-based Approaches in Pharmacology. Mol Inform 2017; 36(10)
[116]
Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 2008; 24(13): i232-40.
[117]
Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 2010; 26(12): i246-54.
[118]
van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 2011; 27(21): 3036-43.
[119]
Alaimo S, Pulvirenti A, Giugno R, Ferro A. Drug-target interaction prediction through domain-tuned network-based inference. Bioinform 2013; 29(16): 2004-8.
[120]
Chen X, Liu MX, Yan GY. Drug-target interaction prediction by random walk on the heterogeneous network. Mol Bio Sys 2012; 8(7): 1970-8.
[121]
Zhang W, Chen Y, Li D. Drug-target interaction prediction through label propagation with linear neighborhood informationMol 2017; 22(12)
[122]
Mei JP, Kwoh CK, Yang P, Li XL, Zheng J. Drug-target interaction prediction by learning from local information and neighbors. Bioinformatics 2013; 29(2): 238-45.
[123]
Shiraishi A, Niijima S, Brown JB, Nakatsui M, Okuno Y. Chemical genomics approach for GPCR-ligand interaction prediction and extraction of ligand binding determinants. J Chem Inf Model 2013; 53(6): 1253-62.
[124]
Ozturk H, Ozkirimli E, Ozgur A. A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction. BMC Bioinformatics 2016; 17: 128.
[125]
Rayhan F, Ahmed S, Shatabda S, et al. iDTI-ESBoost: identification of drug target interaction using evolutionary and structural features with boosting. Sci Rep 2017; 7(1): 17731.
[126]
Seo S, Choi J, Ahn SK, et al. Prediction of GPCR-ligand binding using machine learning algorithms. Comput Math Methods Med 2018.
[127]
Jiang J, Wang N, Chen P, Zhang J, Wang B. DrugECs: An ensemble system with feature subspaces for accurate drug-target interaction prediction. BioMed Res Int 2017; 2017: 6340316.
[128]
Sleire L, Forde HE, Netland IA, et al. Drug repurposing in cancer. Pharmacol Res 2017; 124: 74-91.
[129]
Oh DY, Olefsky JM. G protein-coupled receptors as targets for anti-diabetic therapeutics. Nat Rev Drug Discov 2016; 15(3): 161-72.
[130]
Knight ZA, Lin H, Shokat KM. Targeting the cancer kinome through polypharmacology. Nat Rev Cancer 2010; 10(2): 130-7.
[131]
Moya-Garcia A, Adeyelu T, Kruger FA, et al. Structural and functional view of polypharmacology. Sci Rep 2017; 7(1): 10102.
[132]
Bolognesi ML, Cavalli A. Multitarget drug discovery and polypharmacology. ChemMedChem 2016; 11(12): 1190-2.
[133]
Goldstein I, Lue TF, Padma-Nathan H, et al. Oral sildenafil in the treatment of erectile dysfunction. 1998. J Urol 2002; 167(2 Pt 2): 1197-203.
[134]
Mercurio A, Adriani G, Catalano A, et al. A Mini-review on thalidomide: chemistry, mechanisms of action, therapeutic potential and anti-angiogenic properties in multiple myeloma. Curr Med Chem 2017; 24(25): 2736-44.
[135]
Mulder CJ, van Asseldonk DP, de Boer NK. Drug rediscovery to prevent off-label prescription reduces health care costs: the case of tioguanine in the Netherlands. J Gastrointestin Liver Dis 2014; 23(2): 123-5.
[136]
Simsek M, Meijer B, van Bodegraven AA, de Boer NKH, Mulder CJJ. Finding hidden treasures in old drugs: the challenges and importance of licensing generics. Drug Discov Today 2018; 23(1): 17-21.
[137]
Hauser AS, Chavali S, Masuho I, et al. Pharmacogenomics of GPCR drug targetsCell 2018; 172(1-2): 41-54 e19
[138]
Allen JA, Roth BL. Strategies to discover unexpected targets for drugs active at g protein-coupled receptors. Annu Rev Pharmacol 2011; 51: 117-44.
[139]
Southan C, Sitzmann M, Muresan S. Comparing the chemical structure and protein content of chembl, drugbank, human metabolome database and the therapeutic target database. Mol Inform 2013; 32(11-12): 881-97.
[140]
Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008; 4(11): 682-90.
[141]
Papadopoulos N, Lennartsson J. The PDGF/PDGFR pathway as a drug target. Mol Aspects Med 201(62): 75-88.
[142]
Bae JS, Kim SM, Lee H. The Hippo signaling pathway provides novel anti-cancer drug targets. Oncotarget 2017; 8(9): 16084-98.
[143]
Yang K, Bai H, Ouyang Q, Lai L, Tang C. Finding multiple target optimal intervention in disease-related molecular network. Mol Syst Biol 2008; 4: 228.
[144]
Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Sci 2008; 321(5886): 263-6.
[145]
Wawer M, Peltason L, Weskamp N, Teckentrup A, Bajorath J. Structure-activity relationship anatomy by network-like similarity graphs and local structure-activity relationship indices. J Med Chem 2008; 51(19): 6075-84.
[146]
Vogt M, Stumpfe D, Maggiora GM, Bajorath J. Lessons learned from the design of chemical space networks and opportunities for new applications. J Comput Aided Mol Des 2016; 30(3): 191-208.
[147]
He Z, Zhang J, Shi XH, et al. Predicting drug-target interaction networks based on functional groups and biological features. PLoS One 2010; 5(3): e9603.
[148]
Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005; 27(8): 1226-38.
[149]
Zou Q, Zeng J, Cao L, Ji R. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 2016; 173: 346-54.
[150]
Tang H, Zhao YW, Zou P, et al. HBPred: a tool to identify growth hormone-binding proteins. Int J Biol Sci 2018; 14(8): 957-64.
[151]
Tang H, Chen W, Lin H. Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique. Mol Biosyst 2016; 12(4): 1269-75.
[152]
Chen XX, Tang H, Li WC, et al. Identification of bacterial cell wall lyases via pseudo amino acid composition. BioMed Res Int 2016; 2016: 1654623.
[153]
Yang H, Qiu WR, Liu GQ, et al. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int J Biol Sci 2018; 14(8): 883-91.
[154]
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; 34: 4196-204.
[155]
Lai HY, Chen XX, Chen W, Tang H, Lin H. Sequence-based predictive modeling to identify cancerlectins. Oncotarget 2017; 8(17): 28169-75.
[156]
Yang H, Tang H, Chen XX, et al. Identification of secretory proteins in mycobacterium tuberculosis using pseudo amino acid composition. BioMed Res Int 2016; 2016: 5413903.
[157]
Yu YY, Liu YG, Jiang Y, Li LM. Prediction of drug-target interaction based on fingerprint similarity. PMC 2017; 42(18): 3578-83.
[158]
Mousavian Z, Khakabimamaghani S, Kavousi K, Masoudi-Nejad A. Drug-target interaction prediction from PSSM based evolutionary information. J Pharmacol Toxicol Methods 2016; 78: 42-51.
[159]
Meng FR, You ZH, Chen X, Zhou Y, An JY. Prediction of drug-target interaction networks from the integration of protein sequences and drug chemical structuresMol 2017; 22(7)
[160]
Yao ZJ, Dong J, Che YJ, et al. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models. J Comput Aided Mol Des 2016; 30(5): 413-24.
[161]
Angermueller C, Parnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol 2016; 12(7): 878.
[162]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436-44.
[163]
Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2015; 33(8): 831-8.
[164]
Jo T, Hou J, Eickholt J, Cheng J. Improving protein fold recognition by deep learning networks. Sci Rep 2015; 5: 17573.
[165]
Wei L, Ding Y, Su R, Tang J, Zou Q. Prediction of human protein subcellular localization using deep learning. J Parallel Distrib Comput 2018; 117: 212-7.
[166]
Yu L, Sun X, Tian SW, Shi XY, Yan YL. Drug and nondrug classification based on deep learning with various feature selection strategies. Curr Bioinform 2018; 13(3): 253-9.
[167]
Zong NS, Kim H, Ngo V, Harismendy O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinform 2017; 33(15): 2337-44.
[168]
O’Hayre M, Degese MS, Gutkind JS. Novel insights into G protein and G protein-coupled receptor signaling in cancer. Curr Opin Cell Biol 2014; 27: 126-35.
[169]
Li QR, Wang ZM, Wewer Albrechtsen NJ, et al. Systems signatures reveal unique remission-path of type 2 diabetes following roux-en-y gastric bypass surgery. Exp Biol Med 2018; 28: 234-40.
[170]
Yee SW, Lin L, Merski M, et al. Prediction and validation of enzyme and transporter off-targets for metformin. J Pharmacokinet Pharmacodyn 2015; 42(5): 463-75.
[171]
Dubinsky MC, Vasiliauskas EA, Singh H, et al. 6-thioguanine can cause serious liver injury in inflammatory bowel disease patients. Gastroenterol 2003; 125(2): 298-303.
[172]
Rosenhouse-Dantsker A, Mehta D, Levitan I. Regulation of ion channels by membrane lipids. Compr Physiol 2012; 2(1): 31-68.
[173]
Bukiya AN, Durdagi S, Noskov S, Rosenhouse-Dantsker A. Cholesterol up-regulates neuronal G protein-gated inwardly rectifying potassium (GIRK) channel activity in the hippocampus. J Biol Chem 2017; 292(15): 6135-47.
[174]
Moller C, Netzer R. Effects of estradiol on cardiac ion channel currents. Eur J Pharmacol 2006; 532(1-2): 44-9.

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