In silico approach in reveal traditional medicine plants pharmacological material basis
Tóm tắt
Từ khóa
Tài liệu tham khảo
Schippmann U, Cunningham AB, Leaman DJ. Impact of cultivation and gathering of medicinal plants on biodiversity: global trends and issues. Rome: FAO; 2002. p. 142–67.
Koutsoukas A, et al. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteom. 2011;74(12):2554–74.
Zhang X, et al. Danshen-Chuanxiong-Honghua Ameliorates cerebral impairment and improves spatial cognitive deficits after transient focal ischemia and identification of active compounds. Front Pharmacol. 2017;8:452.
Yi F, et al. In silico approach for anti-thrombosis drug discovery: P2Y1R structure-based TCMs screening. Front Pharmacol. 2016;7:531.
Yi F, et al. In silico profiling for secondary metabolites from Lepidium meyenii (maca) by the pharmacophore and ligand-shape-based joint approach. Chin Med. 2016;11(1):42.
Tu Y. The discovery of artemisinin (qinghaosu) and gifts from Chinese medicine. Nat Med. 2011;17(10):1217–20.
Zaman MA, Oparil S, Calhoun DA. Drugs targeting the renin-angiotensin-aldosterone system. Nat Rev Drug Discov. 2002;1(8):621–36.
Ghosh AK, Gemma S. Structure-based design of drugs and other bioactive molecules. Hoboken: John Wiley & Sons; 2015. p. 397–409.
Rubio-Perez C, et al. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell. 2015;27(3):382–96.
Zhang Y, et al. Pathway of PPAR-gamma coactivators in thermogenesis: a pivotal traditional Chinese medicine-associated target for individualized treatment of rheumatoid arthritis. Oncotarget. 2016;7(13):15885–900.
Ehrman TM, Barlow† DJ, Hylands‡ PJ. Phytochemical informatics of Traditional Chinese medicine and therapeutic relevance. J Chem Inf Model. 2007;47(6):2316–34.
Liu C, et al. Uncovering pharmacological mechanisms of Wu-tou decoction acting on rheumatoid arthritis through systems approaches: drug-target prediction, network analysis and experimental validation. Scientific Rep. 2015;5:9463.
Gao B, et al. Platelet P2Y12 receptors are involved in the haemostatic effect of notoginsenoside Ft1, a saponin isolated from Panax notoginseng. Br J Pharmacol. 2014;171(1):214.
Ji W, et al. Water-compatible molecularly imprinted polymers for selective solid phase extraction of dencichine from the aqueous extract of Panax notoginseng. J Chromatogr B. 2016;1008:225.
Esparza E, et al. Bioactive maca (Lepidium meyenii) alkamides are a result of traditional Andean postharvest drying practices. Phytochemistry. 2015;116:138–48.
Li Z, et al. Antioxidant and anti-inflammatory activities of berberine in the treatment of diabetes mellitus. Evid Based Complement Altern Med eCAM. 2014;2014(33):289264.
Liu H-K. Artemisinin, GABA signaling and cell reprogramming: when an old drug meets modern medicine. Sci Bull. 2017;62(6):386–7.
Boonen J, et al. Alkamid database: chemistry, occurrence and functionality of plant N-alkylamides. J Ethnopharmacol. 2012;142(3):563–90.
Umashankar V, Nandhitha S, Kalabharath P. InPACdb—Indian plant anticancer compounds database. Bioinformation. 2009;4(2):71–4.
Kim S-K, et al. TM-MC: a database of medicinal materials and chemical compounds in Northeast Asian traditional medicine. BMC Complement Altern Med. 2015;15(1):218.
Fang X, et al. CHMIS-C: a comprehensive herbal medicine information system for cancer. J Med Chem. 2005;48(5):1481–8.
Chen L, et al. Identification of compound–protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds. Mol Genet Genomics. 2016;291(6):2065–79.
Loub WD, et al. NAPRALERT: computer handling of natural product research data. J Chem Inf Comput Sci. 1985;25(2):99–103.
Ihlenfeldt WD, et al. Enhanced CACTVS browser of the open NCI database. J Chem Inf Comput Sci. 2002;42(1):46.
Chen CY-C. TCM Database@ Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS ONE. 2011;6(1):e15939.
Xue R, et al. TCMID: Traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res. 2013;41(Database issue):D1089.
Ru J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6(1):13.
Luo M, Reid T-E, Simon Wang X. Discovery of natural product-derived 5-HT1A receptor binders by cheminfomatics modeling of known binders, high throughput screening and experimental validation. Comb Chem High Throughput Screen. 2015;18(7):685–92.
Yi Y-D, Chang I-M. An overview of traditional Chinese herbal formulae and a proposal of a new code system for expressing the formula titles. Evid Based Complement Altern Med. 2004;1(2):125–32.
Irwin JJ, et al. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model. 2012;52(7):1757–68.
Kerns EH, Li D. Drug-like properties: concepts, structure design and methods. Oxford: Elsevier LTD; 2008. p. 125–6.
Lipinski CA, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings1. Adv Drug Deliv Rev. 2001;46(1–3):3–26.
Veber DF, et al. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615–23.
Eldehna WM, et al. Synthesis and cytotoxic activity of biphenylurea derivatives containing indolin-2-one moieties. Molecules. 2016;21(6):762.
Van De Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nature reviews. Drug Discov. 2003;2(3):192.
Dhiman V, et al. Characterization of stress degradation products of amodiaquine dihydrochloride by liquid chromatography with high-resolution mass spectrometry and prediction of their properties by using ADMET predictor. J Sep Sci. 2017;40(23):4530–40.
Willmann S, Lippert J, Schmitt W. From physicochemistry to absorption and distribution: predictive mechanistic modelling and computational tools. Expert Opin Drug Metab Toxicol. 2005;1(1):159–68.
Morris GM, Limwilby M. Molecular docking. New York: Humana Press; 2008. p. 365–82.
Najmanovich RJ, et al. Analysis of binding site similarity, small-molecule similarity and experimental binding profiles in the human cytosolic sulfotransferase family. Bioinformatics. 2007;23(2):e104.
Ewing TJA, et al. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des. 2001;15(5):411–28.
Yang H, et al. X-ray crystallographic structure of a teixobactin analogue reveals key interactions of the teixobactin pharmacophore. Chem Commun. 2017;53(18):2772–5.
Chen YC, 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.
Cosconati S, et al. Virtual screening with AutoDock: theory and practice. Expert Opin Drug Discov. 2010;5(6):597–607.
Allen WJ, et al. DOCK 6: impact of new features and current docking performance. J Comput Chem. 2015;36(15):1132–56.
Hart TN, Ness SR, Read RJ. Critical evaluation of the research docking program for the CASP2 challenge. Proteins. 1997;29(Suppl 1):205–9.
Sullivan DC, Martin EJ. Exploiting structure-activity relationships in docking. J Chem Inf Model. 2008;48(4):817–30.
Zsoldos Z, et al. eHiTS: a new fast, exhaustive flexible ligand docking system. J Mol Graph Model. 2007;26(1):198–212.
Rarey M, et al. A fast flexible docking method using an incremental construction algorithm. J Mol Biol. 1996;261(3):470–89.
Friesner RA, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004;47(7):1739–49.
Jones G, et al. Development and validation of a genetic algorithm for flexible docking. J Mol Biol. 1997;267(3):727–48.
Hsin KY, et al. systemsDock: a web server for network pharmacology-based prediction and analysis. Nucleic Acids Res. 2016;44(W1):W507–13.
Pierce BG, et al. ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics. 2014;30(12):1771–3.
Arockia Babu M, et al. Development of 3D-QSAR models for 5-lipoxygenase antagonists: chalcones. Bioorg Med Chem. 2002;10(12):4035–41.
Liu GY, et al. 3D-QSAR studies of insecticidal anthranilic diamides as ryanodine receptor activators using CoMFA, CoMSIA and DISCOtech. Chemosphere. 2010;78(3):300–6.
Vora J, et al. Molecular docking, QSAR and ADMET based mining of natural compounds against prime targets of HIV. J Biomol Struct Dyn. 2018. https://doi.org/10.1080/07391102.2017.1420489 .
Patel Y, et al. A comparison of the pharmacophore identification programs: catalyst, DISCO and GASP. J Comput Aided Mol Des. 2002;16(8–9):653–81.
Irwin JJ, et al. Predicted biological activity of purchasable chemical space. J Chem Inf Model. 2017. https://doi.org/10.1021/acs.jcim.7b00316 .
Engels MF, et al. CerBeruS: a system supporting the sequential screening process. J Chem Inf Comput Sci. 2000;40(2):241–5.
Lemmen C, Lengauer T, Klebe G. FLEXS: a method for fast flexible ligand superposition. J Med Chem. 1998;41(23):4502–20.
Tervo AJ, et al. BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. 1. Alignment and virtual screening applications. J Med Chem. 2005;48(12):4076–86.
Yan X, et al. Enhancing molecular shape comparison by weighted Gaussian functions. J Chem Inf Model. 2013;53(8):1967–78.
Wermuth CG. Pharmacophores: historical perspective and viewpoint from a medicinal chemist. Methods Princ Med Chem. 2006;32:3.
Zuo Z, MacMillan DW. Decarboxylative arylation of α-amino acids via photoredox catalysis: a one-step conversion of biomass to drug pharmacophore. J Am Chem Soc. 2014;136(14):5257–60.
Cereto-Massagué A, et al. Molecular fingerprint similarity search in virtual screening. Methods. 2015;71:58–63.
Ferreira LG, et al. Molecular docking and structure-based drug design strategies. Molecules. 2015;20(7):13384–421.
Consortium, U. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2017;45(D1):D158–69.
Liu T, et al. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 2007;35(Database issue):D198–201.
Chatr-aryamontri A, et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 2017;45(D1):D369–79.
Dennis G, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4(9):R60.
Wishart DS, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34(suppl_1):D668–72.
Mostafavi S, et al. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 2008;9(1):S4.
Keshava Prasad TS, et al. Human protein reference database-2009 update. Nucleic Acids Res. 2009;37(Database issue):D767–72.
Orchard S, et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014;42(D1):D358–63.
Kanehisa M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353–61.
Deng L, et al. MTA1 modulated by miR-30e contributes to epithelial-to-mesenchymal transition in hepatocellular carcinoma through an ErbB2-dependent pathway. Oncogene. 2017;36(28):3976–85.
Licata L, et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2012;40(1):D857–61.
Wang X, et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017. https://doi.org/10.1093/nar/gkx374 .
Gao Z, et al. PDTD: a web-accessible protein database for drug target identification. BMC Bioinform. 2008;9:104.
Goodsell DS. The protein data bank, in atomic evidence. Berlin: Springer; 2016. p. 1–4.
Croft D, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2014;42(Database issue):D472.
Kuhn M, et al. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 2007;36(suppl_1):D684–8.
Szklarczyk D, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2014;43(D1):D447–52.
Zhu F, et al. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res. 2011;40(D1):D1128–36.
Li S. Mapping ancient remedies: applying a network approach to traditional Chinese medicine. Science. 2015;350(6262):S72–4.
Krämer A, et al. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics. 2013;30(4):523–30.
Du J, et al. KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model. Mol BioSyst. 2014;10(9):2441–7.
Ekins S, et al. Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms. Xenobiotica. 2006;36(10–11):877–901.
Kurata H, et al. Extended CADLIVE: a novel graphical notation for design of biochemical network maps and computational pathway analysis. Nucleic Acids Res. 2007;35(20):e134–e134.
Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.
De Nooy W, Mrvar A, Batagelj V. Exploratory social network analysis with Pajek, vol. 27. Cambridge: Cambridge University Press; 2011.
Junker BH, Klukas C, Schreiber F. VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinform. 2006;7(1):109.
Hu Z, et al. VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res. 2005;33(suppl_2):W352–7.
Schwarz R, et al. Integrated network reconstruction, visualization and analysis using YANAsquare. BMC Bioinform. 2007;8(1):313.