Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Biotechnology

Editor-in-Chief

ISSN (Print): 1389-2010
ISSN (Online): 1873-4316

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Computer-Assisted Drug Virtual Screening Based on the Natural Product Databases

Author(s): Baoyu Yang, Jing Mao, Bing Gao* and Xiuli Lu*

Volume 20, Issue 4, 2019

Page: [293 - 301] Pages: 9

DOI: 10.2174/1389201020666190328115411

Price: $65

Abstract

Background: Computer-assisted drug virtual screening models the process of drug screening through computer simulation technology, by docking small molecules in some of the databases to a certain protein target. There are many kinds of small molecules databases available for drug screening, including natural product databases.

Methods: Plants have been used as a source of medication for millennia. About 80% of drugs were either natural products or related analogues by 1990, and many natural products are biologically active and have favorable absorption, distribution, metabolization, excretion, and toxicology.

Results: In this paper, we review the natural product databases’ contributions to drug discovery based on virtual screening, focusing particularly on the introductions of plant natural products, microorganism natural product, Traditional Chinese medicine databases, as well as natural product toxicity prediction databases.

Conclusion: We highlight the applications of these databases in many fields of virtual screening, and attempt to forecast the importance of the natural product database in next-generation drug discovery.

Keywords: Drug discovery, virtual screening, natural product database, molecular docking, plant natural product, microorganism natural product, traditional chinese medicine.

« Previous Next »
Graphical Abstract

[1]
Atanasov, A.G.; Waltenberger, B.; Pferschy-Wenzig, E.M.; Linder, T.; Wawrosch, C.; Uhrin, P.; Temml, V.; Wang, L.; Schwaiger, S.; Heiss, E.H. Discovery and resupply of pharmacologically active plant-derived natural products: A review. Biotechnol. Adv., 2015, 33(8), 1582-1614.
[2]
Stratton, C.F.; Newman, D.J.; Tan, D.S. Cheminformatic comparison of approved drugs from natural product versussynthetic origins. Bioorg. Med. Chem. Lett., 2015, 25(21), 4802-4807.
[3]
Evans, B.E.; Rittle, K.E.; Bock, M.G.; Dipardo, R.M.; Freidinger, R.M.; Whitter, W.L.; Lundell, G.F.; Veber, D.F.; Anderson, P.S.; Chang, R.S.L. Methods for drug discovery: Development of potent, selective, orally effective cholecystokinin antagonists. ChemInform, 1988, 31(12), 2235.
[4]
Molinari, G. Natural Products in Drug Discovery: Present Status and Perspectives; Springer New York, 2009, pp. 13-27.
[5]
Koehn, F.E.; Carter, G.T. The evolving role of natural products in drug discovery. Nat. Rev. Drug Discov., 2005, 4(3), 206-220.
[6]
Harvey, A.L. Natural products in drug discovery. Drug Discov. Today, 2008, 13(19), 894-901.
[7]
Patridge, E.; Gareiss, P.; Kinch, M.S.; Hoyer, D. An analysis of FDA-approved drugs: Natural products and their derivatives. Drug Discov. Today, 2016, 21(2), 204-207.
[8]
Newman, D.J.; Cragg, G.M. Natural products as sources of new drugs from 1981 to 2014. J. Nat. Prod., 2016, 79(3), 629.
[9]
Harvey, A.L.; Edradaebel, R.; Quinn, R.J. The re-emergence of natural products for drug discovery in the genomics era. Nat. Rev. Drug Discov., 2015, 14(2), 111.
[10]
Li, H.J.; Yan, J.; Ping, L. Chemistry, bioactivity and geographical diversity of steroidal alkaloids from the Liliaceae family. ChemInform, 2007, 38(5), 735-752.
[11]
Chen, Y.; De, C.B.K.; Kirchmair, J. Data resources for the computer-guided discovery of bioactive natural products. J. Chem. Inf. Model., 2017, 57(9), 2099.
[12]
Rodrigues, T.; Reker, D.; Schneider, P.; Schneider, G. Counting on natural products for drug design. Nat. Chem., 2016, 8(6), 531.
[13]
Shen, J.; Xu, X.; Cheng, F.; Liu, H.; Luo, X.; Shen, J.; Chen, K.; Zhao, W.; Shen, X.; Jiang, H. Virtual screening on natural products for discovering active compounds and target information. Curr. Med. Chem., 2003, 10(21), 2327-2342.
[14]
Yang, J.; Chu, P.; Xiong, Y.H.; Wang, R.; Tang, Y.P.; Duan, J.A. Computer-aided drug design using in the modernization of traditional Chinese medicine. World Clin. Drugs, 2009, 3(1), 1-16.
[15]
Wong, Y.H.; Chiu, C.C.; Lin, C.L.; Chen, T.S.; Jheng, B.R.; Lee, Y.C.; Chen, J.; Chen, B.S. A new era for cancer target therapies: Applying systems biology and computer-aided drug design to cancer therapies. Curr. Pharm. Biotechnol., 2010, 17(14), 1246-1267.
[16]
Yue, Q.; Cao, Z.; Guan, S.; Liu, X.; Tao, L.; Wu, W.; Li, Y.; Yang, P.; Liu, X.; Guo, D. Proteomics characterization of the cytotoxicity mechanism of ganoderic acid D and computer-automated estimation of the possible drug target network. Mol. Cell. Proteomics, 2008, 7(5), 949-961.
[17]
Hatherley, R.; Brown, D.K.; Musyoka, T.M.; Penkler, D.L.; Faya, N.; Lobb, K.A.; Bishop, Ö.T. SANCDB: A South African natural compound database. J. Cheminform., 2015, 7(1), 29.
[18]
Ntie-Kang, F.; Telukunta, K.K.; Döring, K.; Simoben, C.V.; Moumbock, A.F.A.; Malange, Y.I.; Njume, L.E.; Yong, J.N.; Sippl, W.; Günther, S. In NANPDB: A web-accessible and downloadable resource for natural products from Northern African sources European Workshop on Drug Design, 2017.
[19]
Ntiekang, F.; Nwodo, J.N.; Ibezim, A.; Simoben, C.V.; Karaman, B.; Ngwa, V.F.; Sippl, W.; Adikwu, M.U.; Mbaze, L.M. Molecular modeling of potential anticancer agents from African medicinal plants. J. Chem. Inf. Model., 2014, 54(9), 2433-2450.
[20]
Banerjee, P.; Erehman, J.; Gohlke, B.O.; Wilhelm, T.; Preissner, R.; Dunkel, M. Super natural II-a database of natural products. Nucleic Acids Res., 2015, 43(D1), D935-D939.
[21]
Sterling, T.; Irwin, J.J. ZINC 15 - ligand discovery for everyone. J. Chem. Inf. Model., 2015, 55(11), 2324-2337.
[22]
PubChem database https://pubchem.ncbi.nlm.nih.gov/(Accessed April 18, 2018)
[23]
Dictionary of Natural Products http://dnp.chemnetbase.com/faces/ chemical/ChemicalSearch.xhtml (Accessed April 18, 2018).
[24]
Afendi, F.M.; Okada, T.; Yamazaki, M.; Hirai-Morita, A.; Nakamura, Y.; Nakamura, K.; Ikeda, S.; Takahashi, H.; Altaf-Ul-Amin, M.; Darusman, L.K.; Saito, K.; Kanaya, S. KNApSAcK family databases: Integrated metabolite-plant species databases for multifaceted plant research. Plant Cell Physiol., 2012, 53(2), e1.
[25]
Ohtana, Y.; Abdullah, A.A.; Altaf-Ul-Amin, M.; Huang, M.; Ono, N.; Sato, T.; Sugiura, T.; Horai, H.; Nakamura, Y.; Morita, A.; Lange, K.W.; Kibinge, N.K.; Katsuragi, T.; Shirai, T.; Kanaya, S. Clustering of 3D-structure similarity based network of secondary metabolites reveals their relationships with biological activities. Mol. Inform., 2014, 33(11-12), 790-801.
[26]
Marti, G.; Erb, M.; Boccard, J.; Glauser, G.; Doyen, G.R.; Villard, N.; Robert, C.A.; Turlings, T.C.; Rudaz, S.; Wolfender, J.L. Metabolomics reveals herbivore-induced metabolites of resistance and susceptibility in maize leaves and roots. Plant Cell Environ., 2013, 36(3), 621-639.
[27]
InterBioScreen https://www.ibscreen.com/(Accessed on: April 18, 2018)
[28]
Lazarev, V.F.; Sverchinsky, D.V.; Mikhaylova, E.R.; Semenyuk, P.I.; Komarova, E.Y.; Niskanen, S.A.; Nikotina, A.D.; Burakov, A.V.; Kartsev, V.G.; Guzhova, I.V. Sensitizing tumor cells to conventional drugs: HSP70 chaperone inhibitors, their selection and application in cancer models. Cell Death Dis., 2018, 9(2), 41.
[29]
Hozumi, I.; Inuzuka, T.; Hiraiwa, M.; Uchida, Y.; Anezaki, T.; Ishiguro, H.; Kobayashi, H.; Uda, Y.; Miyatake, T.; Tsuji, S. Changes of growth inhibitory factor after stab wounds in rat brain. Brain Res., 1995, 688(1-2), 143-148.
[30]
Yamada, M.; Hayashi, S.; Hozumi, I.; Inuzuka, T.; Tsuji, S.; Takahashi, H. Subcellular localization of growth inhibitory factor in rat brain: light and electron microscopic immunohistochemical studies. Brain Res., 1996, 735(2), 257.
[31]
Roy, S.; Kumar, A.; Baig, M.H.; Masařík, M.; Provazník, I. Virtual screening, ADMET profiling, molecular docking and dynamics approaches to search for potent selective natural molecules based inhibitors against metallothionein-III to study Alzheimer’s disease. Methods, 2015, 83, 105-110.
[32]
AntiBase http://wwwuser.gwdg.de/~hlaatsc/antibase.htm (Accessed April 18, 2018).
[33]
Jouda, J.B.; Mawabo, I.K.; Notedji, A.; Mbazoa, C.D.; Nkenfou, J.; Wandji, J.; Nkenfou, C.N. Anti-mycobacterial activity of polyketides from Penicillium sp. endophyte isolated from Garcinia nobilis against Mycobacterium smegmatis. Int. J. Mycobacteriol., 2016, 5(2), 192.
[34]
Lee, H.H.; Molla, M.N.; Cantor, C.R.; Collins, J.J. Bacterial charity work leads to population-wide resistance. Nature, 2010, 467(7311), 82-85.
[35]
Elena, C.; Marisela, T.; Jianni, X.; Sucha, S.; Johnson, D.E. Interactions between traditional Chinese medicines and Western therapeutics. Curr. Opin. Drug Discov. Devel., 2010, 13(1), 50-65.
[36]
Xue, R.; Fang, Z.; Zhang, M.; Yi, Z.; Wen, C.; Shi, T. TCMID: Traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res., 2013, 41(Database issue), D1089.
[37]
Huang, L.; Xie, D.; Yu, Y.; Liu, H.; Shi, Y.; Shi, T.; Wen, C. TCMID 2.0: A comprehensive resource for TCM. Nucleic Acids Res., 2018, 46(Database issue), D1117-D1120.
[38]
Xie, D.; Huang, L.; Zhao, G.; Yu, Y.; Gao, J.; Li, H.; Wen, C. Dissecting the underlying pharmaceutical mechanism of Chinese traditional medicine Yun-Pi-Yi-Shen-Tong-Du-Tang acting on ankylosing spondylitis through systems biology approaches. Sci. Rep., 2017, 7(1), 13436.
[39]
Tsai, T.Y.; Chang, K.W.; Chen, Y.C. iScreen: World’s first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J. Comput. Aided Mol. Des., 2011, 25(6), 525-531.
[40]
Yang, S.C.; Chang, S.S.; Chen, Y.C. Identifying HER2 inhibitors from natural products database. PLoS One, 2011, 6(12), e28793.
[41]
Graham, J.E.; Lees, S.; Marcis, F.L.; Faye, S.L.; Lorway, R.R.; Ronse, M.; Abramowitz, S.; Grietens, K.P. Prepared for the ‘unexpected’? Lessons from the 2014-2016 Ebola epidemic in West Africa on integrating emergent theory designs into outbreak response. BMJ Glob. Health, 2018, 3(4), 1-3.
[42]
Dixon, M.G.; Schafer, I.J. Ebola viral disease outbreak--West Africa, 2014. Ann. Emerg. Med., 2015, 65(1), 114-115.
[43]
Organization, W.H. Ebola data and statistics., 2014.
[44]
Karthick, V.; Nagasundaram, N.; Doss, C.G.P.; Chakraborty, C.; Siva, R.; Lu, A.; Zhang, G.; Zhu, H. Virtual screening of the inhibitors targeting at the viral protein 40 of Ebola virus. Infect. Dis. Poverty, 2016, 5(1), 12.
[45]
Kaczorowski, G.J.; Garcia, M.L. Pharmacology of voltage-gated and calcium-activated potassium channels. Curr. Opin. Chem. Biol., 1999, 3(4), 448-458.
[46]
Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem., 2015, 58(9), 4066-4072.
[47]
Bhattaram, V.A.; Graefe, U.; Kohlert, C.; Veit, M.; Derendorf, H. Pharmacokinetics and bioavailability of herbal medicinal products. Phytomedicine, 2002, 9(Suppl. 3), 1-33.
[48]
Dongyue, C.; Junmei, W.; Rui, Z.; Youyong, L.; Huidong, Y.; Tingjun, H. ADMET evaluation in drug discovery. 11. PharmacoKinetics Knowledge Base (PKKB): A comprehensive database of pharmacokinetic and toxic properties for drugs. J. Chem. Inf. Model., 2012, 52(5), 1132-1137.
[49]
Obach, R.; Lombardo, F.; Waters, N.J. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab. Dispos., 2008, 36(7), 1385-1405.
[50]
Matthews, E.J.; Kruhlak, N.L.R.; Daniel, B.; Contrera, J.F. Assessment of the health effects of chemicals in humans: I. QSAR estimation of the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data. Curr. Drug Discov. Technol., 2004, 1(1), 61-76.
[51]
Yan, A.; Liang, H.; Chong, Y.; Nie, X.; Yu, C. In-silico prediction of blood-brain barrier permeability. SAR QSAR Environ. Res., 2013, 24(1), 61-74.
[52]
Vivian, L.; Craig, K.; Yannick, D.; Tim, J.; An Chi, G.; Yifeng, L.; Adam, M.; David, A.; Michael, W.; Vanessa, N. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res., 2014, 42(Database issue), 1091-1097.
[53]
Breiman, L. Random forests, machine learning 45. J. Clin. Microbiol., 2001, 2, 199-228.
[54]
Fact SheetTOXNET®: Toxicology Data Network.
[55]
Zeng, X.; Zhang, P.; He, W.; Qin, C.; Chen, S.; Tao, L.; Wang, Y.; Tan, Y.; Gao, D.; Wang, B. NPASS: Natural product activity and species source database for natural product research, discovery and tool development. Nucleic Acids Res., 2018, 46(Database issue), D1217-D1222.
[56]
Han, V.D.W.; Eric, G. ADMET in silico modelling: Towards prediction paradise? Nat. Rev. Drug Discov., 2003, 2(3), 192-204.
[57]
Matias, M.; Fortuna, A.; Bicker, J.; Silvestre, S.; Falcão, A.; Alves, G. Screening of pharmacokinetic properties of fifty dihydropyrimidin(thi)ones derivatives using a combo of in vitro and in silico assays. Eur. J. Pharm. Sci., 2017, 109, 334-346.
[58]
Zhao, J.; Wang, G.; Del Mundo, I.M.; Mckinney, J.A.; Lu, X.; Bacolla, A.; Boulware, S.B.; Zhang, C.; Zhang, H.; Ren, P. Distinct mechanisms of nuclease-directed DNA-structure-induced genetic instability in cancer genomes. Cell Reports, 2018, 22(5), 1200-1210.
[59]
Suzuki, H.I.; Young, R.A.; Sharp, P.A. Super-enhancer-mediated RNA processing revealed by integrative microRNA network analysis. Cell, 2017, 168(6), 1000-1014.
[60]
Nucera, S.; Giustacchini, A.; Boccalatte, F.; Calabria, A.; Fanciullo, C.; Plati, T.; Ranghetti, A.; Garcia-Manteiga, J.; Cittaro, D.; Benedicenti, F. miRNA-126 orchestrates an oncogenic program in B cell precursor acute lymphoblastic Leukemia. Cancer Cell, 2016, 29(6), 905-921.
[61]
Lagardère, L.; Jolly, L.H.; Lipparini, F.; Aviat, F.; Stamm, B.; Jing, Z.F.; Harger, M.; Torabifard, H.; Cisneros, G.A.; Schnieders, M.J. Tinker-HP: A massively parallel molecular dynamics package for multiscale simulations of large complex systems with advanced point dipole polarizable force fields. Chem. Sci., 2017, 9(4), 956-972.
[62]
Zhang, L.; Ai, H.X.; Li, S.M.; Qi, M.Y.; Zhao, J.; Zhao, Q.; Liu, H.S. Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function. Oncotarget, 2017, 8(47), 83142.

Rights & Permissions Print Cite
Article Metrics
69
11
© 2024 Bentham Science Publishers | Privacy Policy