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Current Alzheimer Research

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Research Article

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

Author(s): Yan Hu, Guangya Zhou, Chi Zhang, Mengying Zhang, Qin Chen*, Linfeng Zheng* and Bing Niu*

Volume 16, Issue 3, 2019

Page: [193 - 208] Pages: 16

DOI: 10.2174/1567205016666190103154855

Price: $65

Abstract

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics.

Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis.

Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5.

Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

Keywords: Machine learning (ML), Alzheimer's disease, BayesNet, BP neural network (BP), support vector machine (SVM), K nearest-neighbors (KNN), AdaBoost, random forest (RF), C4.5, web server.

« Previous Next »
[1]
Burns A, Iliffe S. Alzheimer’s disease. Brit Med 338: b158. (2009)
[2]
Cuello AC, Garofalo L, Maysinger D, Pioro EP, Silva ARD. Chapter 32 Injury and repair of central cholinergic neurons.Elsevier Science & Technology,. (1990)
[3]
Lewczuk P. Tau protein phosphorylated at threonine 181 in CSF as a neurochemical biomarker in Alzheimer’s disease: original data and review of the literature MN MN. J Mol Neurosci (No 1-2): 115-22. (2004)
[4]
Hardy JA, Higgins GA. Alzheimer’s disease: the amyloid cascade hypothesis. Science 256(5054): 184-5. (1992)
[5]
Dodart JC, Marr RA, Koistinaho M, Gregersen BM, Malkani S, Verma IM, et al. Gene delivery of human apolipoprotein E alters brain Abeta burden in a mouse model of Alzheimer”s disease. Proc Natl Acad Sci USA 102(4): 1211-6. (2005)
[6]
Zhao G. Excitatory amino acid toxicity and ischemic brain injuryCerebrovas Dis Foreign Med Sci. (2004)
[7]
Lane R, Feldman HH, Meyer J, He Y, Ferris SH, Nordberg A, et al. Synergistic effect of apolipoprotein E epsilon 4 and butyrylcholinesterase K-variant on progression from mild cognitive impairment to Alzheimer’s disease. Pharmacogenet Genomics 18(4): 289-98. (2008)
[8]
Zhang X, Hu H, Liu T, Yang Y, Peng Y, Cai Q, et al. Multi-armed poly(L-glutamic acid)-graft-polypropyleneinime as effective and serum resistant gene delivery vectors. Intern J Pharm 465(1-2): 444-54. (2014)
[9]
Wang L, You Z-H, Chen X, Xia S-X, Liu F, Yan X, et al. A Computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network. J Comput Biol 25(3): 361-73. (2018)
[10]
Jain S, Kotsampasakou E, Ecker GF. Comparing the performance of meta-classifiers-a case study on selected imbalanced data sets relevant for prediction of liver toxicity. J Comput Aided Mol Design 32(5): 583-90. (2018)
[11]
Negin F, Rodriguez P, Koperski M, Kerboua A, Gonzalez J, Bourgeois J, et al. PRAXIS: towards automatic cognitive assessment using gesture recognition. Exp Sys Applic 106: 21-35. (2018)
[12]
Kim KW, Choi JD, Lee H, Lee NK, Park S, Chin J, et al. Social event memory test (semt): A video-based memory test for predicting amyloid positivity for Alzheimer’s disease. Sci Rep 8(1): 10421-21. (2018)
[13]
Cao P, Liu X, Yang J, Zhao D, Huang M, Zaiane O. l(2,1) - l(1) regularized nonlinear multi-task representation learning based cognitive performance prediction of Alzheimer’s disease. Patt Recog 79: 195-5. (2018)
[14]
Niu B, Zhao M, Su Q, Zhang M, Lv W, Chen Q, et al. 2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors. Mol Diver 21(2): 413-26. (2017).
[15]
Deb PK, Sharma A, Piplani P, Akkinepally RR. Molecular docking and receptor-specific 3D-QSAR studies of acetylcholinesterase inhibitors. Mol Diver (No.4): 803-23. (2012)
[16]
Rizzo S, Bartolini M, Ceccarini L, Piazzi L, Gobbi S, Cavalli A. 2 Targeting Alzheimer’s disease: novel indanone hybrids bearing a pharmacophoric fragment of AP2238. Bioorganic Med Chem 18(5): 1749-60. (2010)
[17]
Wang L, Esteban G, Ojima M, Bautista-Aguilera O, Inokuchi T, Moraleda I, et al. Donepezil plus propargylamine+8-hydroxyquinoline hybrids as new multifunctional metal-chelators, ChE and MAO inhibitors for the potential treatment of Alzheimer’s disease. Eur J Med Chem 543-61.
[18]
Zhan Z-J, Bian H-L, Wang J-W, Shan W-G. Synthesis of physostigmine analogues and evaluation of their anticholinesterase activities. Bioorgan Med Chem Lett 20(5): 1532-4. (2010)
[19]
Liua J, Chenb W, Xub Y, Rena S, Zhanga W, Lia Y. Design, synthesis and biological evaluation of tasiamide B derivatives as BACE1 inhibitors. Bioorg Med Chem 23(9): 1963-74. (2015)
[20]
Liu J-K, Gu W, Cheng X-R, Cheng J-P, Nie A-H, Zhou W-X. Design and synthesis of 5-cyclopropyl substituted cyclic acylguanidine compounds as BACE1 inhibitors. Chinese Chem Lett (No 10): 1626-9. (2016)
[21]
Ciordia M, Pérez-Benito L, Delgado F, Trabanco AA, Tresadern G. The application of free energy perturbation for the design of bace1 inhibitors. J Chem Inf Model 59(9): 1856-71. (2016)
[22]
Iserloh U, Pan J, Stamford AW, Kennedy ME, Zhang Q, Zhang L, et al. Discovery of an orally efficaceous 4-phenoxypyrrolidine-based BACE-1 inhibitor. Bioorg Med Chem Lett 18(1): 418-22. (2008)
[23]
Coburn CA, Stachel SJ, Jones KG, Steele TG, Rush DM, DiMuzio J, et al. BACE-1 inhibition by a series of psi[CH2NH] reduced amide isosteres. Bioorg Med Chem Lett 16(14): 3635-8. (2006)
[24]
Pietrak BL, Crouthamel M-C, Tugusheva K, Lineberger JE, Xu M, DiMuzio JM, et al. Biochemical and cell-based assays for characterization of BACE-1 inhibitors. Anal Biochem 342(1): 144-51. (2005)
[25]
Garino C, Pietrancosta N, Laras Y, Moret V, Rolland A, Quéléver G, et al. BACE-1 inhibitory activities of new substituted phenyl-piperazine coupled to various heterocycles: Chromene, coumarin and quinoline. Bioorgan Med Chem Lett 16(7): 1995-9. (2006)
[26]
Jeon SY, Kwon SH, Seong YH, Bae K, Hur JM, Lee YY, et al. Beta-secretase (BACE1)-inhibiting stilbenoids from Smilax Rhizoma. Phytomedicine 14(6): 403-8. (2007)
[27]
Piazzi L, Cavalli A, Colizzi F, Belluti F, Bartolini M, Mancini F, et al. Multi-target-directed coumarin derivatives: hAChE and BACE1 inhibitors as potential anti-Alzheimer compounds. Bioorganic Med Chem Lett (No.1): 423-26. (2008)
[28]
Pisani L, Farina R, Nicolotti O, Gadaleta D, Soto-Otero R, Catto M, et al. In silico design of novel 2H-chromen-2-one derivatives as potent and selective MAO-B inhibitors. Eur J Med Chem 89: 98-105. (2015)
[29]
Jayaprakash V, Sinha BN, Ucar G, Ercan A. Pyrazoline-based mycobactin analogues as MAO-inhibitors. Bioorgan Med Chem Lett 18(24): 6362-8. (2008)
[30]
Skrzypek A, Matysiak J, Niewiadomy A, Bajda M, Szymański P. Synthesis and biological evaluation of 1,3,4-thiadiazole analogues as novel AChE and BuChE inhibitors. Eur J Med Chem 62: 311-9. (2013)
[31]
Yerdelen KO, Tosun E. Synthesis, docking and biological evaluation of oxamide and fumaramide analogs as potential AChE and BuChE inhibitors. Med Chem Res 24(2): 588-602. (2015)
[32]
Rosini M, Melchiorre C, Simoni E, Bartolini M, Cavalli A, Ceccarini L, et al. Inhibition of acetylcholinesterase, beta-amyloid aggregation, and NMDA receptors in Alzheimer’s disease: a promising direction for the multi-target-directed ligands gold rush. J Med Chem 51(15): 4381-4. (2008)
[33]
Bulic B, Pickhardt M, Khlistunova I, Biernat J, Mandelkow EM, Mandelkow E, et al. Rhodanine-based tau aggregation inhibitors in cell models of tauopathy. Angew Chem Int Ed Engl 46(48): 9215-9. (2007)
[34]
Crowe A, Ballatore C, Hyde E, Trojanowski JQ, Lee VM-Y. High throughput screening for small molecule inhibitors of heparin-induced tau fibril formation. Biochem Biophys Res Commun (No 1): 1-6. (2007)
[35]
Crowe A, Huang W. BaIlatore C, Johnson RL, Hogan A-ML, Huang R, et al. Identification of aminothienopyridazine inhibitors of tau assembly by quantitative high-throughput screening. Biochemistry 48(32): 7732-45. (2009)
[36]
Pickhardt M, Larbig G, Khlistunova I, Coksezen A, Meyer B, Mandelkow E-M, et al. Phenylthiazolyl-hydrazide and its derivatives are potent inhibitors of aggregation and toxicity in vitro and in cells. Biochemistry 46(35): 10016. (2007)
[37]
Taniguchi S, Suzuki N, Masuda M, Hisanaga S-I, Iwatsubo T, Goedert M, et al. Inhibition of heparin-induced tau filament formation by phenothiazines, polyphenols, and porphyrins. J Biol Chem 280(9): 7614-23. (2005)
[38]
Wischik CM, Edwards PC. Selective inhibition of Alzheimer disease-like tau aggregation by phenothiazines. Proc Natl Acad Sci USA 93(20): 11213-3. (1996)
[39]
Consonni V, Todeschini R. In: Recent advances in QSAR studies: methods and applications. Puzyn T; Leszczynski J; Cronin MT, Eds.; Springer Netherlands: Dordrecht, 2010; pp. 29-102.
[40]
Hall MA. Proc Fourth International Conference on Neural Information Processing and Intelligent Information Systems. Dunedin, New Zealand. (1997)
[41]
Kira K, Rendell LA. In: International Conference on Machine Learning. July, 1992; pp. 249-56.
[42]
Kononenko I. European Conference on Machine Learning on Machine Learning 1994; 171-82.
[43]
Robnik-Sikonja M. Kononenko I. In: Fourteenth International Conference on Machine Learning. 1997; pp. 296-304.
[44]
Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 46(1-3): 389-422. (2002)
[45]
Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell 97(1-2): 273-4. (1997)
[46]
Ben-GalIrad Bayesian Networks Encyclopedia of Statistics in Quality and Reliability. (2007)
[47]
Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Lect Notes Comput Sci 8150: 246-53. (2013)
[48]
Zhang PB, Yang ZX. A novel adaboost framework with robust threshold and structural optimization. IEEE Trans Cybern 48(1): 64-76. (2018)
[49]
Schapire RE. The boosting approach to machine learning: An overview Springer: New York,. (2003)
[50]
Cortes C, Vapnik VN. support vector networks. Mach Learn 3: 273-97. (1995)
[51]
Vapnik VN. An overview of statistical learning theory. IEEE Trans Neural Networks 10(5): 988-99. (1999)
[52]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 20(3): 273-97. (1995)
[53]
Altman NS. An introduction to Kernel and nearest-neighbor nonparametric regression. Am Statis 46(3): 175. (1992)
[54]
C4.5: Programs for Machine Learning,. (1992)
[55]
Zhang M, Su Q, Lu Y, Zhao M, Niu B. Application of machine learning approaches for protein-protein interactions prediction. Med Chem 13(6): 506-14. (2017)
[56]
Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, et al. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626: 1121-35. (2018)
[57]
Farahani FV, Ahmadi A, Zarandi MHF. Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning. Mathem Comput Simul 149: 48-68. (2018)
[58]
Obenberger J, Roth J. Selegiline in the treatment of Alzheimer’s disease. Casopis lekaru ceskych 134(12): 388-90. (1995)

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