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Current Computer-Aided Drug Design

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ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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Research Article

Large-scale Prediction of Drug-Protein Interactions Based on Network Information

Author(s): Xinsheng Li, Daichuan Ma*, Yan Ren, Jiesi Luo and Yizhou Li

Volume 18, Issue 1, 2022

Published on: 15 March, 2021

Page: [64 - 72] Pages: 9

DOI: 10.2174/1573409917666210315094213

Abstract

Background: The prediction of drug-protein interaction (DPI) plays an important role in drug discovery and repositioning. Unfortunately, traditional experimental validation of DPIs is expensive and time-consuming. Therefore, it is necessary to develop in silico methods for the identification of potential DPIs.

Methods: In this work, the identification of DPIs was performed by the generated recommendation of the unexplored interaction of the drug-protein bipartite graph. Three kinds of recommenders were proposed to predict the potential DPIs.

Results: The simulation results showed that the proposed models obtained good performance in crossvalidation and independent test.

Conclusion: Our recommendation strategy based on collaborative filtering can effectively improve the DPI identification performance, especially for certain DPIs lacking chemical structure similarity or genomic sequence similarity.

Keywords: Drug discovery, recommender system, bipartite graph, drug-protein interaction, collaborative filtering, Jaccard index.

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[1]
Schilsky, R.L.; Allen, J.; Benner, J.; Sigal, E.; McClellan, M. Commentary: tackling the challenges of developing targeted therapies for cancer. Oncologist, 2010, 15(5), 484-487.
[http://dx.doi.org/10.1634/theoncologist.2010-0079] [PMID: 20489184]
[2]
Luo, Y.; Zhao, X.; Zhou, J.; Yang, J.; Zhang, Y.; Kuang, W.; Peng, J.; Chen, L.; Zeng, J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun., 2017, 8(1), 573.
[http://dx.doi.org/10.1038/s41467-017-00680-8] [PMID: 28924171]
[3]
Sadeghi, S.S.; Keyvanpour, M.R. Computational Drug Repurposing: Classification of the Research Opportunities and Challenges. Curr Comput Aided Drug Des, 2020, 16(4), 354-364.
[http://dx.doi.org/10.2174/1573409915666190613113822] [PMID: 31198115]
[4]
Hopkins, A.L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4(11), 682-690.
[http://dx.doi.org/10.1038/nchembio.118] [PMID: 18936753]
[5]
Poornima, P.; Kumar, J.D.; Zhao, Q.; Blunder, M.; Efferth, T. Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature. Pharmacol. Res., 2016, 111, 290-302.
[http://dx.doi.org/10.1016/j.phrs.2016.06.018] [PMID: 27329331]
[6]
Li, S.; Zhang, B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin. J. Nat. Med., 2013, 11(2), 110-120.
[http://dx.doi.org/10.1016/S1875-5364(13)60037-0] [PMID: 23787177]
[7]
Zhang, G. B.; Li, Q. Y.; Chen, Q. L.; Su, S. B. Network Pharmacology: A New Approach for Chinese Herbal Medicine Research. Evidence-Based Complementray and Alternative Medicine, 2013. 20May;, 621423.
[http://dx.doi.org/10.1155/2013/621423]
[8]
Chen, X.; Yan, C.C.; Zhang, X.; Zhang, X.; Dai, F.; Yin, J.; Zhang, Y. Drug-target interaction prediction: databases, web servers and computational models. Brief. Bioinform., 2016, 17(4), 696-712.
[http://dx.doi.org/10.1093/bib/bbv066] [PMID: 26283676]
[9]
Ding, Y.; Tang, J.; Guo, F. The Computational Models of Drug-target Interaction Prediction. Protein Pept. Lett., 2020, 27(5), 348-358.
[http://dx.doi.org/10.2174/0929866526666190410124110] [PMID: 30968771]
[10]
Cao, D.S.; Zhang, L.X.; Tan, G.S.; Xiang, Z.; Zeng, W.B.; Xu, Q.S.; Chen, A.F. Computational Prediction of Drug Target Interactions Using Chemical, Biological, and Network Features. Mol. Inform., 2014, 33(10), 669-681.
[http://dx.doi.org/10.1002/minf.201400009] [PMID: 27485302]
[11]
Ding, Y.; Tang, J.; Guo, F. Identification of drug-target interactions via multiple information integration. Inf. Sci., 2017, 418, 546-560.
[http://dx.doi.org/10.1016/j.ins.2017.08.045]
[12]
Wen, M.; Zhang, Z.; Niu, S.; Sha, H.; Yang, R.; Yun, Y.; Lu, H. Deep-Learning-Based Drug-Target Interaction Prediction. J. Proteome Res., 2017, 16(4), 1401-1409.
[http://dx.doi.org/10.1021/acs.jproteome.6b00618] [PMID: 28264154]
[13]
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-i240.
[http://dx.doi.org/10.1093/bioinformatics/btn162] [PMID: 18586719]
[14]
Mei, J.P.; Kwoh, C.K.; Yang, P.; Li, X.L.; Zheng, J. Drug-target interaction prediction by learning from local information and neighbors. Bioinformatics, 2013, 29(2), 238-245.
[http://dx.doi.org/10.1093/bioinformatics/bts670] [PMID: 23162055]
[15]
Luo, W.; Chan, K.C. Discovering patterns in drug-protein interactions based on their fingerprints, BMC bioinformatics; BioMed Central, 2012, p. S4.
[16]
Li, Z.; Han, P.; You, Z.H.; Li, X.; Zhang, Y.; Yu, H.; Nie, R.; Chen, X. In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Sci. Rep., 2017, 7(1), 11174.
[http://dx.doi.org/10.1038/s41598-017-10724-0] [PMID: 28894115]
[17]
Wang, Y-C.; Zhang, C-H.; Deng, N-Y.; Wang, Y. Kernel-based data fusion improves the drug-protein interaction prediction. Comput. Biol. Chem., 2011, 35(6), 353-362.
[http://dx.doi.org/10.1016/j.compbiolchem.2011.10.003] [PMID: 22099632]
[18]
Xiao, X.; Min, J-L.; Wang, P.; Chou, K-C. Predict drug-protein interaction in cellular networking. Curr. Top. Med. Chem., 2013, 13(14), 1707-1712.
[http://dx.doi.org/10.2174/15680266113139990121] [PMID: 23889048]
[19]
Xia, Z.; Wu, L.Y.; Zhou, X.; Wong, S.T. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol., 2010, 4(S2)(Suppl. 2), S6.
[http://dx.doi.org/10.1186/1752-0509-4-S2-S6] [PMID: 20840733]
[20]
Nascimento, A.C.A.; Prudêncio, R.B.C.; Costa, I.G. A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinformatics, 2016, 17(1), 46.
[http://dx.doi.org/10.1186/s12859-016-0890-3] [PMID: 26801218]
[21]
Zheng, X.; Ding, H.; Mamitsuka, H.; Zhu, S. Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, pp. 1025-1033.
[http://dx.doi.org/10.1145/2487575.2487670]
[22]
Liu, Y.; Wu, M.; Miao, C.; Zhao, P.; Li, X.L. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction. PLOS Comput. Biol., 2016, 12(2)e1004760
[http://dx.doi.org/10.1371/journal.pcbi.1004760] [PMID: 26872142]
[23]
Hao, M.; Bryant, S.H.; Wang, Y. Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Sci. Rep., 2017, 7, 40376.
[http://dx.doi.org/10.1038/srep40376] [PMID: 28079135]
[24]
Ezzat, A.; Zhao, P.; Wu, M.; Li, X.; Kwoh, C.K. Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2016, 99, 646-656.
[25]
Bolgár, B. Antal, P. VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization. BMC Bioinformatics, 2017, 18(1), 440.
[http://dx.doi.org/10.1186/s12859-017-1845-z] [PMID: 28978313]
[26]
Peng, L.; Liao, B.; Zhu, W.; Li, Z.; Li, K. Predicting Drug-Target Interactions With Multi-Information Fusion. IEEE J. Biomed. Health Inform., 2017, 21(2), 561-572.
[http://dx.doi.org/10.1109/JBHI.2015.2513200] [PMID: 26731781]
[27]
Lan, W.; Wang, J.; Li, M.; Liu, J.; Li, Y.; Wu, F.X.; Pan, Y. Predicting drug-target interaction using positive-unlabeled learning. Neurocomputing, 2016, 206(C), 50-57.
[http://dx.doi.org/10.1016/j.neucom.2016.03.080]
[28]
Kuang, Q.; Xu, X.; Li, R.; Dong, Y.; Li, Y.; Huang, Z.; Li, Y.; Li, M. An eigenvalue transformation technique for predicting drug-target interaction. Sci. Rep., 2015, 5, 13867.
[http://dx.doi.org/10.1038/srep13867] [PMID: 26350590]
[29]
Hao, M.; Wang, Y.; Bryant, S.H. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique. Anal. Chim. Acta, 2016, 909, 41-50.
[http://dx.doi.org/10.1016/j.aca.2016.01.014] [PMID: 26851083]
[30]
Newman, M.E.J. The structure and function of complex networks. SIAM Rev., 2003, 45(7), 167-256.
[http://dx.doi.org/10.1137/S003614450342480]
[31]
Barabási, A-L. Scale-free networks: a decade and beyond. Science, 2009, 325(5939), 412-413.
[http://dx.doi.org/10.1126/science.1173299] [PMID: 19628854]
[32]
Bondy, J.A.; Murty, U.S.R. Graph theory with applications. J. Oper. Res. Soc., 1977, 28(419), 237-238.
[33]
Chen, X.; Liu, M.X.; Yan, G.Y. Drug-target interaction prediction by random walk on the heterogeneous network. Mol. Biosyst., 2012, 8(7), 1970-1978.
[http://dx.doi.org/10.1039/c2mb00002d] [PMID: 22538619]
[34]
Luo, H.; Wang, J.; Li, M.; Luo, J.; Peng, X.; Wu, F.X.; Pan, Y. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics, 2016, 32(17), 2664-2671.
[http://dx.doi.org/10.1093/bioinformatics/btw228] [PMID: 27153662]
[35]
Cheng, F.; Liu, C.; Jiang, J.; Lu, W.; Li, W.; Liu, G.; Zhou, W.; Huang, J.; Tang, Y. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput. Biol., 2012, 8(5)e1002503
[http://dx.doi.org/10.1371/journal.pcbi.1002503] [PMID: 22589709]
[36]
Lü, L.; Medo, M.; Yeung, C. Recommender systems; Physics Reports A Review, 2012.
[37]
Resnick, P.; Varian, H.R. Recommender systems. Commun. ACM, 1997, 40(3), 56-58.
[http://dx.doi.org/10.1145/245108.245121]
[38]
Law, V.; Knox, C.; Djoumbou, Y.; Jewison, T.; Guo, A.C.; Liu, Y.; Maciejewski, A.; Arndt, D.; Wilson, M.; Neveu, V.; Tang, A.; Gabriel, G.; Ly, C.; Adamjee, S.; Dame, Z.T.; Han, B.; Zhou, Y.; Wishart, D.S. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res., 2014, 42(Database issue), D1091-D1097.
[http://dx.doi.org/10.1093/nar/gkt1068] [PMID: 24203711]
[39]
Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res., 2018, 46(D1), D1074-D1082.
[http://dx.doi.org/10.1093/nar/gkx1037] [PMID: 29126136]
[40]
Wishart, D. S.; Knox, C.; Guo, A. C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic acids research., 2006. 34(suppl_1), D668-D672.
[http://dx.doi.org/10.1093/nar/gkj067]
[41]
Schomburg, I.; Chang, A.; Placzek, S.; Söhngen, C.; Rother, M.; Lang, M.; Munaretto, C.; Ulas, S.; Stelzer, M.; Grote, A.; Scheer, M.; Schomburg, D. BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res., 2013, 41(Database issue), D764-D772.
[PMID: 23203881]
[42]
Hecker, N.; Ahmed, J.; von Eichborn, J.; Dunkel, M.; Macha, K.; Eckert, A.; Gilson, M.K.; Bourne, P.E.; Preissner, R. SuperTarget goes quantitative: update on drug-target interactions. Nucleic Acids Res., 2012, 40(Database issue), D1113-D1117.
[http://dx.doi.org/10.1093/nar/gkr912] [PMID: 22067455]
[43]
Kanehisa, M.; Goto, S.; Hattori, M.; Aoki-Kinoshita, K.F.; Itoh, M.; Kawashima, S.; Katayama, T.; Araki, M.; Hirakawa, M. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res., 2006, 34(Database issue), D354-D357.
[http://dx.doi.org/10.1093/nar/gkj102] [PMID: 16381885]
[44]
Csardi, G.; Nepusz, T. The igraph software package for complex network research. InterJournal. Complex Syst., 2006, 1695(5), 1-9.
[45]
Ihaka, R.; Gentleman, R.R. A Language for Data Analysis and Graphics. J. Comput. Graph. Stat., 1996, 5(3), 299-314.
[46]
Albert, R.; Barabasi, A.L. Statistical mechanics of complex networks. Nature, 2001, 410(6825), 268.
[47]
Strogatz, S.H. Exploring complex networks. Nature, 2001, 410(6825), 268-276.
[http://dx.doi.org/10.1038/35065725] [PMID: 11258382]
[48]
Hu, Y.; Koren, Y.; Volinsky, C. In Collaborative filtering for implicit feedback datasets Eighth IEEE International Conference on Data Mining, Ieee, 2008, pp. 263-272.
[http://dx.doi.org/10.1109/ICDM.2008.22]
[49]
Schafer, J.B.; Frankowski, D.; Herlocker, J.; Sen, S. Collaborative filtering recommender systems.The adaptive web; Springer, 2007, pp. 291-324.
[http://dx.doi.org/10.1007/978-3-540-72079-9_9]
[50]
Seifoddini, H.; Djassemi, M. The production data-based similarity coefficient versus Jaccard’s similarity coefficient. Comput. Ind. Eng., 1991, 21(1-4), 263-266.
[http://dx.doi.org/10.1016/0360-8352(91)90099-R]
[51]
Niwattanakul, S.; Singthongchai, J.; Naenudorn, E.; Wanapu, S. In Using of Jaccard coefficient for keywords similarity Proceedings of the international multiconference of engineers and computer scientists, 2013, pp. 380-384.
[52]
Hahsler, M. recommenderlab: A framework for developing and testing recommendation algorithms 2015.
[53]
Otasek, D.; Morris, J.H.; Bouças, J.; Pico, A.R.; Demchak, B. Cytoscape Automation: empowering workflow-based network analysis. Genome Biol., 2019, 20(1), 185.
[http://dx.doi.org/10.1186/s13059-019-1758-4] [PMID: 31477170]
[54]
Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13(11), 2498-2504.
[http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]
[55]
Kane; John, Clozapine for the Treatment-Resistant Schizophrenic. Arch. Gen. Psychiatry, 1988, 45(9), 789.
[http://dx.doi.org/10.1001/archpsyc.1988.01800330013001] [PMID: 3046553]
[56]
Kane, J.; Honigfeld, G.; Singer, J.; Meltzer, H. Clozapine for the treatment-resistant schizophrenic. A double-blind comparison with chlorpromazine. Arch. Gen. Psychiatry, 1988, 45(9), 789-796.
[http://dx.doi.org/10.1001/archpsyc.1988.01800330013001] [PMID: 3046553]
[57]
Ingelman-Sundberg, M. Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics J., 2005, 5(1), 6-13.
[http://dx.doi.org/10.1038/sj.tpj.6500285] [PMID: 15492763]
[58]
Shimada, T.; Yamazaki, H.; Mimura, M.; Inui, Y.; Guengerich, F.P. Interindividual variations in human liver cytochrome P-450 enzymes involved in the oxidation of drugs, carcinogens and toxic chemicals: studies with liver microsomes of 30 Japanese and 30 Caucasians. J. Pharmacol. Exp. Ther., 1994, 270(1), 414-423.
[PMID: 8035341]
[59]
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection; Ijcai: Montreal, Canada, 1995, pp. 1137-1145.
[60]
Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett., 2005, 27(8), 861-874.
[http://dx.doi.org/10.1016/j.patrec.2005.10.010]
[61]
Metz, C.E. Basic principles of ROC analysis. Semin. Nucl. Med., 1978, 8(4), 283-298.
[http://dx.doi.org/10.1016/S0001-2998(78)80014-2] [PMID: 112681]
[62]
Xavier, R.; Natacha, T.; Alexandre, H.; Natalia, T.; Frédérique, L.; Jean-Charles, S.; Markus, M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 2011, 12, 77.
[63]
Sing, T.; Sander, O.; Beerenwinkel, N.; Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics, 2005, 21(20), 3940-3941.
[http://dx.doi.org/10.1093/bioinformatics/bti623] [PMID: 16096348]

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