In silico Methods for Identification of Potential Therapeutic Targets
Tóm tắt
At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.
Tài liệu tham khảo
Tang Y, Zhu W, Chen K, Jiang H (2006) New technologies in computer-aided drug design: toward target identification and new chemical entity discovery. Drug Discov Today Technol 3:307–313. https://doi.org/10.1016/j.ddtec.2006.09.004
Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, Pangalos MN (2014) Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13:419–431. https://doi.org/10.1038/nrd4309
Morgan P, Brown DG, Lennard S, Anderton MJ, Barrett JC, Eriksson U, Fidock M, Hamren B, Johnson A, March RE, Matcham J, Mettetal J, Nicholls DJ, Platz S, Rees S, Snowden MA, Pangalos MN (2018) Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat Rev Drug Discov 17:167–181. https://doi.org/10.1038/nrd.2017.244
Wooller SK, Benstead-Hume G, Chen X, Ali Y, Pearl FMG (2017) Bioinformatics in translational drug discovery. Biosci Rep 37:BSR20160180. https://doi.org/10.1042/BSR20160180
Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 14:177–184. https://doi.org/10.1016/j.csbj.2016.04.004
Dai YF, Zhao XM (2015) A survey on the computational approaches to identify drug targets in the postgenomic era. Biomed Res Int. https://doi.org/10.1155/2015/239654
Sekyere JO, Asante J (2018) Emerging mechanisms of antimicrobial resistance in bacteria and fungi: advances in the era of genomics. Future Microbiol 13:241–262. https://doi.org/10.2217/fmb-2017-0172
Csermely P, Korcsmaros T, Kiss HJ, London G, Nussinov R (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 138:333–408. https://doi.org/10.1016/j.pharmthera.2013.01.016
Zhang Z, Ren Q (2015) Why are essential genes essential? The essentiality of Saccharomyces genes. Microb Cell 2:280–287. https://doi.org/10.15698/mic2015.08.218
Hopkins AL (2007) Network pharmacology. Nat Biotechnol 25:1110–1111. https://doi.org/10.1038/nbt1007-1110
Barabasi AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68. https://doi.org/10.1038/nrg2918
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113. https://doi.org/10.1038/nrg1272
Xie L, Li J, Xie L, Bourne PE (2009) Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput Biol 5:e1000387. https://doi.org/10.1371/journal.pcbi.1000387
Jain B, Raj U, Varadwaj PK (2018) Drug target interplay: a network-based analysis of human diseases and the drug targets. Curr Top Med Chem 18:1053–1061. https://doi.org/10.2174/1568026618666180719160922
Chu LH, Chen BS (2008) Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets. BMC Syst Biol 2:56. https://doi.org/10.1186/1752-0509-2-56
Buysse JM (2001) The role of genomics in antibacterial target discovery. Curr Med Chem 8:1713–1726. https://doi.org/10.2174/0929867013371699
Abadio AK, Kioshima ES, Teixeira MM, Martins NF, Maigret B, Felipe MS (2011) Comparative genomics allowed the identification of drug targets against human fungal pathogens. BMC Genomics 12:75. https://doi.org/10.1186/1471-2164-12-75
Hosen MI, Tanmoy AM, Mahbuba DA, Salma U, Nazim M, Islam MT, Akhteruzzaman S (2014) Application of a subtractive genomics approach for in silico identification and characterization of novel drug targets in Mycobacterium tuberculosis F11. Interdiscip Sci 6:48–56. https://doi.org/10.1007/s12539-014-0188-y
Shanmugam A, Natarajan J (2010) Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification. Bioinformation 4:392–395. https://doi.org/10.6026/97320630004392
Dutta A, Singh SK, Ghosh P, Mukherjee R, Mitter S, Bandyopadhyay D (2006) In silico identification of potential therapeutic targets in the human pathogen Helicobacter pylori. In Silico Biol 6:43–47
Spaltmann F, Blunck M, Ziegelbauer K (1999) Computer-aided target selection—prioritizing targets for antifungal drug discovery. Drug Discov Today 4:17–26. https://doi.org/10.1016/s1359-6446(98)01278-1
Chawley P, Samal HB, Prava J, Suar M, Mahapatra RK (2014) Comparative genomics study for identification of drug and vaccine targets in Vibrio cholerae: MurA ligase as a case study. Genomics 103:83–93. https://doi.org/10.1016/j.ygeno.2013.12.002
Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38:D355-360. https://doi.org/10.1093/nar/gkp896
UniProt C (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515. https://doi.org/10.1093/nar/gky1049
Lagunin AA, Ivanov SM, Gloriozova TA, Pogodin PV, Filimonov DA, Kumar S, Goel RK (2020) Combined network pharmacology and virtual reverse pharmacology approaches for identification of potential targets to treat vascular dementia. Sci Rep 10:257. https://doi.org/10.1038/s41598-019-57199-9
Lichtblau Y, Zimmermann K, Haldemann B, Lenze D, Hummel M, Leser U (2017) Comparative assessment of differential network analysis methods. Brief Bioinform 18:837–850. https://doi.org/10.1093/bib/bbw061
Fadhal E, Mwambene EC, Gamieldien J (2014) Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery. BMC Syst Biol 8:68. https://doi.org/10.1186/1752-0509-8-68
Yang Y, Adelstein SJ, Kassis AI (2012) Target discovery from data mining approaches. Drug Discov Today 17(Suppl):S16-23. https://doi.org/10.1016/j.drudis.2011.12.006
Su G, Morris JH, Demchak B, Bader GD (2014) Biological network exploration with Cytoscape 3. Curr Protoc Bioinform 47:8 13 11-81324. https://doi.org/10.1002/0471250953.bi0813s47
Huo M, Wang Z, Wu D, Zhang Y, Qiao Y (2017) Using coexpression protein interaction network analysis to identify mechanisms of danshensu affecting patients with coronary heart disease. Int J Mol Sci 18:1298. https://doi.org/10.3390/ijms18061298
Miryala SK, Anbarasu A, Ramaiah S (2019) Systems biology studies in Pseudomonas aeruginosa PA01 to understand their role in biofilm formation and multidrug efflux pumps. Microb Pathog 136:103668. https://doi.org/10.1016/j.micpath.2019.103668
Xue J, Xie F, Xu J, Liu Y, Liang Y, Wen Z, Li M (2017) A new network-based strategy for predicting the potential miRNA-mRNA interactions in tumorigenesis. Int J Genomics 2017:3538568. https://doi.org/10.1155/2017/3538568
Farkas IJ, Korcsmaros T, Kovacs IA, Mihalik A, Palotai R, Simko GI, Szalay KZ, Szalay-Beko M, Vellai T, Wang S, Csermely P (2011) Network-based tools for the identification of novel drug targets. Sci Signal 4:pt3. https://doi.org/10.1126/scisignal.2001950
Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M (2007) The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3:e59. https://doi.org/10.1371/journal.pcbi.0030059
Zhu M, Gao L, Li X, Liu Z, Xu C, Yan Y, Walker E, Jiang W, Su B, Chen X, Lin H (2009) The analysis of the drug-targets based on the topological properties in the human protein-protein interaction network. J Drug Target 17:524–532. https://doi.org/10.1080/10611860903046610
Peng Q, Schork NJ (2014) Utility of network integrity methods in therapeutic target identification. Front Genet 5:12. https://doi.org/10.3389/fgene.2014.00012
Zaman N, Li L, Jaramillo ML, Sun Z, Tibiche C, Banville M, Collins C, Trifiro M, Paliouras M, Nantel A, O’Connor-McCourt M, Wang E (2013) Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets. Cell Rep 5:216–223. https://doi.org/10.1016/j.celrep.2013.08.028
van Dam S, Vosa U, van der Graaf A, Franke L, de Magalhaes JP (2018) Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform 19:575–592. https://doi.org/10.1093/bib/bbw139
Estrada E (2006) Protein bipartivity and essentiality in the yeast protein-protein interaction network. J Proteome Res 5:2177–2184. https://doi.org/10.1021/pr060106e
Jeong H, Mason SP, Barabasi AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411:41–42. https://doi.org/10.1038/35075138
Hwang WC, Zhang A, Ramanathan M (2008) Identification of information flow-modulating drug targets: a novel bridging paradigm for drug discovery. Clin Pharmacol Ther 84:563–572. https://doi.org/10.1038/clpt.2008.129
Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406:378–382. https://doi.org/10.1038/35019019
Boutet E, Lieberherr D, Tognolli M, Schneider M, Bansal P, Bridge AJ, Poux S, Bougueleret L, Xenarios I (2016) UniProtKB/Swiss-Prot, the manually annotated section of the UniProt KnowledgeBase: how to use the entry view. Methods Mol Biol 1374:23–54. https://doi.org/10.1007/978-1-4939-3167-5_2
Luo H, Lin Y, Gao F, Zhang CT, Zhang R (2014) DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res 42:D574-580. https://doi.org/10.1093/nar/gkt1131
Lin Y, Zhang RR (2011) Putative essential and core-essential genes in Mycoplasma genomes. Sci Rep 1:53. https://doi.org/10.1038/srep00053
Chen WH, Minguez P, Lercher MJ, Bork P (2012) OGEE: an online gene essentiality database. Nucleic Acids Res 40:D901-906. https://doi.org/10.1093/nar/gkr986
Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M (2021) KEGG: integrating viruses and cellular organisms. Nucleic Acids Res 49:D545–D551. https://doi.org/10.1093/nar/gkaa970
Tapas S, Kumar Patel G, Dhindwal S, Tomar S (2011) In Silico sequence analysis and molecular modeling of the three-dimensional structure of DAHP synthase from Pseudomonas fragi. J Mol Model 17:621–631. https://doi.org/10.1007/s00894-010-0764-y
Pieper U, Webb BM, Barkan DT, Schneidman-Duhovny D, Schlessinger A, Braberg H, Yang Z, Meng EC, Pettersen EF, Huang CC, Datta RS, Sampathkumar P, Madhusudhan MS, Sjölander K, Ferrin TE, Burley SK, Sali A (2011) ModBase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res 39:D465-474. https://doi.org/10.1093/nar/gkq1091
Pruitt KD, Tatusova T, Maglott DR (2007) NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35:D61-65. https://doi.org/10.1093/nar/gkl842
Galagan JE, Sisk P, Stolte C, Weiner B, Koehrsen M, Wymore F, Reddy TB, Zucker JD, Engels R, Gellesch M, Hubble J, Jin H, Larson L, Mao M, Nitzberg M, White J, Zachariah ZK, Sherlock G, Ball CA, Schoolnik GK (2010) TB database 2010: overview and update. Tuberculosis 90:225–235. https://doi.org/10.1016/j.tube.2010.03.010
Lee RYN, Howe KL, Harris TW, Arnaboldi V, Cain S, Chan J, Chen WJ, Davis P, Gao S, Grove C, Kishore R, Muller HM, Nakamura C, Nuin P, Paulini M, Raciti D, Rodgers F, Russell M, Schindelman G, Tuli MA, Van Auken K, Wang Q, Williams G, Wright A, Yook K, Berriman M, Kersey P, Schedl T, Stein L, Sternberg PW (2018) WormBase 2017: molting into a new stage. Nucleic Acids Res 46:D869–D874. https://doi.org/10.1093/nar/gkx998
Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y, Jin Q (2005) VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res 33:D325-328. https://doi.org/10.1093/nar/gki008
Luo H, Lin Y, Liu T, Lai FL, Zhang CT, Gao F, Zhang R (2021) DEG 15, an update of the Database of Essential Genes that includes built-in analysis tools. Nucleic Acids Res 49:D677–D686. https://doi.org/10.1093/nar/gkaa917
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering CV (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613. https://doi.org/10.1093/nar/gky1131
Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP, Santonico E, Castagnoli L, Cesareni G (2012) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40:D857-861. https://doi.org/10.1093/nar/gkr930
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2013) NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res 41:D991-995. https://doi.org/10.1093/nar/gks1193
Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, 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 (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082. https://doi.org/10.1093/nar/gkx1037
Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK, Surendranath V, Niranjan V, Muthusamy B, Gandhi TK, Gronborg M, Ibarrola N, Deshpande N, Shanker K, Shivashankar HN, Rashmi BP, Ramya MA, Zhao Z, Chandrika KN, Padma N, Harsha HC, Yatish AJ, Kavitha MP, Menezes M, Choudhury DR, Suresh S, Ghosh N, Saravana R, Chandran S, Krishna S, Joy M, Anand SK, Madavan V, Joseph A, Wong GW, Schiemann WP, Constantinescu SN, Huang L, Khosravi-Far R, Steen H, Tewari M, Ghaffari S, Blobe GC, Dang CV, Garcia JG, Pevsner J, Jensen ON, Roepstorff P, Deshpande KS, Chinnaiyan AM, Hamosh A, Chakravarti A, Pandey A (2003) Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res 13:2363–2371. https://doi.org/10.1101/gr.1680803
Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, Campbell NH, Chavali G, Chen C, del-Toro N, Duesbury M, Dumousseau M, Galeota E, Hinz U, Iannuccelli M, Jagannathan S, Jimenez R, Khadake J, Lagreid A, Licata L, Lovering RC, Meldal B, Melidoni AN, Milagros M, Peluso D, Perfetto L, Porras P, Raghunath A, Ricard-Blum S, Roechert B, Stutz A, Tognolli M, van Roey K, Cesareni G, Hermjakob H (2014) The MIntAct project–IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42:D358-363. https://doi.org/10.1093/nar/gkt1115
Oughtred R, Rust J, Chang C, Breitkreutz BJ, Stark C, Willems A, Boucher L, Leung G, Kolas N, Zhang F, Dolma S, Coulombe-Huntington J, Chatr-Aryamontri A, Dolinski K, Tyers M (2021) The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30:187–200. https://doi.org/10.1002/pro.3978
Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D (2000) DIP: the database of interacting proteins. Nucleic Acids Res 28:289–291. https://doi.org/10.1093/nar/28.1.289
Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M (2016) STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44:D380-384. https://doi.org/10.1093/nar/gkv1277
Huang HY, Lin YC, Li J, Huang KY, Shrestha S, Hong HC, Tang Y, Chen YG, Jin CN, Yu Y, Xu JT, Li YM, Cai XX, Zhou ZY, Chen XH, Pei YY, Hu L, Su JJ, Cui SD, Wang F, Xie YY, Ding SY, Luo MF, Chou CH, Chang NW, Chen KW, Cheng YH, Wan XH, Hsu WL, Lee TY, Wei FX, Huang HD (2020) miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res 48:D148–D154. https://doi.org/10.1093/nar/gkz896
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, Papadimitriou D, Kavakiotis I, Maniou S, Skoufos G, Vergoulis T, Dalamagas T, Hatzigeorgiou AG (2018) DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Res 46:D239–D245. https://doi.org/10.1093/nar/gkx1141
Carbon S, Douglass E, Good BM, Unni DR, Harris NL, Mungall CJ, Basu S, Chisholm RL, Dodson RJ, Hartline E, Fey P, Thomas PD, Albou L-P, Ebert D, Kesling MJ, Mi H, Muruganujan A, Huang X, Mushayahama T, LaBonte SA, Siegele DA, Antonazzo G, Attrill H, Brown NH, Garapati P, Marygold SJ, Trovisco V, dos Santos G, Falls K, Tabone C, Zhou P, Goodman JL, Strelets VB, Thurmond J, Garmiri P, Ishtiaq R, Rodríguez-López M, Acencio ML, Kuiper M, Lægreid A, Logie C, Lovering RC, Kramarz B, Saverimuttu SCC, Pinheiro SM, Gunn H, Su R, Thurlow KE, Chibucos M, Giglio M, Nadendla S, Munro J, Jackson R, Duesbury MJ, Del-Toro N, Meldal BHM, Paneerselvam K, Perfetto L, Porras P, Orchard S, Shrivastava A, Chang H-Y, Finn RD, Mitchell AL, Rawlings ND, Richardson L, Sangrador-Vegas A, Blake JA, Christie KR, Dolan ME, Drabkin HJ, Hill DP, Ni L, Sitnikov DM, Harris MA, Oliver SG, Rutherford K, Wood V, Hayles J, Bähler J, Bolton ER, De Pons JL, Dwinell MR, Hayman GT, Kaldunski ML, Kwitek AE, Laulederkind SJF, Plasterer C, Tutaj MA, Vedi M, Wang S-J, D’Eustachio P, Matthews L, Balhoff JP, Aleksander SA, Alexander MJ, Cherry JM, Engel SR, Gondwe F, Karra K, Miyasato SR, Nash RS, Simison M, Skrzypek MS, Weng S, Wong ED, Feuermann M, Gaudet P, Morgat A, Bakker E, Berardini TZ, Reiser L, Subramaniam S, Huala E, Arighi CN, Auchincloss A, Axelsen K, Argoud-Puy G, Bateman A, Blatter M-C, Boutet E, Bowler E, Breuza L, Bridge A, Britto R, Bye-A-Jee H, Casas CC, Coudert E, Denny P, Estreicher A, Famiglietti ML, Georghiou G, Gos A, Gruaz-Gumowski N, Hatton-Ellis E, Hulo C, Ignatchenko A, Jungo F, Laiho K, Le Mercier P, Lieberherr D, Lock A, Lussi Y, MacDougall A, Magrane M, Martin MJ, Masson P, Natale DA, Hyka-Nouspikel N, Orchard S, Pedruzzi I, Pourcel L, Poux S, Pundir S, Rivoire C, Speretta E, Sundaram S, Tyagi N, Warner K, Zaru R, Wu CH, Diehl AD, Chan JN, Grove C, Lee RYN, Muller H-M, Raciti D, Van Auken K, Sternberg PW, Berriman M, Paulini M, Howe K, Gao S, Wright A, Stein L, Howe DG, Toro S, Westerfield M, Jaiswal P, Cooper L, Elser J (2021) The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res 49:D325–D334. https://doi.org/10.1093/nar/gkaa1113
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211
Amberger JS, Bocchini CA, Scott AF, Hamosh A (2019) OMIM.org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res 47:D1038–D1043. https://doi.org/10.1093/nar/gky1151
Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen AV, Cheng AA, Liu S, Min SY, Miroshnichenko A, Tran HK, Werfalli RE, Nasir JA, Oloni M, Speicher DJ, Florescu A, Singh B, Faltyn M, Hernandez-Koutoucheva A, Sharma AN, Bordeleau E, Pawlowski AC, Zubyk HL, Dooley D, Griffiths E, Maguire F, Winsor GL, Beiko RG, Brinkman FSL, Hsiao WWL, Domselaar GV, McArthur AG (2020) CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res 48:D517–D525. https://doi.org/10.1093/nar/gkz935
Schriml LM, Mitraka E, Munro J, Tauber B, Schor M, Nickle L, Felix V, Jeng L, Bearer C, Lichenstein R, Bisordi K, Campion N, Hyman B, Kurland D, Oates CP, Kibbey S, Sreekumar P, Le C, Giglio M, Greene C (2019) Human Disease Ontology 2018 update: classification, content and workflow expansion. Nucleic Acids Res 47:D955–D962. https://doi.org/10.1093/nar/gky1032
Wang Y, Zhang S, Li F, Zhou Y, Zhang Y, Wang Z, Zhang R, Zhu J, Ren Y, Tan Y, Qin C, Li Y, Li X, Chen Y, Zhu F (2020) Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res 48:D1031–D1041. https://doi.org/10.1093/nar/gkz981
Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, Shmoish M, Nativ N, Bahir I, Doniger T, Krug H, Sirota-Madi A, Olender T, Golan Y, Stelzer G, Harel A, Lancet D (2010) GeneCards Version 3: the human gene integrator. Database (Oxford). https://doi.org/10.1093/database/baq020
Ladunga I (2002) Finding homologs to nucleotide sequences using network BLAST searches. Curr Protoc Bioinform Chapter 3:Unit 3 3. https://doi.org/10.1002/0471250953.bi0303s00
Hu G, Kurgan L (2019) Sequence similarity searching. Curr Protoc Protein Sci 95:e71. https://doi.org/10.1002/cpps.71
Manikyam HK, Joshi SK (2020) Whole genome analysis and targeted drug discovery using computational methods and high throughput screening tools for emerged novel coronavirus (2019-nCoV). J Pharm Drug Res 3:341–361
Marchler-Bauer A, Bryant SH (2004) CD-Search: protein domain annotations on the fly. Nucleic Acids Res 32:W327–W331. https://doi.org/10.1093/nar/gkh454
Geer LY, Domrachev M, Lipman DJ, Bryant SH (2002) CDART: protein homology by domain architecture. Genome Res 12:1619–1623. https://doi.org/10.1101/gr.278202
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL (2009) BLAST+: architecture and applications. BMC Bioinform 10:421. https://doi.org/10.1186/1471-2105-10-421
Du Z, Wu Q, Wang T, Chen D, Huang X, Yang W, Luo W (2020) BlastGUI: a python-based cross-platform local BLAST visualization software. Mol Inform 39:e1900120. https://doi.org/10.1002/minf.201900120
Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23:2947–2948. https://doi.org/10.1093/bioinformatics/btm404
Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Soding J, Thompson JD, Higgins DG (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539. https://doi.org/10.1038/msb.2011.75
Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797. https://doi.org/10.1093/nar/gkh340
Troshin PV, Procter JB, Barton GJ (2011) Java bioinformatics analysis web services for multiple sequence alignment–JABAWS:MSA. Bioinformatics 27:2001–2002. https://doi.org/10.1093/bioinformatics/btr304
Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35:W182-185. https://doi.org/10.1093/nar/gkm321
Huang Y, Niu B, Gao Y, Fu L, Li W (2010) CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 26:680–682. https://doi.org/10.1093/bioinformatics/btq003
Brittnacher MJ, Fong C, Hayden HS, Jacobs MA, Radey M, Rohmer L (2011) PGAT: a multistrain analysis resource for microbial genomes. Bioinformatics 27:2429–2430. https://doi.org/10.1093/bioinformatics/btr418
Zomer A, Burghout P, Bootsma H, Hermans P, van Hijum S (2012) ESSENTIALS: software for rapid analysis of high throughput transposon insertion sequencing data. PLoS ONE 7:e43012. https://doi.org/10.1371/journal.pone.0043012
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303
Bauer-Mehren A (2013) Integration of genomic information with biological networks using Cytoscape. Methods Mol Biol 1021:37–61. https://doi.org/10.1007/978-1-62703-450-0_3
Doncheva NT, Morris JH, Gorodkin J, Jensen LJ (2019) Cytoscape stringapp: network analysis and visualization of proteomics data. J Proteome Res 18:623–632. https://doi.org/10.1021/acs.jproteome.8b00702
Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:3448–3449. https://doi.org/10.1093/bioinformatics/bti551
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform 4:2. https://doi.org/10.1186/1471-2105-4-2
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(Suppl 4):S11. https://doi.org/10.1186/1752-0509-8-S4-S11
Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. ICWSM 8:361–362
Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47:W234–W241. https://doi.org/10.1093/nar/gkz240
Schaefer MH, Fontaine JF, Vinayagam A, Porras P, Wanker EE, Andrade-Navarro MA (2012) HIPPIE: integrating protein interaction networks with experiment based quality scores. PLoS ONE 7:e31826. https://doi.org/10.1371/journal.pone.0031826
Farkas IJ, Szanto-Varnagy A, Korcsmaros T (2012) Linking proteins to signaling pathways for experiment design and evaluation. PLoS ONE 7:e36202. https://doi.org/10.1371/journal.pone.0036202
Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, Kong L, Gao G, Li CY, Wei L (2011) KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res 39:W316-322. https://doi.org/10.1093/nar/gkr483
Karp PD, Billington R, Caspi R, Fulcher CA, Latendresse M, Kothari A, Keseler IM, Krummenacker M, Midford PE, Ong Q, Ong WK, Paley SM, Subhraveti P (2019) The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform 20:1085–1093. https://doi.org/10.1093/bib/bbx085
Adamcsek B, Palla G, Farkas IJ, Derenyi I, Vicsek T (2006) CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22:1021–1023. https://doi.org/10.1093/bioinformatics/btl039
Mrvar A, Batagelj V (2016) Analysis and visualization of large networks with program package Pajek. Complex Adapt Syst Model 4:6. https://doi.org/10.1186/s40294-016-0017-8
Muller J, Hemphill A (2016) Drug target identification in protozoan parasites. Expert Opin Drug Discov 11:815–824. https://doi.org/10.1080/17460441.2016.1195945
Nayak S, Pradhan D, Singh H, Reddy MS (2019) Computational screening of potential drug targets for pathogens causing bacterial pneumonia. Microb Pathog 130:271–282. https://doi.org/10.1016/j.micpath.2019.03.024
Melak T, Gakkhar S (2015) Comparative genome and network centrality analysis to identify drug targets of Mycobacterium tuberculosis H37Rv. Biomed Res Int 2015:212061. https://doi.org/10.1155/2015/212061
Doyle MA, Gasser RB, Woodcroft BJ, Hall RS, Ralph SA (2010) Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes. BMC Genom 11:222. https://doi.org/10.1186/1471-2164-11-222
Zumla A, Chan JF, Azhar EI, Hui DS, Yuen KY (2016) Coronaviruses—drug discovery and therapeutic options. Nat Rev Drug Discov 15:327–347. https://doi.org/10.1038/nrd.2015.37
Naqvi AAT, Fatima K, Mohammad T, Fatima U, Singh IK, Singh A, Atif SM, Hariprasad G, Hasan GM, Hassan MI (2020) Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: structural genomics approach. Biochim Biophys Acta Mol Basis Dis 1866:165878. https://doi.org/10.1016/j.bbadis.2020.165878
Damte D, Suh JW, Lee SJ, Yohannes SB, Hossain MA, Park SC (2013) Putative drug and vaccine target protein identification using comparative genomic analysis of KEGG annotated metabolic pathways of Mycoplasma hyopneumoniae. Genomics 102:47–56. https://doi.org/10.1016/j.ygeno.2013.04.011
Cai J, Han C, Hu T, Zhang J, Wu D, Wang F, Liu Y, Ding J, Chen K, Yue J, Shen X, Jiang H (2006) Peptide deformylase is a potential target for anti-Helicobacter pylori drugs: reverse docking, enzymatic assay, and X-ray crystallography validation. Protein Sci 15:2071–2081. https://doi.org/10.1110/ps.062238406
Kumar S, Chaudhary K, Foster JM, Novelli JF, Zhang Y, Wang S, Spiro D, Ghedin E, Carlow CK (2007) Mining predicted essential genes of Brugia malayi for nematode drug targets. PLoS ONE 2:e1189. https://doi.org/10.1371/journal.pone.0001189
Darapaneni V, Prabhaker VK, Kukol A (2009) Large-scale analysis of influenza A virus sequences reveals potential drug target sites of non-structural proteins. J Gen Virol 90:2124–2133. https://doi.org/10.1099/vir.0.011270-0
Muhammad SA, Ahmed S, Ali A, Huang H, Wu X, Yang XF, Naz A, Chen J (2014) Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach. Genomics 104:24–35. https://doi.org/10.1016/j.ygeno.2014.05.002
Birhanu BT, Lee SJ, Park NH, Song JB, Park SC (2018) In silico analysis of putative drug and vaccine targets of the metabolic pathways of Actinobacillus pleuropneumoniae using a subtractive/comparative genomics approach. J Vet Sci 19:188–199. https://doi.org/10.4142/jvs.2018.19.2.188
Swain A, Gnanasekar P, Prava J, Rajeev AC, Kesarwani P, Lahiri C, Pan A (2021) A comparative genomics approach for shortlisting broad-spectrum drug targets in nontuberculous mycobacteria. Microb Drug Resist 27:212–226. https://doi.org/10.1089/mdr.2020.0161
Bhattacharyya M, Chakrabarti S (2015) Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies. Malar J 14:70. https://doi.org/10.1186/s12936-015-0562-1
Sabetian S, Shamsir MS (2015) Identification of putative drug targets for human sperm-egg interaction defect using protein network approach. BMC Syst Biol 9:37. https://doi.org/10.1186/s12918-015-0186-7
Kumar A, Sharma D, Aggarwal ML, Chacko KM, Bhatt TK (2016) Cancer/testis antigens as molecular drug targets using network pharmacology. Tumour Biol 37:15697–15705. https://doi.org/10.1007/s13277-016-5333-2
Rai S, Bhatnagar S (2016) Hyperlipidemia, disease associations, and top 10 potential drug targets: a network view. OMICS 20:152–168. https://doi.org/10.1089/omi.2015.0172
Huang H, He Y, Li W, Wei W, Li Y, Xie R, Guo S, Wang Y, Jiang J, Chen B, Lv J, Zhang N, Chen L, He W (2016) Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network. Oncotarget 7:37906–37919. https://doi.org/10.18632/oncotarget.9353
Li CW, Su MH, Chen BS (2017) Investigation of the cross-talk mechanism in caco-2 cells during clostridium difficile infection through genetic-and-epigenetic interspecies networks: big data mining and genome-wide identification. Front Immunol 8:901. https://doi.org/10.3389/fimmu.2017.00901
Gupta MK, Behera SK, Dehury B, Mahapatra N (2017) Identification and characterization of differentially expressed genes from human microglial cell samples infected with Japanese encephalitis virus. J Vector Borne Dis 54:131–138
Vitali F, Marini S, Balli M, Grosemans H, Sampaolesi M, Lussier YA, Cusella De Angelis MG, Bellazzi R (2017) Exploring wound-healing genomic machinery with a network-based approach. Pharmaceuticals (Basel) 10:55. https://doi.org/10.3390/ph10020055
Liu W, Wang S, Zhou S, Yang F, Jiang W, Zhang Q, Wang L (2017) A systems biology approach to identify microRNAs contributing to cisplatin resistance in human ovarian cancer cells. Mol Biosyst 13:2268–2276. https://doi.org/10.1039/c7mb00362e
Panga V, Raghunathan S (2018) A cytokine protein-protein interaction network for identifying key molecules in rheumatoid arthritis. PLoS ONE 13:e0199530. https://doi.org/10.1371/journal.pone.0199530
Moon SJ, Bae JM, Park KS, Tagkopoulos I, Kim KJ (2019) Compendium of skin molecular signatures identifies key pathological features associated with fibrosis in systemic sclerosis. Ann Rheum Dis 78:817–825. https://doi.org/10.1136/annrheumdis-2018-214778
Rahman MR, Islam T, Turanli B, Zaman T, Faruquee HM, Rahman MM, Mollah MNH, Nanda RK, Arga KY, Gov E, Moni MA (2019) Network-based approach to identify molecular signatures and therapeutic agents in Alzheimer’s disease. Comput Biol Chem 78:431–439. https://doi.org/10.1016/j.compbiolchem.2018.12.011
Tan MF, Zou G, Wei Y, Liu WQ, Li HQ, Hu Q, Zhang LS, Zhou R (2021) Protein-protein interaction network and potential drug target candidates of Streptococcus suis. J Appl Microbiol 131:658–670. https://doi.org/10.1111/jam.14950
Nadeau R, Shahryari Fard S, Scheer A, Hashimoto-Roth E, Nygard D, Abramchuk I, Chung YE, Bennett SAL, Lavallee-Adam M (2020) Computational identification of human biological processes and protein sequence motifs putatively targeted by SARS-CoV-2 proteins using protein-protein interaction networks. J Proteome Res 19:4553–4566. https://doi.org/10.1021/acs.jproteome.0c00422
Martins-de-Souza D, Guest PC, Reis-de-Oliveira G, Schmitt A, Falkai P, Turck CW (2021) An overview of the human brain myelin proteome and differences associated with schizophrenia. World J Biol Psychiatry 22:271–287. https://doi.org/10.1080/15622975.2020.1789217
Farooq QUA, Shaukat Z, Aiman S, Zhou T, Li C (2020) A systems biology-driven approach to construct a comprehensive protein interaction network of influenza A virus with its host. BMC Infect Dis 20:480. https://doi.org/10.1186/s12879-020-05214-0
Yan W, Liu X, Wang Y, Han S, Wang F, Liu X, Xiao F, Hu G (2020) Identifying drug targets in pancreatic ductal adenocarcinoma through machine learning, analyzing biomolecular networks, and structural modeling. Front Pharmacol 11:534. https://doi.org/10.3389/fphar.2020.00534
Huang S, Zhang Z, Li W, Kong F, Yi P, Huang J, Mao D, Peng W, Zhang S (2020) Network pharmacology-based prediction and verification of the active ingredients and potential targets of zuojinwan for treating colorectal cancer. Drug Des Devel Ther 14:2725–2740. https://doi.org/10.2147/DDDT.S250991
Fathima S, Sinha S, Donakonda S (2021) Network analysis identifies drug targets and small molecules to modulate apoptosis resistant cancers. Cancers (Basel) 13:851. https://doi.org/10.3390/cancers13040851
Wu M, Zhao Y, Peng N, Tao Z, Chen B (2021) Identification of chemoresistance-associated microRNAs and hub genes in breast cancer using bioinformatics analysis. Invest New Drugs 39:705–712. https://doi.org/10.1007/s10637-020-01059-1
Lin TY, Chen WJ, Yang P, Li ZQ, Wei QS, Liang D, Wang HB, He W, Zhang QW (2021) Bioinformatics analysis and identification of genes and molecular pathways in steroid-induced osteonecrosis of the femoral head. J Orthop Surg Res 16:327. https://doi.org/10.1186/s13018-021-02464-9
Zeng Y, Li N, Zheng Z, Chen R, Peng M, Liu W, Zhu J, Zeng M, Cheng J, Hong C (2021) Screening of hub genes associated with pulmonary arterial hypertension by integrated bioinformatic analysis. Biomed Res Int. https://doi.org/10.1155/2021/6626094
Yadav M, Pradhan D, Singh R (2021) Integrated analysis and identification of nine-gene signature associated to oral squamous cell carcinoma pathogenesis. 3 Biotech 11:215. https://doi.org/10.1007/s13205-021-02737-4
Miryala SK, Anbarasu A, Ramaiah S (2021) Gene interaction network approach to elucidate the multidrug resistance mechanisms in the pathogenic bacterial strain Proteus mirabilis. J Cell Physiol 236:468–479. https://doi.org/10.1002/jcp.29874
Naha A, Kumar Miryala S, Debroy R, Ramaiah S, Anbarasu A (2020) Elucidating the multi-drug resistance mechanism of Enterococcus faecalis V583: a gene interaction network analysis. Gene 748:144704. https://doi.org/10.1016/j.gene.2020.144704
Debroy R, Miryala SK, Naha A, Anbarasu A, Ramaiah S (2020) Gene interaction network studies to decipher the multi-drug resistance mechanism in Salmonella enterica serovar Typhi CT18 reveal potential drug targets. Microb Pathog 142:104096. https://doi.org/10.1016/j.micpath.2020.104096
Miryala SK, Anbarasu A, Ramaiah S (2020) Role of SHV-11, a class A β-Lactamase, gene in multidrug resistance among Klebsiella pneumoniae strains and understanding its mechanism by gene network analysis. Microb Drug Resist 26:900–908. https://doi.org/10.1089/mdr.2019.0430
Liu B, Pop M (2009) ARDB–antibiotic resistance genes database. Nucleic Acids Res 37:D443-447. https://doi.org/10.1093/nar/gkn656
Tomczak K, Czerwińska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 19:A68-77. https://doi.org/10.5114/wo.2014.47136
Xie B, Ding Q, Han H, Wu D (2013) miRCancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics 29:638–644. https://doi.org/10.1093/bioinformatics/btt014
Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37:D98-104. https://doi.org/10.1093/nar/gkn714
Li Y, Qiu C, Tu J, Geng B, Yang J, Jiang T, Cui Q (2014) HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res 42:D1070-1074. https://doi.org/10.1093/nar/gkt1023
Ponten F, Jirstrom K, Uhlen M (2008) The Human Protein Atlas—a tool for pathology. J Pathol 216:387–393. https://doi.org/10.1002/path.2440
Gupta R, Verma R, Pradhan D, Jain AK, Umamaheswari A, Rai CS (2019) An in silico approach towards identification of novel drug targets in pathogenic species of Leptospira. PLoS ONE 14:e0221446. https://doi.org/10.1371/journal.pone.0221446
Ye Y, Hua Z, Huang J, Rao N, Guo F (2013) CEG: a database of essential gene clusters. BMC Genom 14:769. https://doi.org/10.1186/1471-2164-14-769
Sarangi AN, Lohani M, Aggarwal R (2015) Proteome mining for drug target identification in Listeria monocytogenes strain EGD-e and structure-based virtual screening of a candidate drug target penicillin binding protein 4. J Microbiol Methods 111:9–18. https://doi.org/10.1016/j.mimet.2015.01.011
Melak T, Gakkhar S (2015) Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network. Clin Transl Med 4:61. https://doi.org/10.1186/s40169-015-0061-6
Lohani M, Dhasmana A, Haque S, Wahid M, Jawed A, Dar SA, Mandal RK, Areeshi MY, Khan S (2017) Proteome mining for the identification and in-silico characterization of putative drug targets of multi-drug resistant Clostridium difficile strain 630. J Microbiol Methods 136:6–10. https://doi.org/10.1016/j.mimet.2017.02.008
Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, Chimusa ER (2020) Computational/in silico methods in drug target and lead prediction. Brief Bioinform 21:1663–1675. https://doi.org/10.1093/bib/bbz103
Raman K, Yeturu K, Chandra N (2008) targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2:109. https://doi.org/10.1186/1752-0509-2-109
Wu Z, Li W, Liu G, Tang Y (2018) Network-based methods for prediction of drug-target interactions. Front Pharmacol 9:1134. https://doi.org/10.3389/fphar.2018.01134
Wong YH, Lin CL, Chen TS, Chen CA, Jiang PS, Lai YH, Chu L, Li CW, Chen JJ, Chen BS (2015) Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods. BMC Med Genom 8(Suppl 4):S4. https://doi.org/10.1186/1755-8794-8-s4-s4
Coates AR, Hu Y (2007) Novel approaches to developing new antibiotics for bacterial infections. Br J Pharmacol 152:1147–1154. https://doi.org/10.1038/sj.bjp.0707432
Chung BK, Dick T, Lee DY (2013) In silico analyses for the discovery of tuberculosis drug targets. J Antimicrob Chemother 68:2701–2709. https://doi.org/10.1093/jac/dkt273
Bloomingdale P, Nguyen VA, Niu J, Mager DE (2018) Boolean network modeling in systems pharmacology. J Pharmacokinet Pharmacodyn 45:159–180. https://doi.org/10.1007/s10928-017-9567-4
Potapov AP, Goemann B, Wingender E (2008) The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks. BMC Bioinform 9:227. https://doi.org/10.1186/1471-2105-9-227
Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, Eisenberg D (1999) Detecting protein function and protein-protein interactions from genome sequences. Science 285:751–753. https://doi.org/10.1126/science.285.5428.751
Saha S, Sengupta K, Chatterjee P, Basu S, Nasipuri M (2018) Analysis of protein targets in pathogen-host interaction in infectious diseases: a case study on Plasmodium falciparum and Homo sapiens interaction network. Brief Funct Genomics 17:441–450. https://doi.org/10.1093/bfgp/elx024
Reisdorf WC, Chhugani N, Sanseau P, Agarwal P (2017) Harnessing public domain data to discover and validate therapeutic targets. Expert Opin Drug Discov 12:687–693. https://doi.org/10.1080/17460441.2017.1329296
Barh D, Tiwari S, Jain N, Ali A, Santos AR, Misra AN, Azevedo V, Kumar A (2011) In silico subtractive genomics for target identification in human bacterial pathogens. Drug Dev Res 72:162–177. https://doi.org/10.1002/ddr.20413
Penrod NM, Moore JH (2014) Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics. BMC Syst Biol 8:12. https://doi.org/10.1186/1752-0509-8-12
Vinayagam A, Gibson TE, Lee HJ, Yilmazel B, Roesel C, Hu Y, Kwon Y, Sharma A, Liu YY, Perrimon N, Barabasi AL (2016) Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proc Natl Acad Sci U S A 113:4976–4981. https://doi.org/10.1073/pnas.1603992113
Zong N, Kim H, Ngo V, Harismendy O (2017) Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinformatics 33:2337–2344. https://doi.org/10.1093/bioinformatics/btx160
Peng C, Lin Y, Luo H, Gao F (2017) A comprehensive overview of online resources to identify and predict bacterial essential genes. Front Microbiol 8:2331. https://doi.org/10.3389/fmicb.2017.02331
Li K, Du Y, Li L, Wei DQ (2020) Bioinformatics approaches for anti-cancer drug discovery. Curr Drug Targets 21:3–17. https://doi.org/10.2174/1389450120666190923162203
Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23:1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
Gao D, Chen Q, Zeng Y, Jiang M, Zhang Y (2020) Applications of machine learning in drug target discovery. Curr Drug Metab 21:790–803. https://doi.org/10.2174/1567201817999200728142023
Mayr A, Klambauer G, Unterthiner T, Steijaert M, Wegner JK, Ceulemans H, Clevert DA, Hochreiter S (2018) Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem Sci 9:5441–5451. https://doi.org/10.1039/c8sc00148k
Lee H, Grosse R, Ranganath R, Ng AY (2011) Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun ACM 54:95–103. https://doi.org/10.1145/2001269.2001295
Bengio Y (2009) Learning deep architectures for Al. Found Trends Mach Learn 2:1–127. https://doi.org/10.1561/2200000006
Sturm N, Mayr A, Le Van T, Chupakhin V, Ceulemans H, Wegner J, Golib-Dzib JF, Jeliazkova N, Vandriessche Y, Böhm S, Cima V, Martinovic J, Greene N, Vander Aa T, Ashby TJ, Hochreiter S, Engkvist O, Klambauer G, Chen H (2020) Industry-scale application and evaluation of deep learning for drug target prediction. J Cheminform 12:26. https://doi.org/10.1186/s13321-020-00428-5
Ferrero E, Dunham I, Sanseau P (2017) In silico prediction of novel therapeutic targets using gene-disease association data. J Transl Med 15:182. https://doi.org/10.1186/s12967-017-1285-6
Gao D, Morini E, Salani M, Krauson AJ, Chekuri A, Sharma N, Ragavendran A, Erdin S, Logan EM, Li W, Dakka A, Narasimhan J, Zhao X, Naryshkin N, Trotta CR, Effenberger KA, Woll MG, Gabbeta V, Karp G, Yu Y, Johnson G, Paquette WD, Cutting GR, Talkowski ME, Slaugenhaupt SA (2021) A deep learning approach to identify gene targets of a therapeutic for human splicing disorders. Nat Commun 12:3332. https://doi.org/10.1038/s41467-021-23663-2
Wang Q, Feng Y, Huang J, Wang T, Cheng G (2017) A novel framework for the identification of drug target proteins: combining stacked auto-encoders with a biased support vector machine. PLoS ONE 12:e0176486. https://doi.org/10.1371/journal.pone.0176486
Zeng X, Zhu S, Lu W, Liu Z, Huang J, Zhou Y, Fang J, Huang Y, Guo H, Li L, Trapp BD, Nussinov R, Eng C, Loscalzo J, Cheng F (2020) Target identification among known drugs by deep learning from heterogeneous networks. Chem Sci 11:1775–1797. https://doi.org/10.1039/c9sc04336e
Lee I, Keum J, Nam H (2019) DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol 15:e1007129. https://doi.org/10.1371/journal.pcbi.1007129
Mamoshina P, Vieira A, Putin E, Zhavoronkov A (2016) Applications of deep learning in biomedicine. Mol Pharm 13:1445–1454. https://doi.org/10.1021/acs.molpharmaceut.5b00982
Hu Y, Zhao T, Zhang N, Zhang Y, Cheng L (2019) A Review of recent advances and research on drug target identification methods. Curr Drug Metab 20:209–216. https://doi.org/10.2174/1389200219666180925091851