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Article: Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

TitlePredicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines
Authors
Issue Date2010
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/
Citation
Bmc Bioinformatics, 2010, v. 11 How to Cite?
AbstractBackground: Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.Results: In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.Conclusions: The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs. © 2010 Li et al; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/129253
ISSN
2015 Impact Factor: 2.435
2015 SCImago Journal Rankings: 1.722
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
IBSI
HKRGC7017/07P
HKUCRGC
HKU
Hung Hing Ying Physical Research Sciences Research Grant
National Natural Science Foundation of China10971075
Guangdong Provincial Natural Science Grant9151063101000021
Funding Information:

We would like to thank all the members in bioinformatics group for discussion. We thank the anonymous referees for their very helpful comments and suggestion. This work was supported by IBSI grant (Zhou), HKRGC Grant No. 7017/07P, HKUCRGC Grants, HKU Strategy Research Theme fund on Computational Sciences, Hung Hing Ying Physical Research Sciences Research Grant, National Natural Science Foundation of China Grant No. 10971075 and Guangdong Provincial Natural Science Grant No. 9151063101000021.

References

 

DC FieldValueLanguage
dc.contributor.authorLi, Len_HK
dc.contributor.authorZhou, Xen_HK
dc.contributor.authorChing, WKen_HK
dc.contributor.authorWang, Pen_HK
dc.date.accessioned2010-12-23T08:34:13Z-
dc.date.available2010-12-23T08:34:13Z-
dc.date.issued2010en_HK
dc.identifier.citationBmc Bioinformatics, 2010, v. 11en_HK
dc.identifier.issn1471-2105en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129253-
dc.description.abstractBackground: Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.Results: In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.Conclusions: The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs. © 2010 Li et al; licensee BioMed Central Ltd.en_HK
dc.languageengen_US
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/en_HK
dc.relation.ispartofBMC Bioinformaticsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsB M C Bioinformatics. Copyright © BioMed Central Ltd.-
dc.titlePredicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell linesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1471-2105&volume=11:501&spage=&epage=&date=2010&atitle=Predicting+enzyme+targets+for+cancer+drugs+by+profiling+human+metabolic+reactions+in+NCI-60+cell+lines-
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1471-2105-11-501en_HK
dc.identifier.pmid20932284-
dc.identifier.pmcidPMC2964682-
dc.identifier.scopuseid_2-s2.0-77957556494en_HK
dc.identifier.hkuros183266en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77957556494&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume11en_HK
dc.identifier.eissn1471-2105-
dc.identifier.isiWOS:000283434700001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLi, L=36598792900en_HK
dc.identifier.scopusauthoridZhou, X=35235811100en_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridWang, P=35732553000en_HK
dc.identifier.citeulike7964285-

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