Abstract
Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains.
| Original language | English |
|---|---|
| Journal | BMC systems biology |
| Volume | 7 Suppl 6 |
| DOIs | |
| Publication status | Published - Jan 1 2013 |
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All Science Journal Classification (ASJC) codes
- Structural Biology
- Modelling and Simulation
- Molecular Biology
- Computer Science Applications
- Applied Mathematics
Cite this
Inferring protein domains associated with drug side effects based on drug-target interaction network. / Iwata, Hiroaki; Mizutani, Sayaka; Tabei, Yasuo; Kotera, Masaaki; Goto, Susumu; Yamanishi, Yoshihiro.
In: BMC systems biology, Vol. 7 Suppl 6, 01.01.2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Inferring protein domains associated with drug side effects based on drug-target interaction network.
AU - Iwata, Hiroaki
AU - Mizutani, Sayaka
AU - Tabei, Yasuo
AU - Kotera, Masaaki
AU - Goto, Susumu
AU - Yamanishi, Yoshihiro
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains.
AB - Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains.
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U2 - 10.1186/1752-0509-7-S6-S18
DO - 10.1186/1752-0509-7-S6-S18
M3 - Article
C2 - 24565527
AN - SCOPUS:84908553078
VL - 7 Suppl 6
JO - BMC Systems Biology
JF - BMC Systems Biology
SN - 1752-0509
ER -