Inferring protein domains associated with drug side effects based on drug-target interaction network

Hiroaki Iwata,Yasuo Tabei,Sayaka Mizutani,Masaaki Kotera,Susumu Goto,Yoshihiro Yamanishi

doi:10.1186/1752-0509-7-s6-s18

Abstract

BackgroundMost 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.ResultsIn 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.ConclusionThe 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.

Highlights

Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, our knowledge about the molecular mechanism of the drug-target interactions is very limited
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 and in known drug-target interactions, which is made possible by using classifiers with sparse models

Summary

Introduction

Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, our knowledge about the molecular mechanism of the drug-target interactions is very limited. There is a hypothesis that drug phenotypic effects are involved in many kinds of biological features of Recently, a variety of computational methods have been developed for large-scale prediction of drug-target interactions in the context of chemogenomics or pharmacogenomics. The key idea of the chemogenomic approach is that chemically similar compounds are likely to interact with similar proteins, and the prediction is performed based on compound chemical structures and/or protein sequences [2,3,4,5,6,7]. The key idea of the pharmacogenomic approach is that phenotypically similar drugs are likely to interact with similar proteins, and the prediction is performed based on drug side effects and/or protein sequences [8,9,10]. The predictive models of most previous methods are not biologically interpretable, which makes it difficult to interpret biological features of drug-target interactions or compound-protein interactions

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Conclusion