Relating drug-protein interaction network with drug side effects

Sayaka Mizutani, Edouard Pauwels, Véronique Stoven, Susumu Goto, Yoshihiro Yamanishi

Research output: Contribution to journalArticle

92 Citations (Scopus)

Abstract

Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system-wide approaches for linking different scales of drug actions; namely drugprotein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the cooccurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug-targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles.

Original languageEnglish
Article numberbts383
JournalBioinformatics
Volume28
Issue number18
DOIs
Publication statusPublished - Sep 1 2012

Fingerprint

Protein Interaction Maps
Protein Interaction Networks
Drug-Related Side Effects and Adverse Reactions
Drug Interactions
Drugs
Proteins
Protein
Pharmaceutical Preparations
Protein Binding
Molecular interactions
Gene Ontology
Ontology
Drug Delivery Systems
Genes
Scale Effect
Canonical Correlation Analysis
Development Process
Linking
Pathway

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)

Cite this

Mizutani, S., Pauwels, E., Stoven, V., Goto, S., & Yamanishi, Y. (2012). Relating drug-protein interaction network with drug side effects. Bioinformatics, 28(18), [bts383]. https://doi.org/10.1093/bioinformatics/bts383

Relating drug-protein interaction network with drug side effects. / Mizutani, Sayaka; Pauwels, Edouard; Stoven, Véronique; Goto, Susumu; Yamanishi, Yoshihiro.

In: Bioinformatics, Vol. 28, No. 18, bts383, 01.09.2012.

Research output: Contribution to journalArticle

Mizutani, S, Pauwels, E, Stoven, V, Goto, S & Yamanishi, Y 2012, 'Relating drug-protein interaction network with drug side effects', Bioinformatics, vol. 28, no. 18, bts383. https://doi.org/10.1093/bioinformatics/bts383
Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y. Relating drug-protein interaction network with drug side effects. Bioinformatics. 2012 Sep 1;28(18). bts383. https://doi.org/10.1093/bioinformatics/bts383
Mizutani, Sayaka ; Pauwels, Edouard ; Stoven, Véronique ; Goto, Susumu ; Yamanishi, Yoshihiro. / Relating drug-protein interaction network with drug side effects. In: Bioinformatics. 2012 ; Vol. 28, No. 18.
@article{b7fa1f1382af4870bedd1a548885716a,
title = "Relating drug-protein interaction network with drug side effects",
abstract = "Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system-wide approaches for linking different scales of drug actions; namely drugprotein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the cooccurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug-targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles.",
author = "Sayaka Mizutani and Edouard Pauwels and V{\'e}ronique Stoven and Susumu Goto and Yoshihiro Yamanishi",
year = "2012",
month = "9",
day = "1",
doi = "10.1093/bioinformatics/bts383",
language = "English",
volume = "28",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "18",

}

TY - JOUR

T1 - Relating drug-protein interaction network with drug side effects

AU - Mizutani, Sayaka

AU - Pauwels, Edouard

AU - Stoven, Véronique

AU - Goto, Susumu

AU - Yamanishi, Yoshihiro

PY - 2012/9/1

Y1 - 2012/9/1

N2 - Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system-wide approaches for linking different scales of drug actions; namely drugprotein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the cooccurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug-targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles.

AB - Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system-wide approaches for linking different scales of drug actions; namely drugprotein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the cooccurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug-targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles.

UR - http://www.scopus.com/inward/record.url?scp=84866460840&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866460840&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bts383

DO - 10.1093/bioinformatics/bts383

M3 - Article

C2 - 22962476

AN - SCOPUS:84866460840

VL - 28

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 18

M1 - bts383

ER -