Systematic identification of proteins that elicit drug side effects
Abstract Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large‐scale analysis to systematically predict and characterize protein...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2013-04-01
|
| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/msb.2013.10 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849331420358508544 |
|---|---|
| author | Michael Kuhn Mumna Al Banchaabouchi Monica Campillos Lars Juhl Jensen Cornelius Gross Anne‐Claude Gavin Peer Bork |
| author_facet | Michael Kuhn Mumna Al Banchaabouchi Monica Campillos Lars Juhl Jensen Cornelius Gross Anne‐Claude Gavin Peer Bork |
| author_sort | Michael Kuhn |
| collection | DOAJ |
| description | Abstract Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large‐scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug–target relations to identify overrepresented protein–side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations. |
| format | Article |
| id | doaj-art-d6c2ffd1a87a40f1b2e29e08094192a3 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2013-04-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-d6c2ffd1a87a40f1b2e29e08094192a32025-08-20T03:46:37ZengSpringer NatureMolecular Systems Biology1744-42922013-04-01911910.1038/msb.2013.10Systematic identification of proteins that elicit drug side effectsMichael Kuhn0Mumna Al Banchaabouchi1Monica Campillos2Lars Juhl Jensen3Cornelius Gross4Anne‐Claude Gavin5Peer Bork6Structural and Computational Biology Unit, European Molecular Biology LaboratoryMouse Biology Unit, European Molecular Biology LaboratoryStructural and Computational Biology Unit, European Molecular Biology LaboratoryStructural and Computational Biology Unit, European Molecular Biology LaboratoryMouse Biology Unit, European Molecular Biology LaboratoryStructural and Computational Biology Unit, European Molecular Biology LaboratoryStructural and Computational Biology Unit, European Molecular Biology LaboratoryAbstract Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large‐scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug–target relations to identify overrepresented protein–side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.https://doi.org/10.1038/msb.2013.10computational biologydrug targetsside effects |
| spellingShingle | Michael Kuhn Mumna Al Banchaabouchi Monica Campillos Lars Juhl Jensen Cornelius Gross Anne‐Claude Gavin Peer Bork Systematic identification of proteins that elicit drug side effects Molecular Systems Biology computational biology drug targets side effects |
| title | Systematic identification of proteins that elicit drug side effects |
| title_full | Systematic identification of proteins that elicit drug side effects |
| title_fullStr | Systematic identification of proteins that elicit drug side effects |
| title_full_unstemmed | Systematic identification of proteins that elicit drug side effects |
| title_short | Systematic identification of proteins that elicit drug side effects |
| title_sort | systematic identification of proteins that elicit drug side effects |
| topic | computational biology drug targets side effects |
| url | https://doi.org/10.1038/msb.2013.10 |
| work_keys_str_mv | AT michaelkuhn systematicidentificationofproteinsthatelicitdrugsideeffects AT mumnaalbanchaabouchi systematicidentificationofproteinsthatelicitdrugsideeffects AT monicacampillos systematicidentificationofproteinsthatelicitdrugsideeffects AT larsjuhljensen systematicidentificationofproteinsthatelicitdrugsideeffects AT corneliusgross systematicidentificationofproteinsthatelicitdrugsideeffects AT anneclaudegavin systematicidentificationofproteinsthatelicitdrugsideeffects AT peerbork systematicidentificationofproteinsthatelicitdrugsideeffects |