journal.pcbi.1006888.pdf (2.4 MB)
Predicting synthetic lethal interactions using conserved patterns in protein interaction networks
Version 2 2023-06-06, 09:57
Version 1 2023-06-06, 09:42
journal contribution
posted on 2023-06-06, 09:57 authored by Graeme Benstead-Hume, Xiangrong Chen, Suzi Hopkins, Karen A Lane, Jessica Downs, Frances PearlFrances PearlIn response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development.
Funding
Chromatin remodelling complexes in the maintenance of genome stability; G1178; CANCER RESEARCH UK; C7905/A16417
Genome Damage and Stability Centre - studentships; G1673; MRC-MEDICAL RESEARCH COUNCIL; MR/N50189X/1
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Publication status
- Published
File Version
- Published version
Journal
PLoS Computational BiologyISSN
1553-7358Publisher
Public Library of ScienceExternal DOI
Issue
4Volume
15Article number
e1006888Department affiliated with
- Biochemistry Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2019-03-28First Open Access (FOA) Date
2019-05-02First Compliant Deposit (FCD) Date
2019-03-27Usage metrics
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