Predicting synthetic lethal interactions using conserved patterns in protein interaction networks.
Abstract
In 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.
Collections
Subject
Animals
Humans
Neoplasms
Tumor Suppressor Proteins
Protein Interaction Mapping
Computational Biology
Genes, Essential
Multigene Family
Algorithms
Models, Biological
Artificial Intelligence
Drug Discovery
Synthetic Biology
Molecular Targeted Therapy
Protein Interaction Maps
Gene Ontology
Synthetic Lethal Mutations
Research team
Epigenetics and Genome Stability
Language
eng
Date accepted
2019-02-18
License start date
2019-04-17
Citation
PLoS computational biology, 2019, 15 (4), pp. e1006888 - ?
Publisher
PUBLIC LIBRARY SCIENCE