ArticleScientific reports2016
The exploration of network motifs as potential drug targets from post-translational regulatory networks.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MarkerPredict: predicting clinically relevant predictive biomarkers with machine learning.NPJ systems biology and applications · 2025Article
- An In Silico Method for Predicting Drug Synergy Based on Multitask Learning.Interdisciplinary sciences, computational life sciences · 2021Article
- Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs.Briefings in bioinformatics · 2020Article
- Screening drug target combinations in disease-related molecular networks.BMC bioinformatics · 2019Article
- Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics.Nucleic acids research · 2018Article
- Predicting new indications of compounds with a network pharmacology approach: Liuwei Dihuang Wan as a case study.Oncotarget · 2017Article
- KinView: a visual comparative sequence analysis tool for integrated kinome research.Molecular bioSystems · 2016Article
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Authors and funding
4 authors.
Funding
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Abstract
Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.
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