Dataset Information

Network motifs modulate druggability of cellular targets.

ABSTRACT: Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by cellular networks connected to a drug target. Here, we use computational approaches to reveal basic principles of network motifs that modulate druggability. Through quantitative analysis, we find that inhibiting self-positive feedback loop is a more robust and effective treatment strategy than inhibiting other regulations, and adding direct regulations to a drug-target generally reduces its druggability. The findings are explained through analytical solution of the motifs. Furthermore, we find that a consensus topology of highly druggable motifs consists of a negative feedback loop without any positive feedback loops, and consensus motifs with low druggability have multiple positive direct regulations and positive feedback loops. Based on the discovered principles, we predict potential genetic targets in Escherichia coli that have either high or low druggability based on their network context. Our work establishes the foundation toward identifying and predicting druggable targets based on their network topology.

SUBMITTER: Wu F

PROVIDER: S-EPMC5100546 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Network motifs modulate druggability of cellular targets.

Wu Fan F Ma Cong C Tan Cheemeng C

Scientific reports 20161108

Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by cellular networks connected to a drug target. Here, we use computational approaches to reveal basic principles of network motifs that modulate druggability. Through quantitative analysis, we find that inhibiting self-positive feedback loop is a more ...[more]

PMID: 27824147

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Project description:BackgroundTo understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date.ResultsTo identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease.ConclusionsMIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis.

Dataset Information

Network motifs modulate druggability of cellular targets.

Publications

Network motifs modulate druggability of cellular targets.

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