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.
Highlights
To fundamental understanding of network structures that modulate inhibition of a single target by drug molecules
A critical knowledge gap exists between the paradigms of one-drug-one-target and network pharmacology, in which it is unclear if unsatisfactory treatment outcomes are due to fundamental features of cellular networks that regulate the target
Can we unveil fundamental principles of cellular network structures to guide our selection of a drug target? Answers to the question will lead to general principles to predict and improve druggability of a cellular target
Summary
To fundamental understanding of network structures that modulate inhibition of a single target by drug molecules. Can we unveil fundamental principles of cellular network structures to guide our selection of a drug target? Answers to the question will lead to general principles to predict and improve druggability of a cellular target. We augment the definition of druggability by the likelihood of cellular targets that are embedded in network motifs to be inhibited by drug molecules[18,19]. We ask two specific questions: Does a network motif that regulates a cellular target affect its druggability? Can we use basic features of the network motifs to predict and improve its druggability? We studied all possible three-node motifs to simulate cellular networks, using one of the nodes to simulate a drug target. We applied the principles to the genetic network of E. coli and identified potential druggable targets
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