Common protein targets of significant drugs

We identified common protein targets of significant drugs, and used chemical structure similarity and drug-target relationships to prioritize candidate therapeutics.

Prioritizing drugs by shared target: Twenty-eight significant drugs share a protein target with one or more TOP drugs

We used drug-target data from DrugBank [24] and ChemBank [25] (as provided in MANTRA [26]) to construct a drug-drug interaction network on the set of CMap drugs; two drugs are linked by an edge if they share one or more protein targets (Fig.

In total, 83 of the significant drugs were present in this network (the protein targets of many drugs are still unknown), including 9 TOP drugs.

38 significant drugs share one or more protein targets with 21 TOP drug.

Significant drugs share many protein targets .

In the drug-target network for drug candidates, two drugs are connected by an edge if they have the same protein target .

Green edges indicate drug pairs that, in addition to sharing a protein target , were also found to be highly structurally similar (see Fig.

Description of the “mode-of-action” of a small chemical compound against a protein target is essential for the drug discovery process.

The increase of compound phenotypic screenings over the last years has dramatically increased the number of small molecules with non-annotated protein targets [40—42].

Because target annotation is a crucial step when developing a drug, and specifically the elucidation of the amino acids involved in the interactions is key to understand the mode of action of the compound, many methods have been developed to annotate drug protein targets .

Moreover, the network-based paradigm implemented in nAnnoLyze allows for the integration of other types of additional information such as the diseases linked to the protein targets , which may eventually allow for drug indication predictions.

The most popular structured-based methods rely on molecular docking approaches performing a virtual screening of a compound against a limited number of protein targets or of several compounds against one protein target [19—21].

To overcome the computational limitations, new structure-based methods use the so-called “com-parative docking” approaches that solely rely on structural comparisons, both of compounds and protein targets , to infer new interactions [23,24].

First, we applied functional enrichment analyses based on Gene Ontology (GO) annotation data [23] to assess the characteristics of the human proteins targeted by the nine virulence factors.

Among the highest-interact-ing host proteins targeted by the virulence factors, we found the adapter protein YWHAG (14-3-3 protein gamma) with 376 interactions.

Human proteins targeted by B. mallei proteins formed an interaction module that was primarily linked to cytoskeleton organization and focal adhesion.

3 shows that the proteins targeted in the focal adhesion pathway appeared to be coordinated for pathway activation and largely interacted with each other (yellow boxes).

We start our analysis by assessing the characteristics of the host proteins targeted by these virulence factors.