An integrated strategy for identifying new targets and inferring the mechanism of action: taking rhein as an example

Abstract

Background: Target identification is necessary for the comprehensive inference of the mechanism of action of a compound. The application of computational methods to predict the targets of bioactive compounds saves cost and time in drug research and development. Therefore, we designed an integrated strategy consisting of ligand-protein docking, network analysis, enrichment analysis, and an experimental surface plasmon resonance (SPR) method to identify and validate new targets, and then used enriched pathways to elucidate the underlying pharmacological mechanisms. Here, we used rhein, a compound with various pharmacological activities, as an example to find some of its previously unknown targets and to determine its pharmacological activity.

Results: A total of nine candidate targets were discovered, including LCK, HSP90AA1, RAB5A, EGFR, CDK2, CDK6, GSK3B, p38, and JNK. LCK was confirmed through SPR experiments, and HSP90AA1, EGFR, CDK6, p38, and JNK were validated through previous reports. Rhein network regulations are complex and interconnected. The therapeutic effect of rhein is the synergistic and comprehensive result of this vast and complex network, and the perturbation of multiple targets gives rhein its various pharmacological activities.

Conclusions: This study provided a new integrated strategy to identify new targets of bioactive compounds and reveal their molecular mechanisms of action.

Keywords: Enrichment analysis; Ligand-protein docking; Network analysis; Rhein; SPR; Target identification.

Fig. 1

The strategy of the target identification

Fig. 2

Network construction of rhein targets. a Rhein target protein–protein interaction network (PPI). b Extended rhein target PPI network (EPPI). In these networks, each node is a protein, and an edge indicates that two proteins interact with each other. Purple nodes represent known rhein targets; green nodes represent potential rhein targets; light blue nodes represent extended adjacent proteins of rhein targets

Fig. 3

The receiver-operator characteristic (ROC) curves of five topological parameters in the extended protein–protein interaction (EPPI) network

Fig. 4

The surface plasmon resonance (SPR) results of the interaction between LCK and rhein. Increased concentration of LCK protein showed a trend of increased binding with rhein; the equilibrium dissociation constant (K_D) was 1.060 × 10^− 6 M

Fig. 5

Diagrammatic sketch of the idea for network analysis and enrichment analysis. In this diagrammatic sketch, plane a represents the target protein–protein interaction (PPI) of one bioactive compound, targets of which were mapped to a biological network (plane b). In fact, the target extended PPI (EPPI) of this bioactive compound is the network with broken circle in plane b. According to the importance of nodes in the network, plane c was selected from the EPPI via network analysis. The plane d represents the enriched pathway of proteins in plane c. Thus, the potential targets of this bioactive compound in plane d could be considered to be candidate targets

Fig. 6

The integrated network of enrichment pathways of rhein targets. This pathway was constructed via manually extracting the biological process which is related to enriched targets of rhein from the KEGG pathway. The main body of a biological process was extracted if a rhein target was in this biological process. The protein marked by star is the rhein target. Purple and green stars represent known and candidate targets, respectively