Data mining and structural analysis for multi-tissue regeneration potential of BMP-4 and activator drugs

Abstract

Despite substantial progress in surgery, managing multi-tissue injuries is strenuous to accomplish and requires a multi-staged serial treatment of individual tissues. Stimulated regeneration affects the complete structural and functional repair of both hard and soft tissues post-injury and thus serves as an attractive therapeutic option to target multi-tissue injuries. This study utilized data mining and structural analysis to identify a target that has the ability to evoke healing of the two most commonly injured tissues i.e., bone and muscle, and stimulate the inherent vascular connectivity between the tissues. To find out the multipotential molecule the gene expression profile from GSE34747 was extracted and processed to identify the differentially expressed genes (DEGs). The DEGs were then subjected to gene ontology enrichment analysis to filter out a target that is likely to regulate the multi-tissue regeneration. Further, STITCH and PubChem databases were screened to determine a stimulatory drug against the identified target molecule. Finally, the binding affinity and stability of the potential drug candidate(s) against the target were analysed by molecular docking and molecular dynamics simulation. The results revealed that bone morphogenetic protein-4 (BMP-4) was associated with the regulation of the multiple regeneration processes. The computational screening results suggested Retinoic acid and Torularhodin as potential drug candidates for the stimulation of BMP-4. Both drugs demonstrated slightly different but stable interactions with BMP-4, suggesting that the identified drug candidates are likely to serve as potential leads to further enhance tissues regeneration.

Communicated by Ramaswamy H. Sarma

Keywords:

• Bone morphogenetic protein-4
• retinoic acid
• virtual screening
• drug repurposing
• molecular dynamics simulation

Acknowledgments

We thank Director, INMAS, for his continuous support. Authors would also acknowledge the timely advice from Mr. Subodh Kumar, Senior Research Fellow, INMAS and Dr. Shilpi Modi (Scientist ‘E’), INMAS for extending high-performance computational facility.

Disclosure statement

The authors declare no conflict of interest.

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Data availability

The data that support findings of this study are openly available in Gene Expression Omnibus (GEO), NCBI at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34747, reference number – GSE34747.