Calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) inhibitors: a novel approach in small molecule discovery

Abstract

The calcium/calmodulin dependent protein kinase kinase 2 (CAMKK2) plays a key role in regulation of intracellular calcium levels and signaling pathways. It is involved in activation of downstream signaling pathways that regulate various cellular processes. Dysregulation of CAMKK2 activity has been linked to various diseases including cancer, suggesting that CAMKK2 inhibitors might be beneficial in oncological, metabolic and inflammatory indications. The most pressing issues in small molecule discovery are synthesis feasibility, novel chemical structure and desired biological characteristics. To circumvent this constraint, we employed ‘DrugspaceX’ for rapid lead identification, followed by repositioning seven FDA-approved drugs for CAMKK2 inhibition. Further, first-level transformation (Set1 analogues) was performed in ‘DrugspaceX’, followed by virtual screening. The t-SNE visualization revealed that the transformations surrounding Rucaparib, Treprostinil and Canagliflozin are more promising for developing CAMKK2 inhibitors. Second, using the top-ranked Set1 analogues, Set2 analogues were generated, and virtual screening revealed the top-ranked five analogues. Among the top five Set2 analogues, DE273038_5 had the lowest docking score of −11.034 kcal/mol and SA score of 2.59, retaining the essential interactions with Hotspot residues LYS194 and VAL270 across 250 ns simulation period. When compared to the other four compounds, the ligand effectiveness score was 0.409, and the number of rotatable penalties was only three. Further, DE273038_5 after two rounds of transformations was discovered to be novel and had not been previously described in other databases. These data suggest that the new candidate DE273038_5 is likely to have inhibitory activity at the CAMKK2 active site, implying potential therapeutic use.

Communicated by Ramaswamy H. Sarma

Keywords:

• CAMKK2 inhibitors
• analogues
• docking
• MD simulation
• t-SNE

Acknowledgments

Authors thank Yenepoya (Deemed to be University) for providing the infrastructure facility for Centre for Integrative Omics Data Science. We also thank PSGCP and MS Ramaiah University for Schrodinger and simulation support respectively.

Author contributions

Rex Devasahayam Arokia Balaya: Conceptualization, Project administration, Supervision, Visualization, Writing manuscript. Jaikanth Chandrasekaran: Conceptualization, Investigation, Methodology, Visualization, Writing manuscript. Saptami Kanekar: Drafting, Reviewing and editing. Prashant Kumar Modi: Reviewing and editing. Shobha Dagamajalu: Reviewing and editing. Kirthika Gopinathan: Data analysis. Rajesh Raju: Reviewing and editing. T. S. Keshava Prasad: Reviewing and editing.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available in the Supporting Information Material of this paper.

Funding

The authors did not receive financial support from any organization for the submitted work.