Potential inhibitors for FKBP51: an in silico study using virtual screening, molecular docking and molecular dynamics simulation

Abstract

Over the years, FK506-binding proteins have been targeted for different pharmaceutical interests. The FK506-binding protein, encoded by the FKBP5 gene, is responsible for stress and metabolic-related disorders, including cancer. In addition, the FKBD-I domain of the protein is a potential target for endocrine-related physiological diseases. In the present study, a set of natural compounds from the ZINC database was screened against FKBP51 protein using in silico strategy, namely pharmacophore modeling, molecular docking, and molecular dynamic simulation. A protein–ligand-based pharmacophore model workflow was employed to identify small molecules. The resultant compounds were then assessed for their toxicity using ADMET prediction. Based on ADMET prediction, 4768 compounds were selected for molecular docking to elucidate their binding mode. Based on the binding energy, 857 compounds were selected, and their Similarity Tanimoto coefficient was calculated, followed by clustering according to Jarvis–Patrick clustering methods (Jarp). The clustered singletons resulted in 14 hit compounds. The top 05 hit compounds and 05 known compounds were then subjected to 100 ns MD simulation to check the stability of complexes. The study revealed that the selected complexes are stable throughout the 100 ns simulation; for FKBD-I (4TW6), crystal structure compared with FKBP-51 (1KT0) crystal structure. Finally, the binding free energies of the hit complexes were calculated using molecular mechanics energies combined with Poisson–Boltzmann. The data reveal that all the complexes show negative BFEs, indicating a good affinity of the hit compounds to the protein. The top five compounds are, therefore, potential inhibitors for FKBP51.

Communicated by Ramaswamy H. Sarma

Graphical Abstract

Keywords:

• FKBP51
• natural compounds
• pharmacophore modeling
• molecular docking
• molecular dynamics simulation
• binding free energy calculation

Author contributions

Conceptualization, NT, and SB.; methodology, SB, DD.; software, SB, DD, SE; validation, SB, DD, and SE; formal analysis, SB, DD, SE.; data curation, NT, SB, DD, SE.; writing original draft preparation, NT, SB, DD; writing SB, DD; review and editing, M.J.N, M.J.N.C. SB, DD, P.M., JB, NT, visualization, SB, DD; supervision, JB, P.M., NT; project administration, JB, NT; funding acquisition, NT.

Acknowledgements

The authors are thankful to the DBT, Government of India, for providing the funds (Sanction No. DBT-NER/Health/42/2013). The authors thank JSS college, Ooty, Tamil Nadu, India, for providing us the resources for the completion of our research. The authors are also thankful to the Director of IASST for providing resources to complete our research.

Disclosure statement

The authors have declared that no conflict of interest exists.

Additional information

Funding

The research work was funded by a project grant sanction No. DBT-NER/Health/ 42/2013: Dated 16/06/2016 from DBT New Delhi, India.