Modulation of interaction of mutant TP53 and wild type BRCA1 by alkaloids: a computational approach towards targeting protein-protein interaction as a futuristic therapeutic intervention strategy for breast cancer impediment

Abstract

Protein–protein interactions (PPI) are a new emerging class of novel therapeutic targets. In order to probe these interactions, computational tools provide a convenient and quick method towards the development of therapeutics. Keeping this in view the present study was initiated to analyse interaction of tumour suppressor protein p53 (TP53) and breast cancer associated protein (BRCA1) as promising target against breast cancer. Using computational approaches such as protein–protein docking, hot spot analyses, molecular docking and molecular dynamics simulation (MDS), stepwise analyses of the interactions of the wild type and mutant TP53 with that of wild type BRCA1 and their modulation by alkaloids were done. Protein–protein docking method was used to generate both wild type and mutant complexes of TP53-BRCA1. Subsequently, the complexes were docked using sixteen different alkaloids, fulfilling ADMET and Lipinski’s rule of five criteria, and were compared with that of a well-known inhibitor of PPI, namely nutlin. The alkaloid dicentrine was found to be the best docked alkaloid among all the docked alklaloids as well as that of nutlin. Furthermore, MDS analyses of both wild type and mutant complexes with the best docked alkaloid i.e. dicentrine, revealed higher stability of mutant complex than that of the wild one, in terms of average RMSD, RMSF and binding free energy, corroborating the results of docking. Results suggested more pronounced interaction of BRCA1 with mutant TP53 leading to increased expression of mutated TP53 thus showing a dominant negative gain of function and hampering wild type TP53 function leading to tumour progression.

Keywords:

• alkaloids
• breast cancer
• molecular dynamics simulation
• protein–protein interaction
• TP53-BRCA1

Acknowledgments

Financial supports from Department of Biotechnology (DBT), New Delhi under Bioinformatics Infrastructure Facility, Department of Higher Education, Government of U.P., Lucknow under Centre of Excellence Grant and Institute for Development of Advance Computing and Department of Science and Technology, New Delhi under Promotion of University Research and Scientific Excellence (DST PURSE) program are gratefully acknowledged.