Unravelling the drugability of MSI2 RNA recognition motif (RRM) protein and the prediction of their effective antileukemia inhibitors from traditional herb concoctions

Abstract

MSI2 is a homolog 2 of the Musashi RNA binding proteins (MSI) and is known to contribute to acute myeloid leukaemia (AML) and expressed up to 70% in AML patients. High expression of MSI2 has been found to lead to the lower overall survival of patients with AML. This study proposed the potential antagonists of MSI2 RNA-recognition motifs (MSI2 RRM1) derived from the LC-MS analysis of three traditional herbal samples. The LC-MS analysis of the three traditional herbs concoctions yields a total of 271 unique molecules of which 262 were screened against MSI2 RRM1 protein. After the dynamic study of the selected 8 top molecules from the virtual screening, the five most promising ligands emerged as potential MSI2 antagonists compare to the reference experimental molecule. The results show that the dynamic of MSI2 RRM1 protein is accompanied by a rare even of protein chain dissociation and re-association as evident in both the bound and unbound state of the protein. The unbound protein experience earlier chain dissociation compare to ligand-bound protein indicating that ligand binding to the protein slows down the dissociation time but thereafter increases the frequency of alternation between the protein chain association and dissociation after the first experience. Interestingly, the re-association of the protein chain is also accompanied by full restoration of the ligands to the binding site. The drug candidate Methotrexate (M3) and rescinnamine (M9) are listed among the promising antagonist of MSI2 with unique properties compared to a less promising molecule Ergotamine (M6).

Communicated by Ramaswamy H. Sarma

Keywords:

• Musashi RNA binding proteins
• antileukemia
• bound and unbound protein
• traditional herbs
• protein reassociation
• drugability of MSI2 protein

Acknowledgements

The authors are grateful to acknowledge the University of Kwazulu-Natal, University of the Free State and the National Research Foundation (NRF Grant Nos: 109673, 113327 and 96111) in South Africa for financial support, as well as the Centre for High-Performance Computing (CHPC) for the simulation facilities.

Disclosure statement

This work does not require any ethical statement and there is no conflict of interest.