bioRxivBioinformaticsPreprint
Understanding the characteristic behavior of the wild-type and mutant protein structure of FLT3 protein by computational methods
Saleena YounusiD, Özge TatlıiD, Ahmad Nasimian, Julhash Uddin KaziiD, Lars RönnstrandiD
Abstract
FLT3 emerges as a commonly mutated protein with significant prognostic implications in acute myeloid leukemia (AML). Point mutations or deletions in the tyrosine kinase domain (TKD) at the activation loop and internal tandem duplications (ITD) in the juxtamembrane (JM) region (and less commonly in the TKD) are the primary mutations that occur in the FLT3 protein. Besides, AML treatment with tyrosine kinase inhibitor drugs may result in the acquisition of TKD mutations in the FLT3-ITD structure. All these mutations will induce activation of the kinase activity of FLT3 protein leading to activation of downstream signaling pathways. Therefore, finding better therapeutics against each of these mutant FLT3 proteins is crucial in the treatment of AML. This study aims to comprehend the characteristic behavior of TKD mutants (C and F in Y842), ITD mutants, and the combination of ITD with TKD mutations (C and F in Y842) in the FLT3 protein through computational approaches, including Molecular Dynamic (MD) simulation, cluster analysis, and machine learning techniques. The MD simulation studies revealed the alterations in the optimized state, flexibility, and compactness nature between FLT3-WT and mutated FLT3 proteins and identified significant changes in the point mutants, ITD, and the combined ITD and TKD mutated FLT3 protein structures. Cluster analysis also confirmed that these mutations significantly impact the overall flexibility of the protein structures, especially in the point-mutated structures of FLT3-Y842C and FLT3-ITD-Y842F. These findings emphasize the diverse protein conformations of mutated structures of the FLT3 protein, contributing to the deregulation of FLT3 protein function, and identified these mutated proteins as promising therapeutic targets in the treatment of AML.
- organism: human
- assay: protein structure
- research focus: protein structure prediction
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
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