Role of ELA region in auto-activation of mutant KIT receptor: a molecular dynamics simulation insight

<div><p>KIT receptor is the prime target in gastrointestinal stromal tumor (GISTs) therapy. Second generation inhibitor, Sunitinib, binds to an inactivated conformation of KIT receptor and stabilizes it in order to prevent tumor formation. Here, we investigated the dynamic behavior of wild type and mutant D816H KIT receptor, and emphasized the extended A-loop (EAL) region (805–850) by conducting molecular dynamics simulation (∼100 ns). We analyzed different properties such as root mean square cutoff or deviation, root mean square fluctuation, radius of gyration, solvent-accessible surface area, hydrogen bonding network analysis, and essential dynamics. Apart from this, clustering and cross-correlation matrix approach was used to explore the conformational space of the wild type and mutant EAL region of KIT receptor. Molecular dynamics analysis indicated that mutation (D816H) was able to alter intramolecular hydrogen bonding pattern and affected the structural flexibility of EAL region. Moreover, flexible secondary elements, specially, coil and turns were dominated in EAL region of mutant KIT receptor during simulation. This phenomenon increased the movement of EAL region which in turn helped in shifting the equilibrium towards the active kinase conformation. Our atomic investigation of mutant KIT receptor which emphasized on EAL region provided a better insight into the understanding of Sunitinib resistance mechanism of KIT receptor and would help to discover new therapeutics for KIT-based resistant tumor cells in GIST therapy.</p></div

Solvent-accessible surface area (SASA) of native and mutant AKT1 protein PH domain versus time at 300 K.

<p>Native is shown in black and mutant in red.</p

RMSF of the backbone CAs of Cα atoms of native and mutant AKT1 protein PH domain versus time at 300 K.

<p>Native is shown in black and mutant in red.</p

Backbone RMSDs are shown as a function of time for native and mutant Aurora-A protein structures at 300 K.

<p>Native is shown in black and mutant in red.</p

In order to show the deviations clearly, the RMSD plot is shown for upper RMSD limit of 0.5 nm.

<p>Native is shown in black and mutant in red.</p

Radius of gyration of Cα atoms of native and mutant Aurora-A protein versus time at 300 K.

<p>Native is shown in black and mutant in red.</p

Average number of protein–solvent intermolecular hydrogen bonds in native and mutant MCAK protein motor domain versus time at 300 K.

<p>Native is shown in black and mutant in red.</p

Average number of protein–solvent intermolecular hydrogen bonds in native and mutant AKT1 protein PH domain membrane localization residues versus time at 300 K.

<p>(a) K30 (b) K32 (c) W36 (d) R38 (e) R40 (f) Q53 (g) R56 (h) K57 (i) V58. Native is shown in black and mutant in red.</p

Use of Long Term Molecular Dynamics Simulation in Predicting Cancer Associated SNPs

<div><p>Computational prediction of cancer associated SNPs from the large pool of SNP dataset is now being used as a tool for detecting the probable oncogenes, which are further examined in the wet lab experiments. The lack in prediction accuracy has been a major hurdle in relying on the computational results obtained by implementing multiple tools, platforms and algorithms for cancer associated SNP prediction. Our result obtained from the initial computational compilations suggests the strong chance of Aurora-A G325W mutation (rs11539196) to cause hepatocellular carcinoma. The implementation of molecular dynamics simulation (MDS) approaches has significantly aided in raising the prediction accuracy of these results, but measuring the difference in the convergence time of mutant protein structures has been a challenging task while setting the simulation timescale. The convergence time of most of the protein structures may vary from 10 ns to 100 ns or more, depending upon its size. Thus, in this work we have implemented 200 ns of MDS to aid the final results obtained from computational SNP prediction technique. The MDS results have significantly explained the atomic alteration related with the mutant protein and are useful in elaborating the change in structural conformations coupled with the computationally predicted cancer associated mutation. With further advancements in the computational techniques, it will become much easier to predict such mutations with higher accuracy level.</p></div

Superimposed native and mutant structures of residue region 80–105 at different time steps.

<p>Here the native is shown in green and mutant in red.</p