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Structural insights into conformational stability of wild-type and mutant beta1-adrenergic receptor
Gouthaman S Balaraman1, Supriyo Bhattacharya, Nagarajan Vaidehi
1Division of Immunology, Beckman Research Institute of the City of Hope, Duarte, California, USA.
Abstract:
Recent experiments to derive a thermally stable mutant of turkey beta-1-adrenergic receptor (beta1AR) have shown that a combination of six single point mutations resulted in a 20 degrees C increase in thermal stability in mutant beta1AR. Here we have used the all-atom force-field energy function to calculate a stability score to detect stabilizing point mutations in G-protein coupled receptors. The calculated stability score shows good correlation with the measured thermal stability for 76 single point mutations and 22 multiple mutants in beta1AR. We have demonstrated that conformational sampling of the receptor for various mutants improve the prediction of thermal stability by 50%. Point mutations Y227A5.58, V230A5.61, and F338M7.48 in the thermally stable mutant m23-beta1AR stabilizes key microdomains of the receptor in the inactive conformation. The Y227A5.58 and V230A5.61 mutations stabilize the ionic lock between R139(3.50) on transmembrane helix3 and E285(6.30) on transmembrane helix6. The mutation F338M7.48 on TM7 alters the interaction of the conserved motif NPxxY(x)5,6F with helix8 and hence modulates the interaction of TM2-TM7-helix8 microdomain. The D186-R317 salt bridge (in extracellular loops 2 and 3) is stabilized in the cyanopindolol-bound wild-type beta1AR, whereas the salt bridge between D184-R317 is preferred in the mutant m23. We propose that this could be the surrogate to a similar salt bridge found between the extracellular loop 2 and TM7 in beta2AR reported recently. We show that the binding energy difference between the inactive and active states is less in m23 compared to the wild-type, which explains the activation of m23 at higher norepinephrine concentration compared to the wild-type. Results from this work throw light into the mechanism behind stabilizing mutations. The computational scheme proposed in this work could be used to design stabilizing mutations for other G-protein coupled receptors.
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