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000827445 1001_ $$0P:(DE-HGF)0$$aPonzoni, Luca$$b0$$eCorresponding author
000827445 245__ $$aUnifying view of mechanical and functional hotspots across class A GPCRs
000827445 260__ $$aSan Francisco, Calif.$$bPublic Library of Science$$c2017
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000827445 520__ $$aG protein-coupled receptors (GPCRs) are the largest superfamily of signaling proteins. Their activation process is accompanied by conformational changes that have not yet been fully uncovered. Here, we carry out a novel comparative analysis of internal structural fluctuations across a variety of receptors from class A GPCRs, which currently has the richest structural coverage. We infer the local mechanical couplings underpinning the receptors’ functional dynamics and finally identify those amino acids whose virtual deletion causes a significant softening of the mechanical network. The relevance of these amino acids is demonstrated by their overlap with those known to be crucial for GPCR function, based on static structural criteria. The differences with the latter set allow us to identify those sites whose functional role is more clearly detected by considering dynamical and mechanical properties. Of these sites with a genuine mechanical/dynamical character, the top ranking is amino acid 7x52, a previously unexplored, and experimentally verifiable key site for GPCR conformational response to ligand binding.
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000827445 536__ $$0G:(DE-Juel1)jias59_20161101$$aTowards  the design of allosteric ligands binding to the  human muscarinic receptor M2 (jias59_20161101)$$cjias59_20161101$$fTowards  the design of allosteric ligands binding to the  human muscarinic receptor M2$$x2
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000827445 7001_ $$0P:(DE-Juel1)145921$$aRossetti, Giulia$$b1$$eCorresponding author
000827445 7001_ $$0P:(DE-Juel1)169313$$aMaggi, Luca$$b2
000827445 7001_ $$0P:(DE-Juel1)165199$$aGiorgetti, Alejandro$$b3
000827445 7001_ $$0P:(DE-Juel1)145614$$aCarloni, Paolo$$b4
000827445 7001_ $$0P:(DE-HGF)0$$aMicheletti, Cristian$$b5
000827445 773__ $$0PERI:(DE-600)2193340-6$$a10.1371/journal.pcbi.1005381$$gVol. 13, no. 2, p. e1005381 -$$n2$$pe1005381 -$$tPLoS Computational Biology$$v13$$x1553-7358$$y2017
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