This title appears in the Scientific Report :
2015
Please use the identifier:
http://dx.doi.org/10.1016/j.abb.2015.03.011 in citations.
Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins
Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins
G protein coupled receptors (GPCRs) and intrinsic disordered proteins (IDPs) are key players for neuronal function and dysfunction. Unfortunately, their structural characterization is lacking in most cases. From one hand, no experimental structure has been determined for the two largest GPCRs subfam...
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Personal Name(s): | Rossetti, Giulia |
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Dibenedetto, Domenica / Calandrini, Vania / Giorgetti, Alejandro (Corresponding Author) / Carloni, Paolo | |
Contributing Institute: |
Jülich Supercomputing Center; JSC GRS; GRS Computational Biomedicine; IAS-5 Computational Biomedicine; INM-9 |
Published in: | Archives of biochemistry and biophysics, 582 (2015) S. 91–100 |
Imprint: |
San Diego, Calif.
Elsevier
2015
|
PubMed ID: |
25797436 |
DOI: |
10.1016/j.abb.2015.03.011 |
Document Type: |
Journal Article |
Research Program: |
Computational Science and Mathematical Methods Theory, modelling and simulation |
Publikationsportal JuSER |
G protein coupled receptors (GPCRs) and intrinsic disordered proteins (IDPs) are key players for neuronal function and dysfunction. Unfortunately, their structural characterization is lacking in most cases. From one hand, no experimental structure has been determined for the two largest GPCRs subfamilies, both key proteins in neuronal pathways. These are the odorant (450 members out of 900 human GPCRs) and the bitter taste receptors (25 members) subfamilies. On the other hand, also IDPs structural characterization is highly non-trivial. They exist as dynamic, highly flexible structural ensembles that undergo conformational conversions on a wide range of timescales, spanning from picoseconds to milliseconds. Computational methods may be of great help to characterize these neuronal proteins. Here we review recent progress from our lab and other groups to develop and apply in silico methods for structural predictions of these highly relevant, fascinating and challenging systems. |