Biology Reference
In-Depth Information
INTRODUCTION
G protein-coupled receptors (GPCRs) function rests on intramolecular and intermo-
lecular structural communication (reviewed in
Fanelli & De Benedetti, 2005
).
Indeed, GPCRs regulate most aspects of cell activity by transmitting extracellular
signals inside the cell (reviewed in
Lefkowitz, 2000; Pierce, Premont, &
Lefkowitz, 2002
). GPCRs share an up-down bundle of seven transmembrane
helices (H1-H7) connected by three intracellular (I1, I2, and I3) and three
extracellular loops (E1, E2, and E3), an extracellular N-term, and an intracellular
C-term. Upon activation by extracellular signals, the receptors activate the
-subunit
in heterotrimeric guanine nucleotide-binding proteins (G proteins) by catalyzing the
exchange of bound GDP for GTP, that is, they act as guanine nucleotide exchange
factors (GEFs). Thus, GPCRs are allosteric proteins that transform extracellular sig-
nals into promotion of nucleotide exchange in intracellular G proteins. Regulated
protein-protein interactions are key features of many aspects of GPCR function,
and there is increasing evidence that these receptors act as part of multicomponent
units comprising a variety of signaling and scaffolding molecules (
Brady & Limbird,
2002; Pierce et al., 2002
).
The representation of biomolecular structures as networks of interacting amino
acids/nucleotides is ever increasingly employed to investigate and elucidate complex
phenomena such as protein folding and unfolding, protein stability, the role of struc-
turally and functionally important residues, protein-protein and protein-DNA interac-
tions as well as intraprotein and interprotein communication, and allosterism (
Amitai
et al., 2004; Angelova et al., 2011; Bode et al., 2007; Brinda & Vishveshwara, 2005;
Chennubhotla &Bahar, 2007; Chennubhotla, Yang, &Bahar, 2008; del Sol, Fujihashi,
Amoros, & Nussinov, 2006; Fanelli & Felline, 2011; Fanelli & Seeber, 2010; Pandini,
Fornili, Fraternali, & Kleinjung, 2012; Papaleo, Lindorff-Larsen, & De Gioia, 2012;
Raimondi, Felline, Portella, Orozco, & Fanelli, 2013; Raimondi, Felline, Seeber,
Mariani, & Fanelli, 2013; Tang et al., 2007; Vendruscolo, Paci, Dobson, &
Karplus, 2001; Vishveshwara, Ghosh, & Hansia, 2009
).
These studies rely on methods that differ in the set of graph construction
rules. The graph-based approach proposed by
Vishveshwara et al. (2009)
and
defined as protein structure network (PSN) is the one that we have recently
implemented in the Wordom software (
Seeber et al., 2011
). It basically computes
network features (e.g., nodes, hubs (i.e., hyperconnected nodes), and links) and
shortest communication pathways from molecular dynamics (MD) trajectories
(herein indicated as PSN-MD). The employment of MD trajectories instead
of a single structure serves to provide a dynamic description of the network
as links break and form with atomic fluctuations. We have recently developed
a strategy to infer a dynamic structure network even when dealing with a single
structure rather than a trajectory. In this case, system dynamics is inferred from
the coarse-grained Elastic Network Model paired with Normal Mode Analysis
(ENM-NMA) (
Raimondi, Felline, Seeber, et al., 2013
). The approach is herein
defined as PSN-ENM.
a
