  Allostery commonly refers to the mechanism that regulates protein activity
through the binding of a molecule at a different, usually distal, site from the
orthosteric site. The omnipresence of allosteric regulation in nature and its
potential for drug design and screening render the study of allostery
invaluable. Nevertheless, challenges remain as few computational methods are
available to effectively predict allosteric sites, identify signalling pathways
involved in allostery, or to aid with the design of suitable molecules
targeting such sites. Recently, bond-to-bond propensity analysis has been shown
successful at identifying allosteric sites for a large and diverse group of
proteins from knowledge of the orthosteric sites and its ligands alone by using
network analysis applied to energy-weighted atomistic protein graphs. To
address the identification of signalling pathways, we propose here a method to
compute and score paths of optimised propensity that link the orthosteric site
with the identified allosteric sites, and identifies crucial residues that
contribute to those paths. We showcase the approach with three well-studied
allosteric proteins: h-Ras, caspase-1, and 3-phosphoinositide-dependent
kinase-1 (PDK1). Key residues in both orthosteric and allosteric sites were
identified and showed agreement with experimental results, and pivotal
signalling residues along the pathway were also revealed, thus providing
alternative targets for drug design. By using the computed path scores, we were
also able to differentiate the activity of different allosteric modulators.
