Speaker
Description
Identifying functionally relevant allosteric sites is a non-trivial problem, as such sites are often not evident from static structures and depend on protein dynamics. From an information-theoretic perspective, this problem can be framed as identifying regions that share mutual information relevant to fluctuations at a designated active site. Effective allosteric regulation involves selective coupling to active-site fluctuations. At the same time, the feasibility of allostery is restricted to a subset of residues by context-dependent biological and physical factors, including solvent accessibility, extracellular or intracellular accessibility in membrane proteins, and separation from the active site to avoid steric interference. These constraints induce an information bound on the amount of functionally relevant information that can be transmitted from candidate allosteric sites to a specified active site.
Here, we study protein dynamics modeled by the Gaussian Network Model and use closed-form solutions of the Gaussian Information Bottleneck to quantify the information-theoretic bound on how much information can, in principle, be transmitted from candidate allosteric sites to a specified active site. This approach allows assessment of how closely naturally occurring allosteric sites operate relative to an explicit information-theoretic bound and enables principled comparison of different surface pockets within the same protein. It could also provide principles for mutating proteins or designing new ones that support allostery.
