The susceptibility of edges in a trained resistor network contains all physical information of the cost landscape and identifies the edges responsible for the learned function.
Topological characterization of the continuum of allosteric response
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Allosteric regulation in proteins is often accompanied by conformational changes that facilitate transmission of mechanical signals between distant ligand binding sites. Typically, these deformations are classified in terms of specific archetypes, including various types of hinge mechanisms or allosteric pathways localized to sequences of amino acids.However, many allosteric deformations resist such strict categorization. Here, we introduce a quantitative topological description of allosteric deformation, unifying all archetypal mechanisms into a single framework. The topological description aligns with two key structural features often associated with allosteric deformations, namely hinge domains and allosteric pathways, enabling us to quantify the significance of each of these features. To develop the analysis, we tune computer-generated mechanical networks to perform allostery-like functions, obtaining an ensemble of networks that establish a range of possible allosteric deformations. The analysis shows that these networks' allosteric mechanisms cannot be described in terms of discrete archetypes - they fall on a continuum. We then apply the same analysis to a collection of allosteric proteins with similar results, showing that our framework encompasses these proteins as well as designed allosteric networks. Our results provide a new picture for allostery, demonstrating not only how it can be described quantitatively, but also giving insight into how it emerges as a collective property.
citation-role summary
citation-polarity summary
fields
cond-mat.dis-nn 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Microscopic imprints of learned solutions in adaptive resistor networks
The susceptibility of edges in a trained resistor network contains all physical information of the cost landscape and identifies the edges responsible for the learned function.