Prediction of Protein Allosteric Signalling Pathways and Functional Residues Through Paths of Optimised Propensity

[Display omitted] •Characterise allosteric signalling pathways with weighted atomistic protein graphs.•Allosteric signalling pathways underlie the functional residues at the allosteric site.•Key signalling residues relate to the mutations that alter allosteric effects.•Quantification of allosteric p...

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Veröffentlicht in:Journal of molecular biology 2022-09, Vol.434 (17), p.167749-167749, Article 167749
Hauptverfasser: Wu, Nan, Yaliraki, Sophia N., Barahona, Mauricio
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Sprache:eng
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Zusammenfassung:[Display omitted] •Characterise allosteric signalling pathways with weighted atomistic protein graphs.•Allosteric signalling pathways underlie the functional residues at the allosteric site.•Key signalling residues relate to the mutations that alter allosteric effects.•Quantification of allosteric pathways facilitates the design of drug molecules. 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.
ISSN:0022-2836
1089-8638
DOI:10.1016/j.jmb.2022.167749