Abstract
Allosteric signaling in proteins allows perturbations at one locale to modulate activity at an orthosteric distant site. This may explain how distal mutations disrupt protein activity and offer pathways for the development of allosteric therapeutics, a novel class of restorative compounds to reactivate native function. Despite the ubiquitous presence of allosteric control in nature and the promises that it holds for treating currently untreatable diseases, quantitative theory of the mechanism of allostery is lacking. Working to fill this critical gap, we have developed a novel method to identify groups of covarying residues which the sector hypothesis suggests are capable of transmitting allosteric signals in proteins. A major problem with sectors computed from covariance measures is the selection relies upon a full covariance matrix rather than on the covariance among the residues posited to be in the sector. We demonstrate a novel method which constructs sectors on the basis of cohesion within the residues in the sector to eliminate the incongruity between the sector idea and the way it is calculated. Furthermore, the refinement can be iteratively applied, enabling the extraction of more than one sector in a well-defined, systematic manner. In this study, we report on the development of MD multi-sector selector and its application to allosteric signaling in the tumor suppressor protein p53. We consider the implications of our findings on our long-term goal of allosterically reactivating mutant p53 as a means of curing cancer, and critically assess the broader applicability of MD multi-sector selector across diverse fields.