Analysis and prediction of TetR allostery with machine learning methods and a statistical model

Abstract

Allostery is a fundamental regulatory mechanism in biology. Understanding the mechanism of allostery at the molecular level, especially prediction of allosteric hotspots, has remained a major challenge to date. Recent deep mutational study of the tetracycline repressor (TetR) observed that allosteric hotspots are distributed throughout the protein, in contrast to the popular view that hotspot residues tend to form well-defined pathways for “information transduction”. Remarkably, the loss of allostery due to mutation of hotspot residues could be rescued in multiple ways by additional mutations, suggesting a considerable degree of degeneracy in the allosteric network.