Conformational transitions in yeast chorismate mutase important for allosteric regulation as identified by nuclear magnetic resonance spectroscopy

Mixed, nonclassical behavior in a classic allosteric protein

Allostery is a major driver of biological processes requiring coordination. Thus, it is one of the most fundamental and remarkable phenomena in nature, and there is motivation to understand and manipulate it to a multitude of ends. Today, it is often described in terms of two phenomenological models proposed more than a half-century ago involving only T(tense) or R(relaxed) conformations. Here, methyl-based NMR provides extensive detail on a dynamic T to R switch in the classical dimeric allosteric protein, yeast chorismate mutase (CM), that occurs in the absence of substrate, but only with the activator bound. Switching of individual subunits is uncoupled based on direct observation of mixed TR states in the dimer. This unique finding excludes both classic models and solves the paradox of a coexisting hyperbolic binding curve and highly skewed substrate-free T-R equilibrium. Surprisingly, structures of the activator-bound and effector-free forms of CM appear the same by NMR, providing another example of the need to account for dynamic ensembles. The apo enzyme, which has a sigmoidal activity profile, is shown to switch, not to R, but to a related high-energy state. Thus, the conformational repertoire of CM does not just change as a matter of degree depending on the allosteric input, be it effector and/or substrate. Rather, the allosteric model appears to completely change in different contexts, which is only consistent with modern ensemble-based frameworks.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.