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Incidental conformational switching in an allosteric enzyme

The classical understanding of allostery was initially grounded in two-state models, such as MWC and KNF, where structure and function are inextricably linked through transitions between low-(T) and high-affinity (R) states. Here, we show Yeast chorismate mutase (CM) provides a vivid example of the growing list of exceptions to the traditional T vs R two-state allosteric paradigm. While CM…

Classical models of allostery, like MWC and KNF, traditionally envision a rigid link between structure and function, with transitions between low-affinity (T) and high-affinity (R) states. However, research on yeast chorismate mutase (CM) suggests a departure from this conventional wisdom. Despite CM's tendency to sample the R-state upon activation by tryptophan (Trp), instances arise where conformational status and catalytic activity become disconnected.

Employing NMR spectroscopy and kinetic assays, researchers identified CM variants that predominantly exist in the T-conformation and demonstrate maximal activity, while others that largely occupy the R-conformation exhibit reduced activity. When these findings are quantitatively compared against a parameterized conformational selection (CS) model, discrepancies of up to two orders of magnitude are observed, casting doubt on the applicability of the simpler two-state model for modulating substrate affinity in this case.

The study proposes that the observed T-to-R switching within CM is an "incidental" side effect of an energy landscape that facilitates substrate binding without directly dictating affinity. The authors argue that allosteric regulation in CM might instead be governed by local characteristics of the resting-state ensemble, operating independently of the global T/R status.

This investigation not only challenges existing assumptions about allosteric mechanisms but also advocates for a more comprehensive ensemble-based approach in the realms of protein engineering and the study of allostery.

Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.

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