Contrasting Effects of Inhibitors Li⁺ and Be²⁺ on Catalytic Cycle of Glycogen Synthase Kinase-3β

Abstract

Ionic lithium shows rare effectiveness for treating bipolar disorder and is a promising drug for treating neurodegenerative diseases. Unfortunately, lithium suffers from significant drawbacks as a drug, mainly a narrow therapeutic window. Among the different targets of lithium, glycogen synthase kinase 3β (GSK-3β) might be the one responsible for its therapeutic effects. Developing alternative, selective inhibitors of this kinase could avoid lithium side effects, but efforts to do so have met little success so far. A detailed, atomistic understanding of Li + inhibition and a more detailed understanding of the phosphorylation reaction GSK-3β catalyzes would therefore facilitate the development of new drugs. In this study, we use extensive sampling of catalytic states with our mixed quantum-classical dynamics method QM/DMD and binding affinities from a competitive metal affinity (CMA) approach to fill out the atomic scale picture of Li + GSK-3β inhibition. We compare Li + action with Be 2+ , another known inhibitor, and find our results in agreement with in-vitro kinetics studies. Ultimately, our simulations show that Li + inhibition is driven primarily by directly decreasing the reaction rate of the phosphorylation step, rather than reducing catalytic turnover through tight binding to different GSK-3β states like Be 2+ inhibition. The effect of these metals derive from electrostatic differences and especially their smaller atomic radii compared to the native Mg 2+ and thus provides insight for the development of GSK-3β inhibitors based on other paradigms.

New Advances in the Pharmacology and Toxicology of Lithium: A Neurobiologically Oriented Overview

“…Undoubtedly, among the many molecular targets of lithium, GSK3β may be in part responsible for its therapeutic effects, so the development of selective inhibitors of this kinase could influence the side effects of lithium. Although beyond the scope of this review, in recent years, the application of machine learning is accelerating the discovery of potential drugs with GSK3β inhibitory activity that can be repurposed as therapeutics for mood disorders and/or neurodegenerative disorders (see Kleandrova et al, 2020;Vignaux et al, 2020;Zhu et al, 2020;Reilley et al, 2021 for more details).…”