Key result
Markov state models accurately predict blebbistatin binding affinities across myosin isoforms, explaining ~82% of variance.
Why the study?
It remained unclear why blebbistatin inhibits myosin-II motors with different potencies despite binding at a highly conserved, cryptic pocket.
Markov state models of cryptic pocket opening probabilities can accurately predict isoform-specific binding affinities for allosteric modulators like blebbistatin.
May inform isoform-selective myosin modulator development; leaves open validation in disease models.
The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least six of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 ms of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatin’s binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R 2 =0.82). In a blind prediction for an isoform (Myh7b) whose blebbistatin sensitivity was unknown, we find good agreement between predicted and measured IC50s (0.67 μM vs. 0.36 μM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.
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Meller et al. (2023) studied Myosin motor domain inhibition. Blebbistatin was evaluated on Blebbistatin binding affinity prediction. Markov state models of molecular dynamics simulations accurately predicted blebbistatin binding affinities across myosin isoforms (R2=0.82) and successfully predicted the IC50 of Myh7b as 0.36 μM.
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