Lotthammer, J. M. | bioRxiv Science

bioRxiv2022

Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains

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 6 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 milliseconds of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatins 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 (R2=0.82). To test our ability to make blind predictions, we predicted blebbistatins binding affinity for an isoform (Myh7b) whose blebbistatin sensitivity was unknown. Encouragingly, we find good agreement between the predicted and measured IC50 (0.67 {micro}M vs. 0.36 {micro}M). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets. SignificanceDrug development requires the discovery of compounds which specifically target one member of a protein family without triggering side effects that arise from interactions with other related proteins. Myosins are a family of motor proteins that are drug targets for heart diseases, cancer, and parasitic infections. Here, we investigate why the compound blebbistatin specifically inhibits some myosins more potently than others, even though its binding site is closed in all known experimental structures. We find that the blebbistatin binding pocket opens in molecular dynamics simulations of certain myosin motors, and that the probability of opening predicts how potently blebbistatin inhibits a particular motor. Our work suggests that differences in cryptic pocket formation can be exploited to develop specific therapeutics.

biophysics↗

bioRxiv2022

Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a protein structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly-curated dataset of 39 experimentally-confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures are likely to contain cryptic pockets, vastly expanding the druggable proteome.

biophysics↗

bioRxiv2021

In Silico Electrophysiology of Inner-Ear Mechanotransduction Channel TMC1 Models

Inner-ear sensory hair cells convert mechanical stimuli from sound and head movements into electrical signals during mechanotransduction. Identification of all molecular components of the inner-ear mechanotransduction apparatus is ongoing; however, there is strong evidence that TMC1 and TMC2 are pore-forming subunits of the complex. We present molecular dynamics simulations that probe ion conduction of TMC1 models built based on two different structures of related TMEM16 proteins. Unlike most channels, the TMC1 models do not show a central pore. Instead, simulations of these models in a membrane environment at various voltages reveal a peripheral permeation pathway that is exposed to lipids and that shows cation permeation at rates comparable to those measured in hair cells. Furthermore, our analyses suggest that TMC1 gating mechanisms involve protein conformational changes and tension-induced lipid-mediated pore widening. These results provide insights into ion conduction and activation mechanisms of hair-cell mechanotransduction channels essential for hearing and balance.

biophysics↗