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

  1. Artur Meller
  2. Jeffrey M. Lotthammer
  3. Louis G Smith
  4. Borna Novak
  5. Lindsey A Lee
  6. Catherine C Kuhn
  7. Lina Greenberg
  8. Leslie A Leinwand
  9. Michael J Greenberg
  10. Gregory R Bowman  Is a corresponding author
  1. Washington University in St. Louis, United States
  2. University of Pennsylvania, United States
  3. University of Colorado Boulder, United States

Abstract

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 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 (R2=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 mM vs. 0.36 mM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.

Data availability

Experimental, pocket volume, docking, and trajectory clustering data have been deposited in OSF under accession code CV6D2. Scripts and notebooks used to generate all figures are available in our GitHub repository (https://github.com/bowman-lab/blebbistatin-specificity).

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Artur Meller

    Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5504-2684
  2. Jeffrey M. Lotthammer

    Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Louis G Smith

    Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Borna Novak

    Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Lindsey A Lee

    Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Catherine C Kuhn

    Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Lina Greenberg

    Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Leslie A Leinwand

    Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1470-4810
  9. Michael J Greenberg

    Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1320-3547
  10. Gregory R Bowman

    Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, United States
    For correspondence
    grbowman@seas.upenn.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2083-4892

Funding

National Institutes of Health (1F30HL162431-01A1)

  • Artur Meller

National Institutes of Health (R01 GM124007)

  • Gregory R Bowman

National Institutes of Health (RF1AG067194)

  • Gregory R Bowman

National Institutes of Health (R01 HL141086)

  • Michael J Greenberg

National Institutes of Health (GM 20909)

  • Leslie A Leinwand

National Science Foundation (DGE2139839)

  • Jeffrey M. Lotthammer

National Science Foundation (MCB-1552471)

  • Gregory R Bowman

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2023, Meller et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,674
    views
  • 250
    downloads
  • 26
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Artur Meller
  2. Jeffrey M. Lotthammer
  3. Louis G Smith
  4. Borna Novak
  5. Lindsey A Lee
  6. Catherine C Kuhn
  7. Lina Greenberg
  8. Leslie A Leinwand
  9. Michael J Greenberg
  10. Gregory R Bowman
(2023)
Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains
eLife 12:e83602.
https://doi.org/10.7554/eLife.83602

Share this article

https://doi.org/10.7554/eLife.83602

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.