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

  • 1,653
    views
  • 248
    downloads
  • 25
    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. Computational and Systems Biology
    2. Genetics and Genomics
    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.

    1. Computational and Systems Biology
    Huiyong Cheng, Dawson Miller ... Qiuying Chen
    Research Article

    Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.