Sampling alternative conformational states of transporters and receptors with AlphaFold2

  1. Diego del Alamo
  2. Davide Sala
  3. Hassane S Mchaourab  Is a corresponding author
  4. Jens Meiler  Is a corresponding author
  1. Vanderbilt University, United States

Abstract

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward-facing (IF) and outward-facing (OF) states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein coupled receptors (GPCRs) that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments (MSAs) by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling (TM)-score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

Data availability

All scripts and data presented in this study are made available for download at www.github.com/delalamo/af2_conformations.

The following data sets were generated

Article and author information

Author details

  1. Diego del Alamo

    Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Davide Sala

    Department of Chemistry, Vanderbilt University, Nashville, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3900-0011
  3. Hassane S Mchaourab

    Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, United States
    For correspondence
    hassane.mchaourab@vanderbilt.edu
    Competing interests
    The authors declare that no competing interests exist.
  4. Jens Meiler

    Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, United States
    For correspondence
    jens.meiler@vanderbilt.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8945-193X

Funding

National Institutes of Health (GM 128087)

  • Hassane S Mchaourab

Deutsche Forschungsgemeinschaft (CRC 1423,project number 421152132,subproject Z04)

  • Jens Meiler

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

Reviewing Editor

  1. Janice L Robertson, Washington University in St Louis, United States

Version history

  1. Preprint posted: November 22, 2021 (view preprint)
  2. Received: November 22, 2021
  3. Accepted: March 2, 2022
  4. Accepted Manuscript published: March 3, 2022 (version 1)
  5. Version of Record published: April 21, 2022 (version 2)

Copyright

© 2022, del Alamo 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.

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  1. Diego del Alamo
  2. Davide Sala
  3. Hassane S Mchaourab
  4. Jens Meiler
(2022)
Sampling alternative conformational states of transporters and receptors with AlphaFold2
eLife 11:e75751.
https://doi.org/10.7554/eLife.75751

Share this article

https://doi.org/10.7554/eLife.75751

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