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Sampling the conformational landscapes of transporters and receptors with AlphaFold2

View ORCID ProfileDiego del Alamo, Davide Sala, Hassane S. Mchaourab, View ORCID ProfileJens Meiler
doi: https://doi.org/10.1101/2021.11.22.469536
Diego del Alamo
1Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
2Department of Chemistry, Vanderbilt University, Nashville, TN, USA
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  • ORCID record for Diego del Alamo
Davide Sala
3Institute for Drug Discovery, Leipzig University, Leipzig, DE
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Hassane S. Mchaourab
1Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
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  • For correspondence: hassane.mchaourab@vanderbilt.edu jens@meilerlab.org
Jens Meiler
2Department of Chemistry, Vanderbilt University, Nashville, TN, USA
3Institute for Drug Discovery, Leipzig University, Leipzig, DE
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  • ORCID record for Jens Meiler
  • For correspondence: hassane.mchaourab@vanderbilt.edu jens@meilerlab.org
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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 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) led to the generation of accurate models in multiple conformations. In our benchmark, these conformations were observed to span the range between two experimental structures of interest, suggesting that our protocol allows sampling of the conformational landscape at the energy minimum. Nevertheless, our results also highlight the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/delalamo/af2_conformations

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 22, 2021.
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Sampling the conformational landscapes of transporters and receptors with AlphaFold2
Diego del Alamo, Davide Sala, Hassane S. Mchaourab, Jens Meiler
bioRxiv 2021.11.22.469536; doi: https://doi.org/10.1101/2021.11.22.469536
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Sampling the conformational landscapes of transporters and receptors with AlphaFold2
Diego del Alamo, Davide Sala, Hassane S. Mchaourab, Jens Meiler
bioRxiv 2021.11.22.469536; doi: https://doi.org/10.1101/2021.11.22.469536

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