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PreprintCited by 10Protein Structure and DynamicsReceptor Mechanisms and SignalingComputational Drug Discovery Methods

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Coevolution-driven method for efficiently simulating conformational changes in proteins reveals molecular details of ligand effects in the β2AR receptor

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Mitrovic, Darko, Chen, Yue, Marciniak, Antoni, Delemotte, Lucie·2023·The Journal of Physical Chemistry B
DOI 10.1101/2023.07.20.549854PMC PMC10683026

Preprint — published as 10.1021/acs.jpcb.3c04897 · Paper page

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Abstract

With the advent of AI-powered structure prediction, the scientific community is inching ever closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably and deterministically predict alternative conformational states that are crucial for the function of e.g. transporters, receptors or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds the relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the β2 adrenergic receptor conformational sampling. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein’s conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the β2 adrenergic receptor activation, and that the full conformational landscape, including states related to biased signaling, is discovered using this procedure. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands.

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Metadata

Authors
Mitrovic, D., Chen, Y., Marciniak, A., & Delemotte, L.
Affiliations
Science for Life Laboratory (SE)
Corresponding author
  • Mitrovic, Darko
  • Delemotte, Lucie
ORCID
Venue
The Journal of Physical Chemistry B
Publisher
openRxiv
Published
2023-07-21
Volume
127
Issue
46
Pages
9891-9904
ISSN
1520-5207 (ISSN-L)
DOI
10.1101/2023.07.20.549854
PMC
PMC10683026
OpenAlex
W4388574218
Cited by
10
References
2
Citations by year
2026: 4 · 2025: 5 · 2024: 1
FWCI
0.89
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • The Journal of Physical Chemistry B (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • European High Performance Computing Joint Undertaking
  • Göran Gustafssons Stiftelser
  • Swedish e-Science Research Centre
  • Partnership for Advanced Computing in Europe AISBL
  • Knut och Alice Wallenbergs Stiftelse (2019 . 0130)
  • Vetenskapsrådet (2019-02433)
  • China Scholarship Council
  • Science for Life Laboratory
  • Centro Svizzero di Calcolo Scientifico
  • National Supercomputing Centre Singapore
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords
beta2-adrenergic receptor, protein conformational change, coevolution, conformational sampling, free energy landscape, ligand effects, receptor activation, metastable states, membrane protein dynamics, machine learning, protein evolution, receptor inactivation
MeSH terms
Ligands, Membrane Proteins, Molecular Conformation, Protein Conformation, Receptors, Adrenergic, Receptors, Adrenergic, beta-2

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