Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties
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Abstract
Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high-accuracy single-chain modeling. Since then, different customizations have expanded the number of conformational states accessible through AF2. Here, we further expanded AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in an automatic fashion.
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Well-folded globular proteins such as GPCRs and kinases sample a wide range of conformations to carry out their biological function.
“Apart from IDPs, well-folded globular proteins, such as G-protein coupled receptors and kinases, also sample a wide range of conformations to carry out their biological function102,103.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [102]
Several groups have extended AlphaFold to predict structural ensembles and excited-state structures.
“Thus, several groups have worked to extend AlphaFold to include predictions of structural ensembles and excited state structures102,104.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [102]
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- Authors
- Sala, D., Hildebrand, P. W., & Meiler, J.
- Affiliations
- Leipzig University (DE)
- Vanderbilt University (US)
- Corresponding author
- Sala, Davide
- Meiler, Jens
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2023-02-16
- Volume
- 10
- Pages
- 1121962-1121962
- ISSN
- 2296-889X (ISSN-L)
- PMC
- PMC9978208
- OpenAlex
- W4321090885
- Cited by
- 80
- References
- 32
- Citations by year
- 2026: 17 · 2025: 13 · 2024: 39 · 2023: 11
- FWCI
- 7.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Molecular Biosciences (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deutsche Forschungsgemeinschaft (CRC 1423, 421152132)
- Alexander von Humboldt-Stiftung
- National Institutes of Health (NIGMS R01 GM080403 NIHL R01 HL122010 NIDA R01 DA046138)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- AlphaFold2, G protein-coupled receptors, kinases, template selection, protein structure prediction, integrative structural biology, drug discovery targets
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