Modeling conformational states of proteins with AlphaFold
Open accessAbstract
Many proteins exert their function by switching among different structures. Knowing the conformational ensembles affiliated with these states is critical to elucidate key mechanistic aspects that govern protein function. While experimental determination efforts are still bottlenecked by cost, time, and technical challenges, the machine-learning technology AlphaFold showed near experimental accuracy in predicting the three-dimensional structure of monomeric proteins. However, an AlphaFold ensemble of models usually represents a single conformational state with minimal structural heterogeneity. Consequently, several pipelines have been proposed to either expand the structural breadth of an ensemble or bias the prediction toward a desired conformational state. Here, we analyze how those pipelines work, what they can and cannot predict, and future directions.
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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 [104]
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- Authors
- Sala, D., Engelberger, F., Mchaourab, H. S., & Meiler, J.
- Affiliations
- Leipzig University (DE)
- Vanderbilt University (US)
- Corresponding author
- Meiler, J.
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 81
- Pages
- 102645
- ISSN
- 0959-440X (ISSN-L)
- OpenAlex
- W4382601731
- Cited by
- 182
- References
- 21
- Citations by year
- 2026: 46 · 2025: 51 · 2024: 68 · 2023: 17
- FWCI
- 4.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 333 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Current Opinion in Structural Biology (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- Alexander von Humboldt-Stiftung (SPP 2363)
- Deutsche Forschungsgemeinschaft (421152132, CRC 1423)
- National Institute of General Medical Sciences (R01 GM080403)
- NIH (R01 HL122010, R01 DA046138)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- AlphaFold, protein conformational ensembles, conformational states, protein structural heterogeneity, conformational bias, protein structure prediction, protein function
- MeSH terms
- Protein Conformation, Proteins
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