Impact of protein conformational diversity on AlphaFold predictions
Open accessPublished version of a preprint
Abstract
MOTIVATION: After the outstanding breakthrough of AlphaFold in predicting protein 3D models, new questions appeared and remain unanswered. The ensemble nature of proteins, for example, challenges the structural prediction methods because the models should represent a set of conformers instead of single structures. The evolutionary and structural features captured by effective deep learning techniques may unveil the information to generate several diverse conformations from a single sequence. Here, we address the performance of AlphaFold2 predictions obtained through ColabFold under this ensemble paradigm. RESULTS: Using a curated collection of apo-holo pairs of conformers, we found that AlphaFold2 predicts the holo form of a protein in ∼70% of the cases, being unable to reproduce the observed conformational diversity with the same error for both conformers. More importantly, we found that AlphaFold2's performance worsens with the increasing conformational diversity of the studied protein. This impairment is related to the heterogeneity in the degree of conformational diversity found between different members of the homologous family of the protein under study. Finally, we found that main-chain flexibility associated with apo-holo pairs of conformers negatively correlates with the predicted local model quality score plDDT, indicating that plDDT values in a single 3D model could be used to infer local conformational changes linked to ligand binding transitions. AVAILABILITY AND IMPLEMENTATION: Data and code used in this manuscript are publicly available at https://gitlab.com/sbgunq/publications/af2confdiv-oct2021. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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It is not immediately clear from pLDDT or PAE whether an AF2 model resembles the apo or holo form.
“whether the modeled structure resembles the apo or holo form of the protein is not immediately clear from analysis of pLDDT scores or PAE graphs32”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [32]
The standard AlphaFold implementation detects only a single structural snapshot, the ground state, likely because its training set lacks a large set of redundant protein conformers.
“The standard implementation of AlphaFold performs well only in detecting a single structural snapshot (the “ground state” structure), likely due to the lack of a large set of redundant protein conformers in the training set16,32.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [32]
AF2 has been demonstrated to fail in predicting multiple structures of metamorphic proteins, proteins with apo/holo conformational changes and other multi-state proteins using its default settings.
“yet AF2 has been demonstrated to fail in predicting multiple structures of metamorphic proteins11, proteins with apo/holo conformational changes12 and other multi-state proteins13 using its default settings.”
From Predicting multiple conformations via sequence clustering and AlphaFold2 (2024) · cites this paper as [12]
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- Authors
- Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., García Melani, A., Novomisky Nechcoff, J., Salas, M. N., Peters, T., Demitroff, N., Fernandez Alberti, S., Palopoli, N., Fornasari, M. S., & Parisi, G.
- Affiliations
- National University of Quilmes (AR)
- Independent Researcher , 31400 Toulouse, France
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes , Bernal, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- IMBICE (CONICET – UNLP), Laboratorio de Electrofisiología , La Plata, Argentina
- Fundación Instituto Leloir (AR)
- Corresponding author
- Parisi, Gustavo
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-04-05
- Volume
- 38
- Issue
- 10
- Pages
- 2742-2748
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W4225552973
- Cited by
- 196
- References
- 55
- Citations by year
- 2026: 45 · 2025: 54 · 2024: 52 · 2023: 29 · 2022: 16
- FWCI
- 12.32
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Greater South Information System
- Funders
- Universidad Nacional de Quilmes (PUNQ 1309/19)
- Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (PICT-2018 3457)
- Consejo Nacional de Investigaciones Científicas y Técnicas (PIP-2015-2017 11220150100853CO)
- European Union's Horizon 2020 Research and Innovation Staff Exchange Program (778247, 823886)
- CONICET
- 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
- protein conformational diversity, AlphaFold2, ColabFold, protein ensembles, ligand binding, pLDDT, model quality assessment, homologous protein families, protein structure prediction
- MeSH terms
- Protein Binding, Protein Conformation, Proteins
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