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Impact of protein conformational diversity on AlphaFold predictions
Open accessPreprint — published as 10.1093/bioinformatics/btac202 · Paper page
Abstract
Abstract 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 under this ensemble paradigm. Using a curated collection of apo-holo conformations, 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 an equivalent error than in the estimation of a single conformation. 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.
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“Several preprints have provided evidence that AF2, despite its accuracy, likely does not learn the energy landscapes underpinning protein folding and function (Saldaño et al., 2021; Pak et al., 2021; Akdel et al., 2021).”
From Sampling alternative conformational states of transporters and receptors with AlphaFold2 (2022) · cites this paper as Saldaño et al., 2021
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Predicting multiple conformations via sequence clustering and AlphaFold2
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“In 67% of a dataset tested, AlphaFold prediction resembled holo form and the proteins were less predictable when the conformational differences between apo and holo forms increased (Saldaño et al., 2022).”
Background
Metadata
- Authors
- Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., Melani, A. G., 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)
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Fundación Instituto Leloir (AR)
- Corresponding author
- Parisi, Gustavo
- ORCID
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2021-10-28
- Volume
- 38
- Issue
- 10
- Pages
- 2742-2748
- ISSN
- 1367-4803 (ISSN-L)
- 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
- cc-by
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Greater South Information System
- Funders
- European Commission (HORIZON2020, 778247)
- Consejo Nacional de Investigaciones Científicas y Técnicas (PIP-2015-2017 11220150100853CO, PICT2018)
- Universidad Nacional de Quilmes (PUNQ 1309/19, 778247)
- Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (PICT-2018 3457)
- Agencia Nacional de Promoción Científica y Tecnológica (778247, CONICET)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › 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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