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SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2
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Abstract
The unprecedented performance of Deepmind's Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes and protein sequence repositories is reshaping structural biology. However, because this database returns a single structure, it brought into question Alphafold's ability to capture the intrinsic conformational flexibility of proteins. Here we present a general approach to drive Alphafold2 to model alternate protein conformations through simple manipulation of the multiple sequence alignment via in silico mutagenesis. The approach is grounded in the hypothesis that the multiple sequence alignment must also encode for protein structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations. A systematic modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.
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“follow-up studies suggest that the tool has potential for generation of biologically relevant ensembles,266”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [266]
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- Sergey Ovchinnikov UA [@sokrypton], 2021, In AF_advanced colabfold we suggest enabling dropout (is_training = True), and iterating through seeds to sample from the uncertainty. This should theoretically return multiple conformations if there is any ambiguity in coevolution, w/o the need to resample/subsample the MSA (1/3): https://t.co/MUtee9xIaX, In: Twitter
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Metadata
- Authors
- Stein, R. A., & Mchaourab, H. S.
- Affiliations
- Vanderbilt University (US)
- Corresponding author
- Stein, Richard A.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-08-22
- Volume
- 18
- Issue
- 8
- Pages
- e1010483
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC9436118
- OpenAlex
- W4292636432
- Cited by
- 258
- References
- 52
- Citations by year
- 2026: 55 · 2025: 64 · 2024: 76 · 2023: 51 · 2022: 12
- FWCI
- 16.65
- Type
- journal article
- Language
- en
- Update notices
- new_version (2022-09-01)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (GM128087, GM077659)
- 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
- AlphaFold2, protein conformational heterogeneity, protein ensembles, multiple sequence alignment, in silico mutagenesis, protein flexibility, membrane proteins, conformational landscape, structure-based drug design, protein structure prediction
- MeSH terms
- Amino Acid Sequence, Protein Conformation, Proteins, Drug Design, Sequence Alignment
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