ConforFold recovers alternative protein conformations beyond MSA subsampling
Open accessPublished version of a preprint
Abstract
Conformational changes underlie many aspects of protein function, yet current structure prediction tools remain limited in their ability to systematically sample structural ensembles. Here, we present ConforPSSP and ConforFold, a combined framework that integrates secondary‐structure sampling into a deep learning‐based prediction to recover multiple protein conformational states. ConforPSSP employs a transformer model trained on multi‐residue fragments to generate diverse 8‐state protein secondary structure predictions (PSSPs), which are then used to condition a retrained OpenFold model (ConforFold). ConforFold achieved state‐of‐the‐art performance in conformer recovery. On our test dataset of protein samples with two alternative conformations, it correctly identified both conformers in 84% of cases at TM‐scores ≥0.8, outperforming AlphaFlow (75.4%), which uses diffusion‐based sampling, and Cfold, which relies on MSA clustering. It outperformed BioEmu, a novel method that emulates MD simulation results, in cases where secondary structures between conformers differ significantly (83% and 76% of cases for ConforFold and BioEmu, respectively). These results establish ConforFold as a broadly applicable framework for modeling structural ensembles. It recovers conformations inaccessible to MSA‐based subsampling or diffusion models by explicitly integrating secondary structure, offering a new avenue for investigating conformational heterogeneity, mechanistic transitions, and the structural basis of protein function.
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Cites
Highly accurate protein structure prediction with AlphaFold
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Accurate structure prediction of biomolecular interactions with AlphaFold 3
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- Authors
- Syrlybaeva, R., & Strauch, E.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Strauch, Eva‐Maria
- ORCID
- Strauch, Eva‐Maria
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2026-04-09
- Volume
- 35
- Issue
- 5
- Pages
- e70564-e70564
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- OpenAlex
- W7152457382
- Cited by
- 2
- References
- 10
- Citations by year
- 2026: 2
- FWCI
- 4.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Protein Science (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1r01ai180127-01a1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein conformational ensembles, secondary structure prediction, protein structure prediction, transformer models, conformational heterogeneity, protein folding
- MeSH terms
- Deep Learning, Protein Conformation, Proteins, Software, Protein Structure, Secondary, Computational Biology, Molecular Dynamics Simulation
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