Conditional diffusion with locality-aware modal alignment for generating diverse protein conformational ensembles
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Abstract
Abstract Recent advances in AI have enabled the accurate prediction of a single stable protein structure solely based on its amino acid sequence. However, capturing the complete conformational landscape of a protein and its dynamic flexibility remains challenging. In this work, we developed Modal-aligned conditional Diffusion (Mac-Diff), a score based diffusion model for generating the conformational ensembles for unseen proteins. Central to Mac-Diff is an innovative attention module that enforces a delicate, locality-aware alignment between the conditional view (protein sequence) and the target view (residue pair geometry) to compute highly contextualized features for effective structural denoising. Furthermore, Mac-Diff leverages semantically rich sequence embedding from Protein Language Models like ESM-2 in enforcing the protein sequence condition that captures evolutionary, structural and functional information. This compensates for protein structural heterogeneity more effectively than embeddings from structure prediction models that are possibly biased to the dominant conformation. Mac-Diff showed promising results in generating realistic and diverse protein structures. It successfully recovered conformational distributions of fast folding proteins, captured multiple meta-stable conformations that were only observed in long MD simulation trajectories and efficiently predicted alternative conformations for allosteric proteins. We believe that Mac-Diff offers a useful tool to improve understanding of protein dynamics and structural variability, with broad implications for structural biology, drug discovery, and protein engineering.
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Lisanza, S. L., Gershon, J. M., Tipps, S. W. K., Sims, J. N., Arnoldt, L., Hendel, S. J., Simma, M. K., Liu, G., Yase, M., Wu, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02456-0 · Cited by 17 · see citing works
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SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
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Predicting protein interactions of the kinase Lck critical to T cell modulation
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SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models
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Structure prediction of alternative protein conformations
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Deep Generative Modeling of Protein Conformations: A Comprehensive Review
Dao, T. M., & Rahman, T. (2025) · BioChem · doi:10.3390/biochem5030032 · Cited by 5 · see citing works
Li, W., Cadet, X. F., Medina-Ortiz, D., Davari, M. D., Sowdhamini, R., Bessafi, M., Damour, C., Li, Y., Miranville, A., de Brevern, A. G., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27167151
Learning the reaction coordinate: collective variables from physical intuition to generative models
Talmazan, R. A., Chen, C. G., Tang, C., Megías, A., Arredondo, S. C., & Chipot, C. J. (2026) · Digital Discovery · doi:10.1039/d6dd00290k
Hao, P., Zhang, H., Zhang, C., Zeng, Y., Hua, J., Zhang, J., & Cui, S. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.182673
Self-citationRevealing protein structural transitions in the era of generative artificial intelligence
Hung, T. I., Venkatesan, R., & Chang, C. A. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103421
- Authors
- Wang, B., Wang, C., Chen, J., Liu, D., Sun, C., Zhang, J., Zhang, K., & Li, H.
- Affiliations
- East China University of Science and Technology (CN)
- East China Normal University (CN)
- Shanghai Medical College of Fudan University (CN)
- Venue
- Nature Machine Intelligence
- Published
- 2026-02-25
- Volume
- 8
- Issue
- 3
- Pages
- 415-434
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W7131351614
- Cited by
- 5
- References
- 98
- Citations by year
- 2026: 4 · 2025: 1
- FWCI
- 8.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- National Natural Science Foundation of China (82173690, 2022YFC3400501, 82 425 104, 62276099)
- National Key Research and Development Program of China (82173690, 2022YFC3400501)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- conditional diffusion models, protein conformational ensembles, modal alignment, protein structure generation, protein flexibility, structural heterogeneity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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