Single-sequence protein structure prediction using supervised transformer protein language models
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Abstract
Significant progress has been made in protein structure prediction in recent years. However, it remains challenging for AlphaFold2 and other deep learning-based methods to predict protein structure with single-sequence input. Here we introduce trRosettaX-Single, an automated algorithm for single-sequence protein structure prediction. It incorporates the sequence embedding from a supervised transformer protein language model into a multi-scale network enhanced by knowledge distillation to predict inter-residue two-dimensional geometry, which is then used to reconstruct three-dimensional structures via energy minimization. Benchmark tests show that trRosettaX-Single outperforms AlphaFold2 and RoseTTAFold on orphan proteins and works well on human-designed proteins (with an average template modeling score (TM-score) of 0.79). An experimental test shows that the full trRosettaX-Single pipeline is two times faster than AlphaFold2, using much fewer computing resources (<10%). On 2,000 designed proteins from network hallucination, trRosettaX-Single generates structure models with high confidence. As a demonstration, trRosettaX-Single is applied to missense mutation analysis. These data suggest that trRosettaX-Single may find potential applications in protein design and related studies.
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- Authors
- Wang, W., Peng, Z., & Yang, J.
- Affiliations
- Nankai University (CN)
- Shandong University (CN)
- Corresponding author
- Yang, Jianyi
- Venue
- Nature Computational Science
- Published
- 2022-12-19
- Volume
- 2
- Issue
- 12
- Pages
- 804-814
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W4313430582
- Cited by
- 150
- References
- 13
- Citations by year
- 2026: 23 · 2025: 40 · 2024: 44 · 2023: 34 · 2022: 8
- FWCI
- 10.29
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Computational Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (T2222012, 61873185, T2225007, 11871290)
- 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
- knowledge distillation, multi-scale network, orphan proteins, protein design
- MeSH terms
- Algorithms, Humans, Language, Electric Power Supplies, Benchmarking, Distillation
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