S‐PLM: Structure‐Aware Protein Language Model via Contrastive Learning Between Sequence and Structure
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Abstract
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein functions and the design of proteins with the desired functions. The prediction and design capacity of PLMs relies on the representation gained from the protein sequences. However, the lack of crucial 3D structure information in most PLMs restricts the prediction capacity of PLMs in various applications, especially those heavily dependent on 3D structures. To address this issue, S‐PLM is introduced as a 3D structure‐aware PLM that utilizes multi‐view contrastive learning to align the sequence and 3D structure of a protein in a coordinated latent space. S‐PLM applies Swin‐Transformer on AlphaFold‐predicted protein structures to embed the structural information and fuses it into sequence‐based embedding from ESM2. Additionally, a library of lightweight tuning tools is provided to adapt S‐PLM for diverse downstream protein prediction tasks. The results demonstrate S‐PLM's superior performance over sequence‐only PLMs on all protein clustering and classification tasks, achieving competitiveness comparable to state‐of‐the‐art methods requiring both sequence and structure inputs. S‐PLM and its lightweight tuning tools are available at https://github.com/duolinwang/S-PLM/ .
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- Authors
- Wang, D., Pourmirzaei, M., Abbas, U. L., Zeng, S., Manshour, N., Esmaili, F., Poudel, B., Jiang, Y., Shao, Q., Chen, J., & Xu, D.
- Affiliations
- University of Missouri (US)
- University of Kentucky (US)
- University of Alabama at Birmingham (US)
- Corresponding author
- Shao, Qing
- Chen, Jin
- Xu, Dong
- ORCID
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2024-12-12
- Volume
- 12
- Issue
- 5
- Pages
- e2404212-e2404212
- ISSN
- 2198-3844 (ISSN-L)
- PMC
- PMC11791933
- OpenAlex
- W4405330335
- Cited by
- 46
- References
- 10
- Citations by year
- 2026: 17 · 2025: 19 · 2024: 10
- FWCI
- 6.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Advanced Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (R01LM014510)
- National Science Foundation (#2138259)
- National Centre for Supercomputing Applications (CIS230053)
- Foundation for the National Institutes of Health (R35GM126985)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein language models, sequence-structure alignment, multi-view contrastive learning, Swin Transformer, ESM-2, AlphaFold structures, protein clustering, protein classification, protein function prediction, protein design
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