ProDualNet: dual-target protein sequence design method based on protein language model and structure model
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Abstract
Proteins typically interact with multiple partners to regulate biological processes, and peptide drugs targeting multiple receptors have shown strong therapeutic potential, emphasizing the need for multi-target strategies in protein design. However, most current protein sequence design methods focus on interactions with a single receptor, often neglecting the complexity of designing proteins that can bind to two distinct receptors. We introduced Protein Dual-Target Design Network (ProDualNet), a structure-based sequence design method that integrates sequence-structure information from two receptors to design dual-target protein sequences. ProDualNet used a heterogeneous graph network for pretraining and combines noise-augmented single-target data with real dual-target data for fine-tuning. This approach addressed the challenge of limited dual-target protein experimental structures. The efficacy of ProDualNet has been validated across multiple test sets, demonstrating better recovery and success rates compared to other multi-state design methods. In silico evaluation of cases like dual-target allosteric binding and non-overlapping interface binding highlights its potential for designing dual-target binding proteins. Data and code are available at https://github.com/chengliu97/ProDualNet.
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- Authors
- Cheng, L., Wei, T., Cui, X., Chen, H.-F., & Yu, Z.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Intelligent Medicine Original (Shanghai) Co., Ltd. , Shanghai ,
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai ,
- Corresponding author
- Yu, Zhangsheng
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-07
- Volume
- 26
- Issue
- 4
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC12378908
- OpenAlex
- W4413970867
- Cited by
- 1
- References
- 10
- Citations by year
- 2026: 1
- FWCI
- 0.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Medical Engineering Cross Fund of Shanghai Jiao Tong University (YG2023ZD21)
- Shanghai Science and Technology Commission (YG2023LC03, 23DZ2290600, 23XD1401900, 21ZR1436300, 24JS2810200)
- National Natural Science Foundation of China (32171242, 12171318)
- National Key Research and Development Program of China (2023YFF1205102, 2025YFA0921001)
- SJTU Kunpeng & Ascend Center of Excellence, the Center for HPC at Shanghai Jiao Tong University
- Shanghai Municipal Science and Technology Major Project
- 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
- protein sequence design, structure-based design, protein language models, heterogeneous graph neural networks, allosteric binding
- MeSH terms
- Algorithms, Amino Acid Sequence, Humans, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Software, Computational Biology
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