DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design
Li, Yanting Wang, Zikang Jiang, Jiyue Lin, Ziqian He, Dongchen Shan, Yuheng Shao, Yanruisheng Li, Jiayi Shi, Xiangyu Wang, Jiuming
Li, Yanting
Wang, Zikang
Jiang, Jiyue
Lin, Ziqian
He, Dongchen
Shan, Yuheng
Shao, Yanruisheng
Li, Jiayi
Shi, Xiangyu
Wang, Jiuming
Supervisor
Department
Computational Biology
Embargo End Date
Type
Conference proceeding
Date
License
Language
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Inverse Protein Folding (IPF) is a critical subtask in the field of protein design, aiming to engineer amino acid sequences capable of folding correctly into a specified three-dimensional (3D) conformation. Although substantial progress has been achieved in recent years, existing methods generally rely on either backbone coordinates or molecular surface features alone, which restricts their ability to fully capture the complex chemical and geometric constraints necessary for precise sequence prediction. To address this limitation, we present DS-ProGen, a dual-structure deep language model for functional protein design, which integrates both backbone geometry and surface-level representations. By incorporating backbone coordinates as well as surface chemical and geometric descriptors into a next-amino-acid prediction paradigm, DS-ProGen is able to generate functionally relevant and structurally stable sequences while satisfying both global and local conformational constraints. On the PRIDE dataset, DS-ProGen attains the current state-of-the-art recovery rate of 61.47%, demonstrating the synergistic advantage of multi-modal structural encoding in protein design. Furthermore, DS-ProGen excels in predicting interactions with a variety of biological partners, including ligands, ions, and RNA, confirming its robust functional retention capabilities.
Citation
Y. Li, Z. Wang, J. Jiang, Z. Lin, D. He, Y. Shan , et al., "DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design," 2026, pp. 712-720.
Source
Proceedings of the AAAI Conference on Artificial Intelligence
Conference
Fortieth AAAI Conference on Artificial Intelligence
Keywords
31 Biological Sciences, 3101 Biochemistry and Cell Biology, 3102 Bioinformatics and Computational Biology, 33 Built Environment and Design, 3303 Design
Subjects
Source
Fortieth AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence