DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design
Journal:
arXiv
Published Date:
May 18, 2025
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.
