DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design

TL;DR

DS-ProGen is a novel dual-structure deep language model that integrates backbone and surface features to improve functional protein sequence design, achieving state-of-the-art accuracy and interaction prediction.

Contribution

It introduces a dual-structure model combining backbone and surface information for enhanced protein design, a novel approach in the field.

Findings

Achieves 61.47% recovery rate on PRIDE dataset.

Outperforms existing methods in predicting protein interactions.

Generates structurally stable and functionally relevant sequences.

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.