Towards A Generative Protein Evolution Machine with DPLM-Evo

Xinyou Wang, Liang Hong, Jiasheng Ye, Zaixiang Zheng, Shujian Huang, Quanquan Gu

Keywords: evolutionary discrete diffusion model, protein sequence generation, protein sequence evolutionary modeling

Abstract:

Proteins are shaped by evolution under biophysical and functional constraints.
Protein language models can learn rich evolutionary constraints, and discrete diffusion-based PLMs (DPLMs) are promising for both understanding and generation.
However, existing DPLMs rely on masking-based diffusion, which is a loose proxy for evolution, and difficult to model the edit operations that drive sequence change in nature: substitutions and insertions/deletions (indels).
In this paper, we present DPLM-Evo, an evolutionay discrete diffusion protein language model that explicitly predicts substitution, insertion, and deletion actions during denoising.
To make indel-aware generation tractable, we introduce a latent alignment formulation that supports variable-length sequences.
To make substitution corruption informative, we propose a contextual evolutionary noising kernel that generates biologically plausible mutations.
On ProteinGym, DPLM-Evo achieves state-of-the-art mutation effect prediction in the single-sequence setting, and it enables variable-length generation and post-editing via explicit edit trajectories.

Presenter: ~Xinyou_Wang1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: No, the presenting author of this submission does not fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 89