Zhang, S.; Huang, T.; Chen, E.; Qing, R.

Post-translational modifications (PTMs) are critical for protein function, yet their precise design by harnessing site specific information derived from native proteins remains challenging. Here, we present a deep learning-based PTM design framework that integrates latent diffusion models with ControlNet for sequence generation with site-specific PTM-control. The framework incorporates a PTM-aware protein language model featuring extractor, trained on a curated SwissProt PTM dataset with specialized modification tokens. Through de novo generation of protein sequences with designated PTM sites, our framework facilitates the exploration of PTM-driven functional landscapes and advances position-aware protein engineering.