HD-Prot: New Protein Language Model Integrates Sequence and Structure Data Using Continuous Tokens
Researchers have developed HD-Prot, a protein language model that combines discrete sequence information with continuous structural data without losing fine-grained details. The model uses a hybrid diffusion approach to handle both types of information simultaneously, avoiding the information loss that occurs when structures are discretized. This advancement could improve protein design and prediction tasks while requiring significantly fewer computational resources than comparable methods.
HD-Prot is a hybrid diffusion protein language model designed to address a key limitation in current protein modeling: the loss of structural information when continuous protein structures are converted into discrete tokens. The model extends traditional sequence-based protein language models by embedding a continuous-valued diffusion head that processes both discrete sequence tokens and high-fidelity structure latents. Through an absorbing diffusion process, HD-Prot captures dependencies between sequence and structure modalities, using categorical prediction for sequences and continuous diffusion for structures. Experimental results show the model performs competitively across multiple tasks including unconditional sequence-structure co-generation, motif-scaffolding, protein structure prediction, and inverse folding. Notably, HD-Prot achieves performance comparable to state-of-the-art multimodal protein language models while using less than one-tenth the computational resources typically required for modality extension fine-tuning.
What's missing
The study does not discuss potential limitations of the continuous diffusion approach, computational scalability to larger protein complexes, or how performance compares to non-diffusion-based continuous token methods. The paper also does not address generalization to novel protein folds or out-of-distribution sequences.
What different sources said
- arXiv cs.AICenter
HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens
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