ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding

Understanding biological processes, drug development, and biotechnological advancements requires detailed analysis of protein structures and sequences, a task in protein research that is inherently complex and time-consuming when performed manually. To streamline this process, we introduce ProteinGPT, a state-of-the-art multi-modal protein chat system, that allows users to upload protein sequences and/or structures for comprehensive protein analysis and responsive inquiries. ProteinGPT seamlessly integrates protein sequence and structure encoders with linear projection layers for precise representation adaptation, coupled with a large language model (LLM) to generate accurate and contextually relevant responses. To train ProteinGPT, we construct a large-scale dataset of 132,092 proteins with annotations, and optimize the instruction-tuning process using GPT-4o. This innovative system ensures accurate alignment between the user-uploaded data and prompts, simplifying protein analysis. Experiments show that ProteinGPT can produce promising responses to proteins and their corresponding questions.

@article{xiao2025_2408.11363,
  title={ ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding },
  author={ Yijia Xiao and Edward Sun and Yiqiao Jin and Qifan Wang and Wei Wang },
  journal={arXiv preprint arXiv:2408.11363},
  year={ 2025 }
}