ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding

• URL: http://arxiv.org/abs/2408.11363v1
• Date: Wed, 21 Aug 2024 06:16:22 GMT
• Title: ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
• Authors: Yijia Xiao, Edward Sun, Yiqiao Jin, Qifan Wang, Wei Wang,
• Abstract summary: We introduce ProteinGPT, a state-of-the-art multi-modal protein chat system. ProteinGPT seamlessly integrates protein sequence and structure encoders with linear projection layers for precise representation adaptation. We train a large-scale dataset of 132,092 proteins with annotations, and optimize the instruction-tuning process using GPT-4o. Experiments show that ProteinGPT can produce promising responses to proteins and their corresponding questions.
• Score: 22.610060675922536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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