ConoGPT:通过将二硫化物键信息纳入用于共毒素序列生成的蛋白质语言模型的微调.
Guohui Zhao1, Cheng Ge2, Wenzheng Han1
1College of Computer Science and Technology, Ocean University of China, Songling Road, Qingdao 266100, China.
Toxins
|February 25, 2025
概括
一个新的AI模型ConoGPT产生了具有二硫化物键信息的共毒素序列,克服了传统方法的局限性. 这种方法加速了针对尼古丁乙胆受体的新药候选物的发现.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 毒素是来自牛的有价值,具有治疗潜力.
- 传统的毒素生产是昂贵的,耗时的,并且限制了序列的多样性.
研究的目的:
- 开发一个人工智能模型 (ConoGPT) 以实现高效和多样化的共毒素序列生成.
- 将二硫化物键信息纳入人工智能模型,以改进毒素设计.
主要方法:
- 微调 ProtGPT2 模型与二硫化物键数据,以创建 ConoGPT.
- 使用分子对接和动力学模拟来评估生成的序列.
- 评估新型共毒毒素候选物的物理化学特性和结构完整性.
主要成果:
- ConoGPT产生具有真实的物理化学性质的共毒毒素序列.
- 在生成有序结构方面,ConoGPT的表现优于缺乏二硫化物键信息的模型.
- 生成的序列显示了与尼古丁乙胆受体 (nAChR) 的高结合亲和力.
- 分子动力学证实了设计的毒素的稳定性.
结论:
- ConoGPT提供了一种新的计算方法,用于孔毒素的设计和发现.
- 该模型促进了具有治疗潜力的功能性的生成.
- 这一策略增强了用于药物开发的共毒素序列空间的探索.
相关概念视频
Protein Folding
7.7K
Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
7.7K
Conserved Binding Sites
4.1K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.1K


