De novo功能蛋白序列生成:通过再生和大型语言模型克服数据稀缺性
Chenyu Ren1, Daihai He1, Jian Huang1,2
1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
Briefings in bioinformatics
|March 8, 2026
概括
这项研究介绍了ProteinRG,这是一种用于设计功能蛋白序列的新型层次模型. 蛋白RG有效地产生多样化和准确的蛋白质序列,即使数据有限,超越现有的生成模型.
科学领域:
- 生物化学和分子生物学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质的工程.
背景情况:
- 蛋白质对生命至关重要,在医学和材料方面具有广泛的应用.
- 蛋白质的设计,特别是氨基酸序列的设计,对于利用蛋白质的潜力至关重要.
- 深度生成模型对蛋白质序列设计有希望,但需要大量的数据.
研究的目的:
- 开发一种新的等级模型,ProteinRG,用于生成功能蛋白序列.
- 为了应对功能蛋白序列数据有限的挑战,用于训练生成模型.
- 使用相对较小的数据集,实现高效的蛋白质设计.
主要方法:
- 蛋白质RG采用分层方法,首先生成蛋白质序列表示.
- 它利用现有的大型蛋白质序列模型进行初始表示.
- 然后,该模型根据这种表示生成一个功能性蛋白质序列.
主要成果:
- 生成的蛋白质序列与原始序列具有相似性和功能一致性.
- 使用多个序列对齐,t-SNE分析和3D结构预测的评估支持了这些发现.
- 与蛋白质序列生成中的其他生成模型相比,ProteinRG表现出卓越的性能.
结论:
- ProteinRG提供了一种有效的解决方案,用于从有限的数据中生成功能蛋白序列.
- 该模型成功地保持了序列相似性和功能完整性.
- 蛋白质RG代表了蛋白质设计的生成模型的重大进步.
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