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Updated: May 17, 2025

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A Practical Guide to Phylogenetics for Nonexperts
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使用贝叶斯流网络进行蛋白序列建模
Timothy Atkinson1, Thomas D Barrett2, Scott Cameron1
1InstaDeep, 5 Merchant Square, London, W2 1AY, England.
Nature communications
|April 3, 2025
概括
贝叶斯流网络 (BFNs) 能够实现先进的蛋白质序列生成. 我们的ProtBFN模型创造了多样化的,与自然类似的蛋白质序列,在无条件和条件任务中表现优于现有的方法.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 机器学习 机器学习
背景情况:
- 生成型机器学习已经推进了蛋白质序列建模.
- 现有的模型在无条件和有条件生成方面都在扎.
- 贝叶斯流网络 (BFNs) 为生成模型提供了一个新的框架.
研究的目的:
- 为蛋白质序列生成引入贝叶斯流网络 (BFNs).
- 开发和评估ProtBFN,一个用于蛋白质序列的大规模BFN模型.
- 评估BFNs在条件生成中的抗体设计能力.
主要方法:
- 开发了基于贝叶斯流网络的650M参数模型ProtBFN.
- 在UniProtKB的蛋白质序列上训练了ProtBFN.
- 在抗体重链上微调的ProtBFN,用于条件生成任务创建AbBFN.
主要成果:
- ProtBFN产生与自然相似的,多样化的,结构连贯的和新的蛋白质序列.
- ProtBFN显著优于领先的自回归和离散扩散模型.
- 与BERT型模型相比,抗体特定模型AbBFN显示出具有竞争力或优异的零射击条件生成.
结论:
- 贝叶斯流网络对于蛋白质序列生成非常有效.
- 在生成性蛋白质建模方面,ProtBFN代表了一项重大进展.
- 在抗体工程和治疗性蛋白质设计等专业应用中,BFN是有前途的.
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