通过掩面语言建模和转移学习来预测RiPP生物合成酶的基质
Joseph D Clark1, Xuenan Mi2, Douglas A Mitchell3,4
1School of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign Urbana IL 61801 USA.
Digital discovery
|December 9, 2024
概括
大型语言模型有效地预测了核糖体合成和翻译后修饰 (RiPP) 生物合成中的酶特异性. 一个单一的高质量数据集改善了对不同酶的预测,有助于路径设计.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 合成生物学 合成生物学
背景情况:
- 核糖体合成和翻译后改性 (RiPPs) 是一类多样化的天然产品.
- 预测RiPP生物合成酶的基质特异性是具有挑战性的,因为复杂的,不可还原的规则.
- 大型语言模型 (LLM) 显示出预测酶特异性的前景,但需要广泛的训练数据.
研究的目的:
- 评估LLM在预测RiPP生物合成酶的基质偏好方面的有效性.
- 评估相同路径内的不同酶之间学习的功能形式的可转移性.
- 为了确定单一的高质量数据集是否可以改善对不同酶的数据稀缺场景中的预测.
主要方法:
- 蒙面语言建模 (MLM) 应用于对LazBF和LazDEF酶的基质偏好进行分析.
- 从基板数据生成的LLM嵌入被用于下游预测任务.
- 模型在特定数据集上进行了微调,以评估可解释性和促进路径设计.
主要成果:
- 多媒体营销嵌入改善了LazBF和LazDEF的基质预测,证明了可转移性.
- 一个单一的高质量的数据集显著提高了对不同酶的基质预测,即使在数据稀缺的条件下.
- 微调提供了可解释的洞察力,这对于设计兼容的基板库至关重要.
结论:
- 实际上,LLM可以有效地学习和传输RiPP生物合成酶之间的功能信息.
- 高质量的基质数据对于改善酶特异性预测是有价值的,特别是在数据有限的情况下.
- 这种方法促进了RiPP生物合成途径和基质库的合理设计.
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