使用深度变换器蛋白语言模型识别蛋白质工程的有希望的序列
Trevor S Frisby1, Christopher James Langmead1
1Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Proteins
|June 20, 2023
概括
这项研究引入了一种新的承诺分数,来自深度变压器蛋白质语言模型,以有效地识别工程方面的有希望的蛋白质序列. 该得分有助于发现新型纳米体,并通过预测结合相互作用来优化现有蛋白质.
科学领域:
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
- 人工智能在生物化学中的应用
背景情况:
- 发现具有所需性质的新型蛋白序列是具有挑战性的,因为序列空间广.
- 对于纳米体发现和蛋白质优化等应用来说,识别有前途的序列往往是昂贵和耗时的.
研究的目的:
- 开发和验证基于深度学习的方法,以有效地识别高潜力的蛋白质序列.
- 引入"承诺分数"以指导蛋白质工程在序列发现和优化方面的努力.
主要方法:
- 利用深度变压蛋白语言模型及其自我注意力图来计算承诺分数.
- 将Promise Score应用于纳米体 (Nb) 发现和蛋白质优化工作流程.
- 分析了自我注意力图,以确定参与分子间相互作用的关键蛋白质区域.
- 探索了用于预测性质建模的蛋白质语言模型的微调策略.
主要成果:
- 答应分数有效地从纳米体谱中选择有希望的序.
- 承诺得分指导特定地点的突变发生实验,识别了高比例的改善的蛋白质序列.
- 自我注意地图提供了对驱动特定相互作用的蛋白质区域的洞察.
- 证明了微调蛋白质语言模型对目标蛋白质工程任务的有用性.
结论:
- 承诺分数提供了一个计算高效的方法来加速蛋白质工程.
- 深度学习模型,特别是基于变压器的语言模型,是预测和设计功能蛋白质的强大工具.
- 通过自我注意地图来了解蛋白质相互作用,可以增强设计过程.
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