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将定向进化与机器学习相结合,可以准确地预测基因型到表型
Alexander J Howard1, Ellen Y Rim1, Oscar D Garrett1
1Department of Plant Pathology and the Genome Center, University of California, Davis, CA, 95616, USA.
bioRxiv : the preprint server for biology
|February 20, 2025
概括
研究人员开发了一种使用定向进化和蛋白质语言建模的新方法,以了解大米免疫受体变体. 这种方法通过分析序列变异及其对蛋白质功能的影响来确定新的抗米疾病基因.
科学领域:
- 植物科学 植物科学
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 了解遗传变异和可观察的特征 (表型效应) 之间的关系对于有效利用大型基因组数据集至关重要.
- 像Pik-1这样的水免疫受体在植物对病原体的防御中起着至关重要的作用,但它们的自然变体通常会逃避检测特定的真菌因子.
研究的目的:
- 开发和应用一种结合定向进化和蛋白质语言建模的新方法,以表征大米免疫受体Pik-1的自然存在变体.
- 设计Pik-1变种,能够识别现有的Pik-1等位基因不针对的真菌蛋白 (Avr-PikC和Avr-PikF).
- 通过分析3000米基因组项目数据集中的序列变异来识别和验证大米中疾病耐药性的新来源.
主要方法:
- 使用高通量定向进化来设计Pik-1受体以结合和识别真菌蛋白Avr-PikC和Avr-PikF.
- 微调了一种蛋白质语言模型,使用定向进化数据来建立序列变异和联结能力之间的相关性.
- 利用受过训练的蛋白质语言模型来分析来自3000米基因组项目数据集的Pik-1变体.
主要成果:
- 成功设计了Pik-1变体,可以结合和识别真菌蛋白质Avr-PikC和Avr-PikF,这些蛋白质通常不会被当前的Pik-1等位基因识别.
- 蛋白质语言模型有效地将序列变异与改变的连接体结合行为相关联.
- 从3000米基因组项目中确定了两种Pik-1变异,它们对Avr-PikC具有很高的结合亲和力,通过体外分析证实了比野生型Pik-1更好的结合对象.
结论:
- 定向进化和蛋白质语言建模的结合方法是描述蛋白质变异及其功能影响的强大工具.
- 这种以机器学习为驱动的策略成功地识别了具有增强联体结合能力的新型大米免疫受体变体,代表了对疾病耐药性的有希望来源.
- 该方法对探索其他感兴趣的蛋白质的表型变异具有重大潜力,有助于作物改进和理解蛋白质演变.
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