塑体和线粒体定位预测的可靠性随着训练集的进化距离增加而迅速下降
Sven B Gould1, Jonas Magiera1, Carolina García García1
1Institute for Molecular Evolution, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
PLoS computational biology
|November 11, 2024
概括
蛋白质局部化预测算法在各种物种中表现不佳. 精度随着进化的距离而下降,特别是植物线粒体,突出显示了对进化多样化的训练数据的需求.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 进化生物学 进化生物学
背景情况:
- 线粒体和塑体进口数千种蛋白质,需要实验性定位.
- 实验性蛋白质定位是资源密集的,有时是不可能的.
- 基于序列的预测算法被广泛使用,但它们的跨物种可靠性是未知的.
研究的目的:
- 评估常见的蛋白质定位预测算法 (TargetP,Localizer,WoLFPSORT) 的性能.
- 在进化多样化的真核生物中评估算法可靠性,包括四种具有可用的蛋白质组数据的光合作用物种.
- 识别限制预测准确性的因素.
主要方法:
- 使用Arabidopsis thaliana,Zea mays,Physcomitrium patens和Chlamydomonas reinhardtii的实验性蛋白质组数据对TargetP,Localizer和WoLFPSORT的性能评估.
- 使用正义学推断,将评估扩展到171个额外的真核生物.
- 进行了特异性,敏感性和精度分析.
主要成果:
- 预测准确度差异很大,从75%降至2%.
- 随着训练和查询物种之间的进化距离的增加,性能下降.
- 植物线粒体的预测特别差,接近随机;跨器官错误很常见.
结论:
- 目前的蛋白质定位预测算法在进化多样化的物种中缺乏可靠性.
- 细胞器的进化分歧是性能限制的主要原因.
- 未来的算法需要对进化多样化的细胞器蛋白进行培训,以提高准确性和可靠性.
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