Single amino acid substitution (SAAS) produces the most common variant of protein function change under physiological conditions. As the number of SAAS events in plants has increased exponentially, an effective prediction tool is required to help identify and distinguish functional SAASs from the whole genome as either potentially causal traits or as variants. Here, we constructed a plant SAAS database that stores 12 865 SAASs in 6172 proteins and developed a tool called Plant Protein Variation Effect Detector (PPVED) that predicts the effect of SAASs on protein function in plants. PPVED achieved an 87% predictive accuracy when applied to plant SAASs, an accuracy that was much higher than those from six human database software: SIFT, PROVEAN, PANTHER-PSEP, PhD-SNP, PolyPhen-2, and MutPred2. The predictive effect of six SAASs from three proteins in Arabidopsis and maize was validated with wet lab experiments, of which five substitution sites were accurately predicted. PPVED could facilitate the identification and characterization of genetic variants that explain observed phenotype variations in plants, contributing to solutions for challenges in functional genomics and systems biology. PPVED can be accessed under a CC-BY (4.0) license via http://www.ppved.org.cn.

中文翻译：

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PPVED：预测单个氨基酸替代对植物蛋白质功能影响的机器学习工具

单一氨基酸取代 (SAAS) 在生理条件下产生最常见的蛋白质功能变化变体。随着植物中 SAAS 事件的数量呈指数增长，需要一种有效的预测工具来帮助从整个基因组中识别和区分功能性 SAAS，作为潜在的因果性状或变异。在这里，我们构建了一个植物 SAAS 数据库，该数据库存储了 6172 种蛋白质中的 12865 个 SAAS，并开发了一种称为植物蛋白质变异效应检测器 (PPVED) 的工具，可以预测 SAAS 对植物蛋白质功能的影响。PPVED 在应用于植物 SAAS 时实现了 87% 的预测准确度，该准确度远高于六种人类数据库软件的准确度：SIFT、PROVEAN、PANTHER-PSEP、PhD-SNP、PolyPhen-2 和 MutPred2。来自三种蛋白质的六种 SAAS 的预测作用拟南芥和玉米通过湿实验室实验进行了验证，其中五个替代位点被准确预测。PPVED 可以促进对解释观察到的植物表型变异的遗传变异的识别和表征，有助于解决功能基因组学和系统生物学的挑战。PPVED 可通过 http://www.ppved.org.cn 在 CC-BY (4.0) 许可下访问。