Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters.
Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters.
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DOI：
10.1371/journal.pone.0270128
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发表时间：
2022
期刊：
影响因子：
3.7
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中科院分区：
文献类型：
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法尼基转移酶 (FTase) 进行的蛋白质异戊二烯化通常被描述为靶向含半胱氨酸的基序 (CaaX)，该基序在 a1 和 a2 位点富含脂肪族氨基酸，而在 X 位点则非常灵活。尽管有新的证据表明 FTase 具有比之前认为的更广泛的靶标特异性，但异戊二烯化预测方法通常依赖于这些特征。本研究使用基于规范（异戊二烯化、蛋白水解和羧甲基化）和最近确定的分流基序（仅异戊二烯化）的机器学习方法和训练集，旨在改进异戊二烯化预测，目标是确定 8000 个可能的 Cxxx 序列组合中异戊二烯化潜力的全部范围。此外，本研究旨在将异戊二烯化序列细分为分流（即未切割）或切割（即规范）。确定了酿酒酵母 FTase 的预测，并与使用当前可用的异戊二烯化预测方法得出的结果进行比较。使用与两个酵母报告基因（酵母交配信息素 a 因子和 Hsp40 Ydj1p）耦合的体内方法进一步评估了计算机预测，它们分别代表具有规范和分流 CaaX 基序的蛋白质。我们基于机器学习的方法扩展了预测 FTase 目标的范围，并提供了功能分类的框架。
Protein prenylation by farnesyltransferase (FTase) is often described as the targeting of a cysteine-containing motif (CaaX) that is enriched for aliphatic amino acids at the a1 and a2 positions, while quite flexible at the X position. Prenylation prediction methods often rely on these features despite emerging evidence that FTase has broader target specificity than previously considered. Using a machine learning approach and training sets based on canonical (prenylated, proteolyzed, and carboxymethylated) and recently identified shunted motifs (prenylation only), this study aims to improve prenylation predictions with the goal of determining the full scope of prenylation potential among the 8000 possible Cxxx sequence combinations. Further, this study aims to subdivide the prenylated sequences as either shunted (i.e., uncleaved) or cleaved (i.e., canonical). Predictions were determined for Saccharomyces cerevisiae FTase and compared to results derived using currently available prenylation prediction methods. In silico predictions were further evaluated using in vivo methods coupled to two yeast reporters, the yeast mating pheromone a-factor and Hsp40 Ydj1p, that represent proteins with canonical and shunted CaaX motifs, respectively. Our machine learning-based approach expands the repertoire of predicted FTase targets and provides a framework for functional classification.