Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA.
Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA.
J Mass Spectrom. 2024 Sep;59(9):e5078. doi: 10.1002/jms.5078.
Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work, we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transformed with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produced. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of detailed DB positions and enzymatic specificity.
了解真菌脂质生物学和代谢对于抗真菌靶点的发现至关重要，因为脂质在细胞过程中起着核心作用。脂质结构差异的细微差别会显著影响其功能，因此有必要详细描述脂质以了解它们在这些复杂系统中的作用。特别是，脂质双键（DB）位置是脂质结构的一个重要组成部分，只能使用少数专门的分析技术来确定。臭氧诱导解离质谱（OzID-MS）就是这样一种技术，它使用臭氧来打断脂质的 DB，产生一对特征碎片，从而确定 DB 的位置。在这项工作中，我们应用 OzID-MS 和 LipidOz 软件来分析经过不同 Histoplasma capsulatum 脂肪酸去饱和酶转化的酿酒酵母菌株的复杂脂质，以确定产生的特定不饱和脂质。LipidOz 中的自动数据分析使得从这个大数据集中确定 DB 位置变得更加实用，但对所有目标的手动验证仍然很耗时。DL 模型减少了数据分析中的人工干预，但由于它是使用哺乳动物脂质提取物进行训练的，因此对酵母衍生数据的预测准确性降低了。我们通过重新训练 DL 模型作为自动分析的优先级预筛选器来解决这两个缺点，提供有信心的手动验证结果，但需要更少的计算时间和人工努力。我们的工作流程确定了详细的 DB 位置和酶特异性。
