A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

IF 1.9 3区 化学 Q3 BIOCHEMICAL RESEARCH METHODS
Dylan H. Ross, Erin L. Bredeweg, Josie G. Eder, Daniel J. Orton, Meagan C. Burnet, Jennifer E. Kyle, Ernesto S. Nakayasu, Xueyun Zheng
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引用次数: 0

Abstract

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.

Abstract Image

LipidOz 中以深度学习为指导的自动工作流程,通过臭氧分解法详细表征真菌脂肪酸的不饱和度。
了解真菌脂质生物学和代谢对于发现抗真菌靶标至关重要,因为脂质在细胞过程中发挥着核心作用。脂质结构差异的细微差别会对其功能产生重大影响,因此有必要对脂质进行详细表征,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,只能通过一些专门的分析技术来确定。臭氧诱导解离质谱法(OzID-MS)就是这样一种技术,它利用臭氧破坏脂质双键,产生成对的特征碎片,从而确定双键的位置。在这项工作中,我们应用 OzID-MS 和 LipidOz 软件来分析用不同的组织胞浆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从这一大型数据集中确定 DB 位置变得更加实用,但对所有目标进行人工验证仍然非常耗时。DL 模型减少了数据分析中的人工参与,但由于该模型是使用哺乳动物脂质提取物训练的,因此对酵母衍生数据的预测准确性有所降低。为了解决这两个缺点,我们重新训练了 DL 模型,使其成为一个预过滤器,为自动分析优先选择目标,提供可靠的人工验证结果,但需要的计算时间和人工工作量更少。我们的工作流程确定了详细的 DB 位置和酶特异性。
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来源期刊
Journal of Mass Spectrometry
Journal of Mass Spectrometry 化学-光谱学
CiteScore
5.10
自引率
0.00%
发文量
84
审稿时长
1.5 months
期刊介绍: The Journal of Mass Spectrometry publishes papers on a broad range of topics of interest to scientists working in both fundamental and applied areas involving the study of gaseous ions. The aim of JMS is to serve the scientific community with information provided and arranged to help senior investigators to better stay abreast of new discoveries and studies in their own field, to make them aware of events and developments in associated fields, and to provide students and newcomers the basic tools with which to learn fundamental and applied aspects of mass spectrometry.
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