TAILOR-MS, a Python Package that Deciphers Complex Triacylglycerol Fatty Acyl Structures: Applications for Bovine...

Area of Science:

• Biochemistry
• Analytical Chemistry
• Bioinformatics

Background:

• Triacylglycerol (TAG) profiling using liquid chromatography-tandem mass spectrometry (LC/MS) is crucial for understanding lipid metabolism.
• Identifying specific fatty acyl moieties within TAG species is challenging due to numerous combinations.
• Manual TAG structure determination is inefficient and prone to errors.

Purpose of the Study:

• To develop TAILOR-MS, a Python package for automated TAG structural determination and targeted LC/MS method generation.
• To assist in the efficient and accurate identification of TAG species in complex biological matrices.

Main Methods:

• Development of the TAILOR-MS Python package.
• Application of LC/MS for triacylglycerol profiling of bovine milk and infant formulas.
• Utilizing TAILOR-MS for automated TAG structural identification and prediction.

Main Results:

• TAILOR-MS significantly reduces TAG structural identification time from hours to seconds.
• Analysis of bovine milk identified 247 TAG species and predicted 317 more.
• Comprehensive TAG profiles were generated for infant formulas, identifying over 200 species.
• Dissimilarities in TAG composition between bovine milk and infant formulas were confirmed.

Conclusions:

• TAILOR-MS is a valuable tool for accelerating and enhancing the accuracy of targeted LC/MS-based TAG profiling.
• The package demonstrates robust performance in structural prediction, even with incomplete data.
• TAILOR-MS facilitates more comprehensive TAG analysis in various biological and food samples.