Proteome-wide analysis of protein lipidation using chemical probes: in-gel fluorescence visualization, identification and quantification of N-myristoylation, N- and S-acylation, O-cholesterylation, S-farnesylation and S-geranylgeranylation.

Protein lipidation is one of the most widespread post-translational modifications (PTMs) found in nature, regulating protein function, structure and subcellular localization. Lipid transferases and their substrate proteins are also attracting increasing interest as drug targets because of their dysregulation in many disease states. However, the inherent hydrophobicity and potential dynamic nature of lipid modifications makes them notoriously challenging to detect by many analytical methods. Chemical proteomics provides a powerful approach to identify and quantify these diverse protein modifications by combining bespoke chemical tools for lipidated protein enrichment with quantitative mass spectrometry-based proteomics. Here, we report a robust and proteome-wide approach for the exploration of five major classes of protein lipidation in living cells, through the use of specific chemical probes for each lipid PTM. In-cell labeling of lipidated proteins is achieved by the metabolic incorporation of a lipid probe that mimics the specific natural lipid, concomitantly wielding an alkyne as a bio-orthogonal labeling tag. After incorporation, the chemically tagged proteins can be coupled to multifunctional 'capture reagents' by using click chemistry, allowing in-gel fluorescence visualization or enrichment via affinity handles for quantitative chemical proteomics based on label-free quantification (LFQ) or tandem mass-tag (TMT) approaches. In this protocol, we describe the application of lipid probes for N-myristoylation, N- and S-acylation, O-cholesterylation, S-farnesylation and S-geranylgeranylation in multiple cell lines to illustrate both the workflow and data obtained in these experiments. We provide detailed workflows for method optimization, sample preparation for chemical proteomics and data processing. A properly trained researcher (e.g., technician, graduate student or postdoc) can complete all steps from optimizing metabolic labeling to data processing within 3 weeks. This protocol enables sensitive and quantitative analysis of lipidated proteins at a proteome-wide scale at native expression levels, which is critical to understanding the role of lipid PTMs in health and disease.