Protein digest extraction strategies improve the identification of lipidated peptides in bottom-up proteomics.

Abstract

Alteration of proteins by attaching lipid moieties is a type of post-translational modification that affects the properties of proteins. It plays a role in various physiological processes, mostly due to direct interaction with cell membranes. Proteomic identification of lipoproteins typically depends on detecting their unmodified regions. However, lipopeptides, due to their distinct structural and chemical properties, display chromatographic and mass-spectrometric behavior which differs substantially from that of unmodified peptides. The altered chromatographic and mass-spectrometric behavior of lipid-modified peptides makes their detection and identification challenging, largely because of the increased hydrophobicity introduced by lipid moieties. In order to detect lipopeptides by means of LC-MS, the whole analytical approach, including sample preparation, LC-MS analysis and data evaluation usually has to be modified. We propose an optimized strategy that combines an extraction step enriching lipopeptides prior to LC-MS analysis, adjustments to chromatographic gradients and buffers, and a modified data analysis workflow. Solid-phase and liquid-liquid extraction procedures effectively enrich lipopeptides from complex digests, enabling the detection of low-abundance lipidated peptides. Current search algorithms have difficulties in detecting lipopeptides reliably. The application of a simple script to search for diagnostic fragments in MS/MS data revealed that approximately 50% of N-myristoylated peptides were not detected by MaxQuant. De novo sequencing confirmed 37 N-myristoylated peptide sequences, of which 27 were assignable to known proteins. These results demonstrate that optimized sample preparation combined with diagnostic fragment-based searches improves the detection of N-terminal myristoylation while also highlighting the limited reliability of lipopeptide identification using commonly employed database search algorithms.