# Differentiation of Isomeric TAT1-CARNOSINE Peptides by Energy-Resolved Mass Spectrometry and Principal Component Analysis

## TL;DR

This study shows how PCA can help distinguish similar peptides using mass spectrometry data, even when traditional methods fail.

## Contribution

The novel use of PCA to differentiate isomeric peptides based on full MS/MS spectra rather than major fragment peaks.

## Key findings

- PCA successfully differentiated three isomeric TAT1-Car peptides from similar MS/MS spectra.
- Distinct low-intensity fragment ions were identified for each peptide through PCA loadings.
- Some specific peaks were linked to fragmentation scrambling, offering insights into peptide behavior.

## Abstract

L-carnosine (Car) is an endogenous dipeptide with significant potential in drug discovery for neurodegenerative diseases, while TAT1, a small arginine-rich peptide derived from the HIV-1 trans-activator protein (TAT), is known to stimulate proteasome activity. In this study, three isomeric peptides were synthesised by incorporating the Car moiety at the N-terminus, C-terminus, or central position of the TAT1 sequence. To differentiate these isomers, high-resolution and energy-resolved CID MS/MS experiments were conducted. The resulting MS/MS spectra showed a high degree of similarity among the peptides, predominantly characterised by fragment ion peaks arising from arginine-specific neutral losses. Energetic analysis was similarly inconclusive in resolving the isomers. However, Principal Component Analysis (PCA) enabled clear differentiation of the three peptides by considering the entire MS/MS spectra rather than focusing solely on precursor ion intensities or major fragment peaks. PCA loadings revealed distinct fragment ions for each peptide, albeit with lower intensities, providing insights into consecutive fragmentation patterns. Some of these specific peaks could also be attributed to scrambling during fragmentation. These results demonstrate the potential of PCA as a simple chemometric tool for semi-automated peak identification in complex MS/MS spectra.