Annotation of Dipeptides and Tripeptides Derivatized via Dansylation Based on Liquid Chromatography–Mass Spectrometry and Iterative Quantitative Structure Retention Relationship

Small
peptides such as dipeptides and tripeptides show various
biological activities in organisms. However, methods for identifying
dipeptides/tripeptides from complex biological samples are lacking.
Here, an annotation strategy involving the derivatization of dipeptides
and tripeptides via dansylation was suggested based on liquid chromatography–mass
spectrometry (LC–MS) and iterative quantitative structure retention
relationship (QSRR) to choose dipeptides/tripeptides by using a small
number of standards. First, the LC-autoMS/MS method and initial QSRR
model were built based on 25 selected grid-dipeptides and 18 test-dipeptides.
To achieve high-coverage detection, dipeptide/tripeptide pools containing
abundant dipeptides/tripeptides were then obtained from four dansylated
biological samples including serum, tissue, feces, and soybean paste
by using the parameter-optimized LC-autoMS/MS method. The QSRR model
was further optimized through an iterative train-by-pick strategy.
Based on the specific fragments and tR tolerances, 198 dipeptides and 149 tripeptides were annotated. The
dipeptides at lower annotation levels were verified by using authentic
standards and grid-correlation analysis. Finally, variation in serum
dipeptides/tripeptides of three different liver diseases including
hepatitis B infection, liver cirrhosis, and hepatocellular carcinoma
was characterized. Dipeptides with N-prolinyl, C-proline, N-glutamyl,
and N-valinyl generally increased with disease severity. In conclusion,
this study provides an efficient strategy for annotating dipeptides/tripeptides
from complex samples.