Retention order prediction of peptides containing non-proteinogenic amino acids.

ABSTRACT:

Motivation

Peptides containing non-proteinogenic amino acids (PNPAs) are promising targets in drug development for their unique pharmacological properties. The lack of their mass spectra or retention data has been hindering PNPA research, where accurate assessment of their retention time in chromatography is crucial for identifying structures and characterizing functions. Conventional methods are often ineffective due to limited amount of data. This study aims to predict their retention order, not absolute time, from structures by using data from peptides and small molecules. This approach can advance natural product identification and drug research.

Results

Our model uses the Ranking Support Vector Machine, and successfully predicted the retention order of PNPA with an accu

SUBMITTER: Nakamukai S 

PROVIDER: S-EPMC12643230 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature