PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids.

ABSTRACT: The recent interest in peptides incorporating non-canonical amino acids has surged within the scientific community, driven by their enhanced stability and resistance to proteolytic degradation. These so-called non-canonical peptides offer significant potential for modifying biological, pharmacological, and physiochemical characteristics in both native and synthetic contexts. Despite their advantages, there remains a notable gap in the availability of an efficient pre-trained model capable of effectively capturing feature representations from such intricate peptide sequences. This study herein introduces PepLand, a novel pre-training framework designed for the comprehensive representation and analysis of peptides, encompassing both canonical and non-canonical amino acids. PepLand leverages

SUBMITTER: Zhang R 

PROVIDER: S-EPMC12315545 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature