AmpHGT: expanding prediction of antimicrobial activity in peptides containing non-canonical amino acids using multi-view constrained heterogeneous graph transformer.
Journal:
BMC biology
Published Date:
Jul 1, 2025
Abstract
BACKGROUND: Antimicrobial peptide (AMP) prediction has been extensively studied in recent years. However, many existing models do not fully leverage the intrinsic chemical structures of AMPs, such as atomic composition and sidechain group characteristics. Instead, these models often focus on letter composition, positional encodings, and pre-defined chemical-physical descriptors. The incorporation of non-canonical amino acids, which enhance peptide stability and reduce toxicity, is getting more attention in peptide design. Despite this, they are overlooked in predictive studies, as traditional deciphering methods and single-letter representation systems are inadequate for this task. Even though some efforts have been made to expand current alphabets, these approaches remain insufficient, impeding the development of novel AMPs.
Authors
Keywords
No keywords available for this article.
