In recent years, the scientific community has become increasingly interested
on peptides with non-canonical amino acids due to their superior stability and
resistance to proteolytic degradation. These peptides present promising
modifications to biological, pharmacological, and physiochemical attributes in
both endogenous and engineered peptides. Notwithstanding their considerable
advantages, the scientific community exhibits a conspicuous absence of an
effective pre-trained model adept at distilling feature representations from
such complex peptide sequences. We herein propose PepLand, a novel pre-training
architecture for representation and property analysis of peptides spanning both
canonical and non-canonical amino acids. In essence, PepLand leverages a
comprehensive multi-view heterogeneous graph neural network tailored to unveil
the subtle structural representations of peptides. Empirical validations
underscore PepLand's effectiveness across an array of peptide property
predictions, encompassing protein-protein interactions, permeability,
solubility, and synthesizability. The rigorous evaluation confirms PepLand's
unparalleled capability in capturing salient synthetic peptide features,
thereby laying a robust foundation for transformative advances in
peptide-centric research domains. We have made all the source code utilized in
this study publicly accessible via GitHub at
this https URL

我们提出了 PepLand，它是一种新颖的预训练架构，用于表示和属性分析既包括规范又包括非规范氨基酸的多肽。经验验证证实了 PepLand 在蛋白质相互作用、渗透性、溶解度和合成能力等各种多肽性质预测中的有效性，为多肽中心研究领域的转变性进展奠定了坚实的基础。