Recent advances in protein language models have catalyzed significant
progress in peptide sequence representation. Despite extensive exploration in
this field, pre-trained models tailored for peptide-specific needs remain
largely unaddressed due to the difficulty in capturing the complex and
sometimes unstable structures of peptides. This study introduces a novel
multi-view contrastive learning framework PepHarmony for the sequence-based
peptide encoding task. PepHarmony innovatively combines both sequence- and
structure-level information into a sequence-level encoding module through
contrastive learning. We carefully select datasets from the Protein Data Bank
(PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences
and structures. The experimental data highlights PepHarmony's exceptional
capability in capturing the intricate relationship between peptide sequences
and structures compared with the baseline and fine-tuned models. The robustness
of our model is confirmed through extensive ablation studies, which emphasize
the crucial roles of contrastive loss and strategic data sorting in enhancing
predictive performance. The proposed PepHarmony framework serves as a notable
contribution to peptide representations, and offers valuable insights for
future applications in peptide drug discovery and peptide engineering. We have
made all the source code utilized in this study publicly accessible via GitHub
at this https URL or
this http URL

该研究介绍了一种新颖的多视角对比学习框架 PepHarmony，用于序列化的肽编码任务，它创新性地将序列和结构级别信息结合到序列级编码模块中，并通过对比学习捕捉了肽序列和结构之间的复杂关系。此框架对于肽表示具有显著的能力，并为肽药物发现和肽工程的未来应用提供了有价值的见解。