Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models

• Main Authors: Liang, Po-Yu, Huang, Xueting, Duran, Tibo, Wiemer, Andrew J, Bai, Jun
• Format: Journal Article
• Language: English
• Published: 15-08-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Learning; Quantitative Biology - Quantitative Methods
• DOI: 10.48550/arxiv.2408.08341

Generating peptides with desired properties is crucial for drug discovery and
biotechnology. Traditional sequence-based and structure-based methods often
require extensive datasets, which limits their effectiveness. In this study, we
proposed a novel method that utilized autoencoder shaped models to explore the
protein embedding space, and generate novel peptide analogs by leveraging
protein language models. The proposed method requires only a single sequence of
interest, avoiding the need for large datasets. Our results show significant
improvements over baseline models in similarity indicators of peptide
structures, descriptors and bioactivities. The proposed method validated
through Molecular Dynamics simulations on TIGIT inhibitors, demonstrates that
our method produces peptide analogs with similar yet distinct properties,
highlighting its potential to enhance peptide screening processes.