PHLA-SiNet: A novel peptide-HLA binding prediction model using heterogeneous Siamese neural networks

Abstract

Predicting peptide–HLA binding is crucial for advancing immunotherapy; however, current models face several challenges, including peptide length variability, HLA sequence similarity, and a lack of experimentally validated negative data. To address these issues, we present PHLA-SiNet, an efficient pipeline that combines innovative representations with a lightweight architecture. PHLA-SiNet introduces three key components: (1) ESM-Pep, a peptide representation derived from a pre-trained language model (ESM), enabling flexible and training-free embedding of variable-length peptides; (2) IC-HLA, an HLA representation that captures allele-specific discriminative features using information content from binding and non-binding peptides; and (3) SiNet, a Siamese neural network that aligns peptide and HLA embeddings, bringing true binders closer in feature space. Prioritizing sensitivity—essential for reliably …

Maryam Nazarloo

M.Sc. Graduate in Bioinformatics, Expert in Computational Models in Immunology and X-ray Image Data Analysis

Mahsa Sa'adat

Ph.D. candidate in Computer Science specializing in Soft Computing and Artificial Intelligence, with a strong focus on bioinformatics, immunoinformatics, and computational drug design. Experienced in teaching, academic leadership, and organizing international conferences. Passionate about employing AI and machine learning to solve complex problems in healthcare and biology.

Fatemeh Zare

Associate Professor

My research interests include bioinformatics, computational biology and artificial intelligence.