Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection

TL;DR

VenusVaccine is a deep learning model with dual attention that improves immunogenicity prediction accuracy, aiding vaccine target selection by leveraging extensive datasets and advanced representations.

Contribution

We introduce VenusVaccine, a novel dual attention deep learning framework that integrates protein sequence and structure embeddings for enhanced immunogenicity prediction.

Findings

VenusVaccine outperforms existing methods across multiple metrics.

The comprehensive dataset improves model training and evaluation.

Post-hoc validation confirms practical utility in vaccine design.

Abstract

Immunogenicity prediction is a central topic in reverse vaccinology for finding candidate vaccines that can trigger protective immune responses. Existing approaches typically rely on highly compressed features and simple model architectures, leading to limited prediction accuracy and poor generalizability. To address these challenges, we introduce VenusVaccine, a novel deep learning solution with a dual attention mechanism that integrates pre-trained latent vector representations of protein sequences and structures. We also compile the most comprehensive immunogenicity dataset to date, encompassing over 7000 antigen sequences, structures, and immunogenicity labels from bacteria, virus, and tumor. Extensive experiments demonstrate that VenusVaccine outperforms existing methods across a wide range of evaluation metrics. Furthermore, we establish a post-hoc validation protocol to assess the practical significance of deep learning models in tackling vaccine design challenges. Our work provides an effective tool for vaccine design and sets valuable benchmarks for future research. The implementation is at https://github.com/songleee/VenusVaccine.