Paper

MimicNeoAI: An integrated pipeline for identifying microbial epitopes and mimicry of tumor neoepitopes

Ye, Fang, Wang, Wei, Liu, Huimin, Chen, Tao, Zuo, Xiao, Zhang, Yuanwei, Zhou, Yanfei, Li, Mingwei, Zhang, Lu, Zhang, Yuxin, Zhang, Bin, He, Yin, Fang, Jinman, Jiang, Qionghui, Li, Zhilei

2025

Overview

Tumor-associated microbial antigens represent promising immunotherapy targets, yet systematic identification methods remain underdeveloped. We developed MimicNeoAI, a computational pipeline integrating BiLSTM networks to identify microbial epitopes, mutation-derived neoepitopes, and their microbial mimics from sequencing data. Training on validated epitope datasets yielded 0.90 AUC with 91% accuracy on experimental validation sets. Application to colorectal cancer revealed that microbial epitopes, despite originating from a nine-fold smaller peptide pool, generated twice the immunogenic candidates (153 vs 75) compared to mutation-derived neoepitopes. These microbial epitopes exhibited exclusive tumor-specificity with no overlap in normal tissues. Single-cell TCR sequencing confirmed clonal expansion against 75% of predicted highly immunogenic epitopes, with molecular dynamics simulations demonstrating positive correlation between predicted immunogenicity and HLA-epitope-TCR binding stability. Collectively, our pipeline systematically unveils abundant, tumor-specific, and highly immunogenic microbial epitopes, providing a computational framework for developing broadly applicable cancer immunotherapies that leverage the tumor microbiome as an untapped source of therapeutic targets.

• Publisher: Cold Spring Harbor Laboratory
• Subject: Bioinformatics