DeepHLApan A deep learning approach for predicting high-confidence neoantigens by considering both the presentation possibilities of mutant peptides and the potential immunogenicity of pMHC
Introduction
Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.
Publications
Credits
- Zhan Zhou zhanzhou@zju.edu.cn Investigator
College of Pharmaceutical Sciences, Zhejiang University, China
Community Ratings
Usability | Efficiency | Reliability | Rated By |
---|---|---|---|
0 user | |||
Sign in to rate |
Accession | BT007085 |
---|---|
Tool Type | Application |
Category | T-cell epitopes, Epitope selection |
Platforms | Linux/Unix |
Technologies | Python3 |
User Interface | Terminal Command Line |
Input Data | VCF |
Download Count | 0 |
Country/Region | China |
Submitted By | Zhan Zhou |