NeoDesign: a Computational Tool for Optimal Selection of Polyvalent Neoantigen Combinations

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2024 Sep 27

PMID 39331572

Authors

Wenqian Yu

Hongwu Yu

Jingjing Zhao

Hena Zhang

Kalam Ke

Zhixiang Hu

Shenglin Huang

Affiliations

Soon will be listed here.

Abstract

Motivation: Tumor polyvalent neoantigen mRNA vaccines are gaining prominence in immunotherapy. The design of sequences in vaccine development is crucial for enhancing both the immunogenicity and safety of vaccines. However, a major challenge lies in selecting the optimal sequences from the large pools generated by multiple peptide combinations and synonymous codons.

Results: We introduce NeoDesign, a computational tool designed to tackle the challenge of sequence design. NeoDesign comprises four modules: Library Construction, Optimal Path Filtering, Linker Addition, and λ-Evaluation. It aims to identify the optimal protein sequence for tumor polyvalent neoantigen vaccines by minimizing linker usage, avoiding unexpected neoantigens and functional domains, and simplifying the structure. It also provides a preference scheme to balance mRNA stability and protein expression when designing mRNA sequences for the optimal protein sequence. This tool can potentially improve the sequence design of tumor polyvalent neoantigen mRNA vaccines, thereby significantly advancing immunotherapy strategies.

Availability And Implementation: NeoDesign is freely available on https://github.com/HuangLab-Fudan/neoDesign and https://figshare.com/projects/NeoDesign/221704.

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