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. 2024 Oct 1;40(10):btae585.
doi: 10.1093/bioinformatics/btae585.

NeoDesign: a computational tool for optimal selection of polyvalent neoantigen combinations

Wenqian Yu  1   2 , Hongwu Yu  1   2 , Jingjing Zhao  1   2 , Hena Zhang  1   2 , Kalam Ke  1   2 , Zhixiang Hu  1   2 , Shenglin Huang  1   2
Affiliations

NeoDesign: a computational tool for optimal selection of polyvalent neoantigen combinations

Wenqian Yu et al. Bioinformatics. .

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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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
NeoDesign structure. The NeoDesign pipeline comprises four main modules. The input library consists of neoantigen peptides, with color coding that matches peptides to corresponding optional libraries during library construction. Decision-making on peptide additions is guided by the decision function (l*num+domain+looseness), selecting peptides that minimize this function’s value. This process repeats until all peptides from the input library have been incorporated. The linker library includes common linkers, and the Linker Addition module is tasked with integrating linkers at crucial sites. Finally, the λ-Evaluation module calculates a suitable λ value for the optimal protein sequence which assists in further mRNA design (By Figdraw).
See this image and copyright information in PMC

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