frontier-banner
前沿速递
首页>前沿速递>

Antibodies | A Reproducible Sequence-Level Strategy Enhances Peptide Immunogenicity

Antibodies | A Reproducible Sequence-Level Strategy Enhances Peptide Immunogenicity
--

This study proposes an improved sequence-level strategy for removing peptide conserved regions and optimizing MHC binding prediction. It enhances antibody titers and IgG1 isotype switching through conservative substitutions while maintaining recognition capacity of wild-type epitopes. This approach provides a practical and reproducible immunogen design pathway for targets lacking commercial antibodies.

 

Literature Overview

This article, 'A Reproducible Sequence-Level Strategy to Enhance Peptide Immunogenicity While Preserving Wild-Type Epitope Recognition', published in the journal Antibodies, reviews and summarizes the application bottlenecks of short peptide epitopes in immunological research and proposes a three-step screening workflow based on cross-species conservation, structural features, and MHC binding prediction. The study validated the feasibility of this strategy in enhancing immunogenicity while maintaining native epitope recognition through a visfatin model, offering a new perspective for antibody production.

Background Knowledge

Short peptide epitopes are valuable for functional studies, immunoassays, and signal pathway analysis but often result in low-efficiency antibody production due to their inherent low immunogenicity and high cross-species conservation. Conventional approaches, such as carrier protein conjugation (KLH, OVA, BSA), strong adjuvant formulations, or long peptide designs, can increase antibody titers but often lead to immune-dominant epitopes from the carrier protein, failing to address the immunogenicity defects of the peptide sequence itself. Recently, heterologous substitution strategies within T cell epitopes have been shown to enhance MHC binding capacity while preserving native recognition. However, their application in B cell epitope design remains limited. This study systematically introduces conservative heterologous substitution strategies into B cell epitope design, combining structural exposure and MHC prediction to establish a versatile and reproducible framework for peptide antigen optimization.

 

 

Research Methods and Experiments

The research team established a three-step screening workflow by integrating cross-species alignment, structural filtering, and MHC binding prediction. First, based on human-mouse visfatin sequence alignment, regions with <95% conservation were selected. Second, surface-exposed loop structures were analyzed using crystal structures (PDB: 2E5B, 2GVL), excluding glycosylation or phosphorylation modification sites. Third, MHC-I (H2-Kd) and MHC-II (H2-IEd) binding capacities were predicted using the IEDB platform, prioritizing peptides with improved binding predictions for mutation design. Mutations were limited to conservative substitutions with similar physicochemical properties (e.g., F→Y, T→M) to preserve epitope conformational stability. The optimized peptides (V1-1 and V2-1) were conjugated with KLH for mouse immunization, with antibody titers measured by ELISA and further evaluated for binding affinity (Kd) and cross-reactivity through isothermal titration calorimetry.

Key Conclusions and Perspectives

  • Mutant peptides significantly improved MHC binding prediction, with V1-1 and V2-1 showing 11% and 7% enhancement in MHC-I and MHC-II binding capacity, respectively.
  • Immunized mice demonstrated that mutant groups had significantly higher antibody titers compared to wild-type groups, along with increased IgG1 isotype ratios, indicating stronger T-helper cell involvement.
  • Polyclonal antibodies induced by mutations maintained cross-reactivity against the wild-type peptides, with Kd values improved to the 10^-9 M level.
  • Double mutation experiments confirmed that spatial arrangement affects binding strength without compromising original epitope recognition, validating the flexibility of the mutation design.
  • The strategy was extendable to highly conserved targets such as THBS2, ANTXR1, and IFITM1, indicating broad applicability.


Research Significance and Prospects

This strategy provides a low-barrier, reproducible immunogen design approach for targets without existing commercial antibodies. Future development could integrate structural modeling, high-throughput mutation scanning, or phage display technologies to further systematize substitution rules and automate design pipelines. Additionally, this method holds translational potential in diagnostic biomarker discovery, tumor neoantigen studies, and viral conserved epitope research, although cross-reactivity risks and clinical applicability require further evaluation.

 

 

Conclusion

This study demonstrates a viable approach to enhancing peptide immunogenicity through conservative sequence editing. The method does not rely on complex carriers or special adjuvants; instead, it utilizes minimal amino acid substitutions with conserved physicochemical properties to significantly boost antibody responses while preserving original epitope recognition. The strategy is particularly applicable to highly conserved antigens and offers a systematic, reproducible optimization protocol for antibody production, epitope identification, and immunological studies. Future research should integrate structural biology and mutation scanning techniques to further validate epitope recognition mechanisms and cross-reactivity profiles, extending this approach to multi-disease models and preclinical studies.

 

Reference:
Chia-Hung Chen, Yu-Chi Chiu, Kai-Yao Huang, Shun-Long Weng, and Kuang-Wen Liao. A Reproducible Sequence-Level Strategy to Enhance Peptide Immunogenicity While Preserving Wild-Type Epitope Recognition. Antibodies.
ImmuneBuilder(AntiBody)
ImmuneBuilder, including ABodyBuilder2, NanoBodyBuilder2, and TCRBuilder2, is specifically designed for predicting the structure of immunoproteins (e.g., antibodies, nanobodies, and T-cell receptors), and employs AlphaFold-Multimer's structural modules with modifications specific to the immunoproteins to improve prediction accuracy.ImmuneBuilder is able to quickly generate immunoprotein structures that resemble experimental data much faster than AlphaFold2 and without the need for large sequence databases or multiple sequence comparisons. The tool's features include high accuracy, fast prediction, and open-source accessibility for structural analysis of large-scale sequence datasets, especially in the study of immunoprotein structures from next-generation sequencing data. immuneBuilder also provides error estimation to help filter out erroneous models, enhancing its value for applications in biotherapeutics and immunology research. Figure 1 shows the architecture of AbBuilder2, and the same architecture is used for NanoBodyBuilder2 and TCRBuilder2.
0