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Methods and Resources report novel methods, substantial improvements to current methodologies, or informational datasets.
Antibodies are extensively used in biomedical research, clinical fields, and disease treatment. However, to enhance the reproducibility and reliability of antibody-based experiments, it is crucial to have a detailed understanding of the antibody’s target specificity and epitope. In this study, we developed a high-throughput and precise epitope analysis method, DECODE (Decoding Epitope Composition by Optimized-mRNA-display, Data analysis, and Expression sequencing). This method allowed identifying patterns of epitopes recognized by monoclonal or polyclonal antibodies at single amino acid resolution and predicted cross-reactivity against the entire protein database. By applying the obtained epitope information, it has become possible to develop a new 3D immunostaining method that increases the penetration of antibodies deep into tissues. Furthermore, to demonstrate the applicability of DECODE to more complex blood antibodies, we performed epitope analysis using serum antibodies from mice with experimental autoimmune encephalomyelitis (EAE). As a result, we were able to successfully identify an epitope that matched the sequence of the peptide inducing the disease model without relying on existing antigen information. These results demonstrate that DECODE can provide high-quality epitope information, improve the reproducibility of antibody-dependent experiments, diagnostics and therapeutics, and contribute to discover pathogenic epitopes from antibodies in the blood.
Citation:Matsumoto K, Harada SY, Yoshida SY, Narumi R, Mitani TT, Yada S, et al. (2025) DECODE enables high-throughput mapping of antibody epitopes at single amino acid resolution. PLoS Biol 23(1): e3002707.
Academic Editor:Takeshi Tsubata, Tokyo Medical and Dental University Medical Research Institute, JAPAN
Received:June 2, 2024; Accepted:December 6, 2024; Published: January 23, 2025
Copyright: © 2025 Matsumoto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability:Underlying data is available in Zenodo: 10.5281/zenodo.14286317. This paper performed GPU-based epitope using a genome-wide protein database in Figs 2 and S2. The codes for calculating DECODE score and visualization is available in Zenodo: 10.5281/zenodo.14221823.
Funding:This work was supported by a JST ERATO grant (H.R.U., no. JPMJER2001), a JST (Moonshot R&D) (K.M., no. JPMJMS2023), Science and Technology Platform Program for Advanced Biological Medicine JP21am0401011, AMED-CREST JP21gm0610006 (AMED/MEXT) (H.R.U.), a Brain/MINDS JP21dm0207049, Grant-in-Aid for Scientific Research (S) JP18H05270 (JSPS KAKENHI) (H.R.U.), a JSPS KAKENHI grant-in-aid for scientific research (c) (K.M., no. 20K06885), a JSPS KAKENHI grant-in-aid for Early-Career Scientists (S.Y. Y., no. 20K16626), a JSPS KAKENHI grant-in-aid for Early-Career Scientists (T. T. M., no. 20K16498), a grant-in-aid from the Human Frontier Science Program (H.R.U.), a MEXT Quantum Leap Flagship Program (MEXT QLEAP) (H.R.U., no. JPMXS0120330644) and an intramural Grant-in-Aid from the RIKEN BDR (H.R.U.).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: HR.U, KM, SY.H and YS have patents filed for DECODE. HR.U and KM are concurrently working for CUBICStars Inc.
Abbreviations:CFA, complete Freund’s adjuvant; DECODE, Decoding Epitope Composition by Optimized-mRNA-display, Data analysis, and Expression sequencing; EAE, experimental autoimmune encephalomyelitis; ELISA, enzyme-linked immunosorbent assay; GCE, genetic code expansion; HRP, horseradish peroxidase; MOG, myelin oligodendrocyte glycoprotein; NGS, next-generation sequencing; NMR, nuclear magnetic resonance; PTM, posttranslational modification
Antibodies play a crucial role in biological and biomedical research, as well as clinical applications like diagnostics and antibody-based therapies. Currently, there are over 7 million antibodies listed worldwide. Despite the acceleration in antibody production, the reproducibility and reliability of antibody-based studies have been a longstanding concern. These issues often stem from antibody quality, including factors like purity, affinity, specificity, and cross-reactivity. While purity and affinity can be quantitatively assessed using indicators like titer and K d, there is a lack of sufficient indicators for specificity and cross-reactivity. Epitope information, which refers to the site an antibody recognizes, is valuable for evaluating antibody specificity and cross-reactivity. Typically, antibodies recognize 10 or fewer amino acid residues when binding to linear epitope, with the most critical being the five or fewer hotspot residues energetically required for binding. Understanding the significance and characteristics of hotspot residues in epitopes can assist researchers in selecting antibodies that are most suitable for their experimental conditions or in designing the optimal conditions for these antibodies. Additionally, by exploring sites similar to the epitope across genome-wide protein sequences, it becomes possible to predict antibody target specificity and cross-reactivity with non-targets. Leveraging such detail epitope information has the potential to enhance the reproducibility of antibody-dependent scientific investigations and pathological diagnoses. Despite the significance of detailed epitope information, commercially available antibodies remain largely uncharacterized in this regard. Epitope databases like IEDB and IMGT, as well as antibody manufacturer websites, rarely provide such details. Addressing this challenge necessitates the development of high-throughput genome-wide epitope analysis methods with single amino acid resolution.
Many epitope analysis methods have been reported to date, but achieving high-resolution, comprehensiveness, and genome-wide analysis simultaneously has been challenging due to their limitations in detection sensitivity and throughput. Although HDX-MS, X-ray crystallography and nuclear magnetic resonance (NMR) methods allow for 3D structure observation, they require an antigen itself and have low throughput. Epitope mapping using peptide libraries, such as peptide microarrays and peptide selection, is effective for identifying binding sites and hot spot residues. The theoretical diversity that hotspot residues in an epitope can be calculated to approximately 10 9 by raising the number of hotspot residues to the 20th power and then multiplying by the number of possible positions of the hotspot residues in the binding site. In other words, to accurately identify hotspot residues, a library size that exceeds this diversity is required. Peptide selection by peptide microarrays or bacterial display limits the range or quality of epitope searches due to the small size of peptide libraries (approximately <10 5 and <10 6, respectively). Among peptide selection methods, the library size of phage display is approximately <10 9, and the library size of mRNA display and of ribosome display are approximately <10 13. Given that these methods can handle large libraries, they are anticipated to accurately identify patterns of significance in the amino acids recognized by antibodies within epitopes, including hotspot residues. In particular, mRNA display and ribosome display are in vitro translation-based peptide selection methods, where all components are well defined and controllable, providing scope for developing simpler, higher-throughput protocols. Additionally, mRNA display methods have the advantage of eliminating large ribosomes that may interfere with peptides binding to the target molecule due to the formation of covalent bonds between the mRNA and the translation product vi
