SPAID: a comprehensive database for disease-specific autoantigens in autoimmune disorders

Shunhui Deng , Fangfang Wei , Ya Pang , Luowanyue Zhang , Shengyao Zhi , Tianjian Chen , Zhixiang Zuo , Jian Ren , Yubin Xie , Xiaotong Luo

Advanced Biotechnology ›› 2026, Vol. 4 ›› Issue (2) : 23

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Advanced Biotechnology ›› 2026, Vol. 4 ›› Issue (2) :23 DOI: 10.1007/s44307-026-00117-8
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SPAID: a comprehensive database for disease-specific autoantigens in autoimmune disorders
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Abstract

Autoimmune diseases (ADs) are chronic inflammatory disorders characterized by complex etiologies and significant diagnostic challenges. Although autoantigens are critical for precision diagnosis and therapy, much of the immunogenic landscape remains unexplored due to the historical focus on canonical proteins. Here, we developed SPAID (https://spaid.renlab.cn), a comprehensive resource for candidate autoantigen discovery across 14 ADs that integrates canonical and non-canonical proteins within a two-level evidence framework. The validated level contains proteins associated with experimentally confirmed epitopes from T-cell assays and major histocompatibility complex (MHC) ligand assays. The proteomics-based level contains proteins identified by mass spectrometry (MS) from human samples, further annotated with differential expression patterns, immunogenicity scores, and functional features to support candidate autoantigen discovery and further validation. By combining validated evidence with proteomics-based evidence, SPAID enables the comprehensive characterization of candidate autoantigen repertoires and facilitates mechanistic investigation into antigen origins and pathogenic recognition. Overall, SPAID provides a foundational resource for advancing antigen-centered research and developing novel diagnostic and therapeutic strategies in autoimmunity.

Keywords

Autoimmune diseases / Proteomics / Non-canonical proteins / Small peptides / Candidate autoantigens

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Shunhui Deng, Fangfang Wei, Ya Pang, Luowanyue Zhang, Shengyao Zhi, Tianjian Chen, Zhixiang Zuo, Jian Ren, Yubin Xie, Xiaotong Luo. SPAID: a comprehensive database for disease-specific autoantigens in autoimmune disorders. Advanced Biotechnology, 2026, 4 (2) : 23 DOI:10.1007/s44307-026-00117-8

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References

[1]

Alunno A, Caneparo V, Bistoni O, Caterbi S, Terenzi R, Gariglio M, et al.. Circulating interferon-inducible protein IFI16 correlates with clinical and serological features in rheumatoid arthritis. Arthritis Care Res (Hoboken), 2016, 68: 440-445.

[2]

Chen T, Ma J, Liu Y, Chen Z, Xiao N, Lu Y, et al.. iProX in 2021: connecting proteomics data sharing with big data. Nucleic Acids Res, 2022, 50: D1522-D1527.

[3]

Choi M, Carver J, Chiva C, Tzouros M, Huang T, Tsai TH, et al. MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets. Nat Methods. 2020; 17:981–4. https://doi.org/10.1038/s41592-020-0955-0

[4]

Curran AM, Girgis AA, Jang Y, Crawford JD, Thomas MA, Kawalerski R, et al.. Citrullination modulates antigen processing and presentation by revealing cryptic epitopes in rheumatoid arthritis. Nat Commun, 2023, 14. ArticleID: 1061

[5]

Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods, 2020, 17: 41-44.

[6]

Deutsch EW, Lam H, Aebersold R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep, 2008, 9: 429-434.

[7]

Dyer SC, Austine-Orimoloye O, Azov AG, Barba M, Barnes I, Barrera-Enriquez VP, et al.. Ensembl 2025. Nucleic Acids Res, 2025, 53: D948-D957.

[8]

Gao Y, Gao Y, Fan Y, Zhu C, Wei Z, Zhou C, et al.. Pan-peptide meta learning for T-cell receptor–antigen binding recognition. Nat Mach Intell, 2023, 5: 236-249.

[9]

García-Ruiz S, Gustavsson EK, Zhang D, Reynolds RH, Chen Z, Fairbrother-Browne A, et al.. IntroVerse: a comprehensive database of introns across human tissues. Nucleic Acids Res, 2023, 51: D167-D178.

[10]

Gulati G, Brunner HI. Environmental triggers in systemic lupus erythematosus. Semin Arthritis Rheum, 2018, 47: 710-717.

[11]

Gutierrez-Roelens I, Lauwerys BR. Genetic susceptibility to autoimmune disorders: clues from gene association and gene expression studies. Curr Mol Med, 2008, 8: 551-561.

[12]

Jiang S, Li H, Zhang L, Mu W, Zhang Y, Chen T, et al.. Generic Diagramming Platform (GDP): a comprehensive database of high-quality biomedical graphics. Nucleic Acids Res, 2025, 53: D1670-D1676.

[13]

Karopka T, Fluck J, Mevissen HT, Glass A. The Autoimmune Disease Database: a dynamically compiled literature-derived database. BMC Bioinformatics, 2006, 7. ArticleID: 325

[14]

Khan MA. Polymorphism of HLA-B27: 105 subtypes currently known. Curr Rheumatol Rep, 2013, 15. ArticleID: 362

[15]

Kolde R, Laur S, Adler P, Vilo J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics, 2012, 28: 573-580.

[16]

Lee S, Choi E, Chae S, Koh JH, Choi Y, Kim JG, et al.. Identification of MYH9 as a key regulator for synoviocyte migration and invasion through secretome profiling. Ann Rheum Dis, 2023, 82: 1035-1048.

[17]

Lin R, Xu Z, Zhi M. Canonical pathways and selective mechanisms of autophagy in inflammatory bowel disease. Adv Biotechnol, 2026, 4. ArticleID: 4

[18]

Lodha M, Erhard F, Dölken L, Prusty BK. The hidden enemy within: non-canonical peptides in virus-induced autoimmunity. Front Microbiol, 2022, 13. ArticleID: 840911

[19]

Luo X, Huang Y, Li H, Luo Y, Zuo Z, Ren J, et al.. SPENCER: a comprehensive database for small peptides encoded by noncoding RNAs in cancer patients. Nucleic Acids Res, 2022, 50: D1373-D1381.

[20]

Martorell-Marugán J, López-Domínguez R, García-Moreno A, Toro-Domínguez D, Villatoro-García JA, Barturen G, et al.. A comprehensive database for integrated analysis of omics data in autoimmune diseases. BMC Bioinformatics, 2021, 22. ArticleID: 343

[21]

Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat Methods, 2022, 19: 679-682.

[22]

Moriya Y, Kawano S, Okuda S, Watanabe Y, Matsumoto M, Takami T, et al.. The jPOST environment: an integrated proteomics data repository and database. Nucleic Acids Res, 2019, 47: D1218-D1224.

[23]

Nesvizhskii AI. Proteogenomics: concepts, applications and computational strategies. Nat Methods, 2014, 11: 1114-1125.

[24]

Nguyen H, Guyer P, Ettinger RA, James EA. Non-genetically encoded epitopes are relevant targets in Autoimmune Diabetes. Biomedicines, 2021, 9(2. ArticleID: 202

[25]

O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al.. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res, 2016, 44: D733-D745.

[26]

Othoum G and Maher CA. CrypticProteinDB: an integrated database of proteome and immunopeptidome derived non-canonical cancer proteins. NAR Cancer. 2023; 5:zcad024. https://doi.org/10.1093/narcan/zcad024

[27]

Peng FZ, Wang C, Chen T, Schussheim B, Vincoff S, Chatterjee P. PTM-Mamba: a PTM-aware protein language model with bidirectional gated Mamba blocks. Nat Methods, 2025, 22: 945-949.

[28]

Perez-Riverol Y, Bandla C, Kundu DJ, Kamatchinathan S, Bai J, Hewapathirana S, et al.. The PRIDE database at 20 years: 2025 update. Nucleic Acids Res, 2025, 53: D543-D553.

[29]

Prinz JC. Human Leukocyte Antigen-Class I alleles and the autoreactive T cell response in Psoriasis pathogenesis. Front Immunol, 2018, 9. ArticleID: 954

[30]

Pujar S, O'Leary NA, Farrell CM, Loveland JE, Mudge JM, Wallin C, et al.. Consensus coding sequence (CCDS) database: a standardized set of Human and Mouse protein-coding regions supported by expert curation. Nucleic Acids Res, 2018, 46: D221-D228.

[31]

Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, Comeau DC, et al.. Database resources of the National Center for Biotechnology Information in 2023. Nucleic Acids Res, 2023, 51: D29-D38.

[32]

Singh JA, Saag KG, Bridges SLJr, Akl EA, Bannuru RR, Sullivan MC, et al.. 2015 American College of Rheumatology guideline for the treatment of Rheumatoid Arthritis. Arthritis Rheumatol, 2016, 68: 1-26.

[33]

Starck SR, Shastri N. Nowhere to hide: unconventional translation yields cryptic peptides for immune surveillance. Immunol Rev, 2016, 272: 8-16.

[34]

Sun L, Luo H, Bu D, Zhao G, Yu K, Zhang C, et al.. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Res, 2013, 41. ArticleID: e166

[35]

The RNAcentral Consortium. RNAcentral: a hub of information for non-coding RNA sequences. Nucleic Acids Res, 2019, 47: D221-D229.

[36]

Tyanova S, Temu T, Cox J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc, 2016, 11: 2301-2319.

[37]

UniProt Consortium. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res, 2023, 51: D523-D531.

[38]

van Boekel MA, Vossenaar ER, van den Hoogen FH, van Venrooij WJ. Autoantibody systems in rheumatoid arthritis: specificity, sensitivity and diagnostic value. Arthritis Res Ther, 2002, 4: 87-93.

[39]

Vanderlugt CL, Miller SD. Epitope spreading in immune-mediated diseases: implications for immunotherapy. Nat Rev Immunol, 2002, 2: 85-95.

[40]

Vita R, Blazeska N, Marrama D, IEDB Curation Team Members, Duesing S, Bennett J, et al. The Immune Epitope Database (IEDB): 2024 update. Nucleic Acids Res. 2025; 53:D436-D43. https://doi.org/10.1093/nar/gkae1092

[41]

Wang M, Claesson MH. Classification of human leukocyte antigen (HLA) supertypes. Methods Mol Biol, 2014, 1184: 309-317.

[42]

Wang L, Park HJ, Dasari S, Wang S, Kocher JP, Li W. CPAT: Coding-potential assessment tool using an alignment-free logistic regression model. Nucleic Acids Res, 2013, 41. ArticleID: e74

[43]

Wang D, Yang L, Zhang P, LaBaer J, Hermjakob H, Li D, et al. AAgAtlas 1.0: a human autoantigen database. Nucleic Acids Res. 2017; 45:D769-D76. https://doi.org/10.1093/nar/gkw946

[44]

Wells DK, van Buuren MM, Dang KK, Hubbard-Lucey VM, Sheehan KCF, Campbell KM, et al.. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell, 2020, 183: 818-34 e13.

[45]

Wucherpfennig KW, Call MJ, Deng L, Mariuzza R. Structural alterations in peptide-MHC recognition by self-reactive T cell receptors. Curr Opin Immunol, 2009, 21: 590-595.

[46]

Xie Y, Li H, Luo X, Li H, Gao Q, Zhang L, et al. IBS 2.0: an upgraded illustrator for the visualization of biological sequences. Nucleic Acids Res. 2022; 50:W420-W6. https://doi.org/10.1093/nar/gkac373

[47]

Yuan Z, Ye J, Liu B, Zhang L. Unraveling the role of autophagy regulation in Crohn's disease: from genetic mechanisms to potential therapeutics. Adv Biotechnol, 2024, 2. ArticleID: 14

[48]

Yurasov S, Wardemann H, Hammersen J, Tsuiji M, Meffre E, Pascual V, et al.. Defective B cell tolerance checkpoints in systemic lupus erythematosus. J Exp Med, 2005, 201: 703-711.

[49]

Zhao X, Ma S, Wang B, Jiang X, The Han100K Initiative and Xu S. PGG.MHC: toward understanding the diversity of major histocompatibility complexes in human populations. Nucleic Acids Res. 2023; 51:D1102-D8. https://doi.org/10.1093/nar/gkac997

Funding

National Key Research and Development Program of China(2023YFC2705900)

National Natural Science Foundation of China(32470709)

Guangdong Province Excellent Youth Team Project(2024B1515040009)

Natural Science Foundation of Guangdong Province(2025A1515011032)

Guangzhou Science and Technology Bureau ‘Qihang’ Program for Young PhDs in Basic and Applied Research(SL2024A04J01833)

Discipline training, innovation, the quality improvement engineering team project of Guangdong Pharmaceutical University(2024QZ02)

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