Microproteins: How DNA's dark matter is reshaping the drugs of the future

Giovanni Colonna; Domenico Aprile; Giovanni Colonna; Domenico Aprile

doi:10.3934/biophy.2026014

AIMS Biophysics

2026, Volume 13, Issue 2: 225-230. doi: 10.3934/biophy.2026014

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Editorial

Microproteins: How DNA's dark matter is reshaping the drugs of the future

Giovanni Colonna ^{1
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Domenico Aprile ^{2
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1.
Medical Informatics Unit, AOU Università degli Studi della Campania, L. Vanvitelli, Naples, Italy
2.
Università degli Studi Link Campus University, Dipartimento di Scienze della Vita, della Salute e delle Professioni Sanitarie, Rome, Italy

Received: 18 June 2026 Revised: 22 June 2026 Accepted: 23 June 2026 Published: 24 June 2026

Citation: Giovanni Colonna, Domenico Aprile. Microproteins: How DNA's dark matter is reshaping the drugs of the future[J]. AIMS Biophysics, 2026, 13(2): 225-230. doi: 10.3934/biophy.2026014

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Conflict of interest

Giovanni Colonna and Domenico Aprile are the guest editor of special issue “The Dark Side of the Human Genome: Microproteins” for AIMS Biophysics and were not involved in the editorial review or the decision to publish this article. The authors declare no conflict of interest.

Author contributions

Conceptualization, Giovanni Colonna and Domenico Aprile; writing - original draft, Giovanni Colonna and Domenico Aprile; writing - review and editing, Giovanni Colonna and Domenico Aprile. Both authors have read and agreed to the final version of the manuscript.

References

[1]	Deutsch E W, Lindskog C, Budnik B, et al. (2026) The 2025 report on the human proteome from the HUPO human proteome project. J Proteome Res 25: 539-555. https://doi.org/10.1021/acs.jproteome.5c00759
[2]	Ko S H, Cho B L, Shin D (2025) Microproteins in metabolic biology: emerging functions and potential roles as nutrient-linked biomarkers. Int J Mol Sci 26: 11883. https://doi.org/10.3390/ijms262411883
[3]	Mohsen JJ, Martel AA, Slavoff SA (2023) Microproteins—Discovery, structure, and function. Proteomics 23: 2100211. https://doi.org/10.1002/pmic.202100211
[4]	AlQuraishi M, Sorger PK (2021) Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms. Nat Methods 18: 1169-1180. https://doi.org/10.1038/s41592-021-01283-4
[5]	Miller B, de Souza EV, Pai VJ, et al. (2025) ShortStop: a machine learning framework for microprotein discovery. BMC Methods 2: 16. https://doi.org/10.1186/s44330-025-00037-4
[6]	Khoroshkin M, Buyan A, Dodel M, et al. (2024) Systematic identification of post-transcriptional regulatory modules. Nat Commun 15: 7872. https://doi.org/10.1038/s41467-024-52215-7
[7]	Porozova NO, Petukhova NV, Gureev MA, et al. (2025) Beyond monoclonal antibodies: constraints and the case for alternative PD-1/PD-L1-targeting formats. Front Immunol 16: 1729468. https://doi.org/10.3389/fimmu.2025.1729468
[8]	Du E, Tang X, Zhang W, et al. (2025) Emerging mechanically interlocked cages. Nat Rev Chem 9: 506-522. https://doi.org/10.1038/s41570-025-00721-7
[9]	Baumgart M, Röpke M, Mühlbauer ME, et al. (2021) Design of buried charged networks in artificial proteins. Nat Commun 12: 1895. https://doi.org/10.1038/s41467-021-21909-7
[10]	Perez-Riverol Y (2022) Proteomic repository data submission, dissemination, and reuse: key messages. Expert Rev Proteomic 19: 297-310. https://doi.org/10.1080/14789450.2022.2160324

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