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Cited by 1Protein Structure and DynamicsComputational Drug Discovery MethodsMachine Learning in Bioinformatics

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An integrative multitiered computational analysis for better understanding the structure and function of 85 miniproteins

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Veluri, Reethika, Pollin, Gareth, Wagenknecht, Jessica B, Urrutia, Raul, Zimmermann, Michael T·2025·NAR Genomics and Bioinformatics
DOI 10.1093/nargab/lqaf178PMC PMC11838408

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Abstract

Miniproteins, defined as polypeptides containing fewer than 50 amino acids, have recently elicited significant interest due to an emerging understanding of their diverse roles in fundamental biological processes. In addition, miniprotein dysregulation underlies human diseases and is a considerable focus for biotechnology and drug development. The human genome project revealed many miniproteins, most of which remain uncharacterized. This study reports an approach for analyzing and scoring previously uncharacterized miniproteins by integrating knowledge from classic sequence-based bioinformatics, computational biophysics, and system biology annotations. We identified 85 human miniproteins using this simple multi-tier approach. Then, we predicted miniprotein three-dimensional structures using AI-based methods and peptide modeling to determine their relative yields for these understudied polymers. We identify that structural propensity is not strictly dependent on polymer length, and peptide-based algorithms may have advantages over AI-based algorithms for certain groups of miniproteins. Subsequently, we used several computational biophysics methods and structure-based calculations to annotate and evaluate results from both algorithms. We propose novel structure-function relationships for miniproteins, which expands our understanding of their potential roles in cellular processes. Finally, we practically identify which sequence- and structure-based tools provide the most information, aiding future studies of miniproteins, with emphasis on their biomedical relevance.

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Metadata

Authors
Veluri, R., Pollin, G., Wagenknecht, J. B., Urrutia, R., & Zimmermann, M. T.
Affiliations
Medical College of Wisconsin (US)
Corresponding author
Zimmermann, Michael T
ORCID
Venue
NAR Genomics and Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2025-10-07
Volume
7
Issue
4
Pages
lqaf178-lqaf178
ISSN
2631-9268 (ISSN-L)
DOI
10.1093/nargab/lqaf178
PMC
PMC11838408
OpenAlex
W4417034047
Cited by
1
References
1
Citations by year
2026: 1
FWCI
0.30
Type
journal article
Language
en
Open access
Licence
Locations
  • NAR Genomics and Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed (publishedVersion)
  • Ulster University Research Portal (Ulster University) (publishedVersion, cc-by)
  • PubMed Central (submittedVersion, cc-by-nd)
Funders
  • Medical College of Wisconsin (RRID:SCR_022926)
  • Advancing a Healthier Wisconsin Endowment
  • Research Computing Center
  • National Institute of General Medical Sciences
  • National Institutes of Health (R35GM153740)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
miniproteins, structure-function relationship, computational biophysics, hypothetical protein

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