PreprintCited 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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Preprint — published as 10.1093/nargab/lqaf178 · Paper page

Abstract

Background: 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 significant focus for biotechnology and drug development. Notably, the human genome project revealed the existence of many novel 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. Our results demonstrate that these approaches provide novel information on the structure-function relationship of these molecules with a particular focus on their biomedical relevance. Methods: We identified 85 human miniproteins using a simple multi-tier approach. First, we performed a sequence-based analysis of these proteins using several algorithms to identify regions of structural and functional importance. Protein-protein interactions and gene ontology annotations were used to analyze miniprotein function. Then, we predicted miniprotein three-dimensional structures using AI-based methods and peptide modeling to determine their relative yields for these understudied polymers. Subsequently, we used several computational biophysics methods and structure-based calculations to annotate and evaluate results from both algorithms. Results: We find several relations between predicted structure and functional properties to assign these proteins to several groups with similar properties. Sequence-based analysis leads us to identify motifs and residues that link structure-to-function for most of these proteins. We suggest novel miniprotein functions, such as thymosin beta proteins regulating the shelterin complex through TERF1 and POT1 interactions, FAM86JP and FAM66E participating in endocytic processes, and BAGE1 influencing chromatin remodeling through interaction with nuclear proteins. Further, known functions of miniproteins, such as STRIT1, STMP1, and SLN, were supported. Finally, structure-based scoring led us to build 3D models that provided complementary information to ontologies. We identify that structural propensity is not strictly dependent on polymer length. In fact, in this dataset, peptide-based algorithms may have advantages over AI-based algorithms for certain groups of miniproteins. Conclusion: This analytic approach and resulting identification and annotation of miniproteins adds much to what is currently known about miniproteins. Our determination of novel properties of miniproteins bears significant mechanistic and biomedical relevance. We propose novel functions of miniproteins, which expands our understanding of their potential roles in cellular processes. And, we practically identify which sequence and structure-based tools provide the most information, aiding future studies of miniproteins.

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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.
Venue
NAR Genomics and Bioinformatics
Publisher
openRxiv
Published
2025-02-05
Volume
7
Issue
4
Pages
lqaf178-lqaf178
ISSN
2631-9268 (ISSN-L)
OpenAlex
W4417034047
Cited by
1
References
1
Citations by year
2026: 1
FWCI
0.30
Type
journal article
Language
en
Open access
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
  • Advancing a Healthier Wisconsin Endowment
  • Medical College of Wisconsin (RRID:SCR_022926)
  • National Institutes of Health
  • Research Computing Center, University of Chicago
  • National Institute of General Medical Sciences (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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