Secondary structure of proteins in the context of molecular interactions

Yoshito Abe; Yoshito Abe

doi:10.3934/biophy.2026002

AIMS Biophysics

2026, Volume 13, Issue 1: 21-28. doi: 10.3934/biophy.2026002

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Review

Secondary structure of proteins in the context of molecular interactions

Yoshito Abe ^,

School of Pharmacy at Fukuoka, International University of Health and Welfare, 137-1 Enoki-zu, Okawa Fukuoka 831-8501, Japan

Received: 07 January 2025 Revised: 30 January 2026 Accepted: 03 February 2026 Published: 05 February 2026

Protein secondary structure has long been viewed as a local property encoded in the amino acid sequence. While classical definitions of α-helices and β-sheets and established experimental and computational methods have provided a solid framework for structural analysis, growing evidence indicates that secondary structure is strongly influenced by intra- and intermolecular interactions. Such context dependence poses significant challenges for conventional sequence-based prediction approaches. In this review, I discuss the limitations of traditional secondary-structure prediction methods and highlight recent advances enabled by deep learning–based structure prediction, with a particular focus on AlphaFold. Using oligomeric proteins and polytopic membrane proteins as representative examples, I illustrate how interaction-dependent secondary structures can be revealed by integrating computational predictions with experimental data. These observations emphasize the importance of considering molecular interaction contexts to achieve a comprehensive understanding of protein secondary structure and its functional roles.
- secondary structure,
- oligomerization,
- polytopic membrane protein,
- protein-protein interaction,
- protein folding
Citation: Yoshito Abe. Secondary structure of proteins in the context of molecular interactions[J]. AIMS Biophysics, 2026, 13(1): 21-28. doi: 10.3934/biophy.2026002

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Abstract

Protein secondary structure has long been viewed as a local property encoded in the amino acid sequence. While classical definitions of α-helices and β-sheets and established experimental and computational methods have provided a solid framework for structural analysis, growing evidence indicates that secondary structure is strongly influenced by intra- and intermolecular interactions. Such context dependence poses significant challenges for conventional sequence-based prediction approaches. In this review, I discuss the limitations of traditional secondary-structure prediction methods and highlight recent advances enabled by deep learning–based structure prediction, with a particular focus on AlphaFold. Using oligomeric proteins and polytopic membrane proteins as representative examples, I illustrate how interaction-dependent secondary structures can be revealed by integrating computational predictions with experimental data. These observations emphasize the importance of considering molecular interaction contexts to achieve a comprehensive understanding of protein secondary structure and its functional roles.

Conflict of interest

The author declares no conflicts of interest.

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