Secondary structure of proteins in the context of molecular interactions

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.

Keywords

secondary structure / oligomerization / polytopic membrane protein / protein-protein interaction / protein folding

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