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<p>Understanding protein–protein interactions (PPIs) is a central challenge in plant proteomics, allowing us to understand the full range of molecular mechanisms that drive biological processes<i>.</i> Elucidating PPIs is key to revealing the roles of proteins within different signaling pathways, offering insights into how plants live and respond to different stimuli. For this reason, various techniques have been developed and progressively refined to identify PPIs under different conditions, with specific adaptations for the detection of PPIs in plants. Techniques have been developed that range from using yeast as tools to techniques that allow precise detection of PPIs based on subcellular localization and biochemical properties of the proteins. In vitro and in vivo methods validate dynamic PPIs, while in silico tools like AlphaFold2 and Rosetta predict PPI structures with unprecedented accuracy, integrating deep learning and multiple sequence alignments. Over the next 50&#xa0;years, AI-driven in silico predictions, high-resolution imaging, and advanced in vitro and in vivo assays will map comprehensive plant interactomes, designing stress-tolerant proteins to enhance crop resilience and address global food security, seamlessly merging computational and experimental approaches.</p>

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Protein–protein interaction detection in plants: from classical approaches to future perspectives

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • A. F. Rosiles-Ortega,
  • M. J. Abraham-Juárez,
  • F. Jaimes-Miranda

摘要

Understanding protein–protein interactions (PPIs) is a central challenge in plant proteomics, allowing us to understand the full range of molecular mechanisms that drive biological processes. Elucidating PPIs is key to revealing the roles of proteins within different signaling pathways, offering insights into how plants live and respond to different stimuli. For this reason, various techniques have been developed and progressively refined to identify PPIs under different conditions, with specific adaptations for the detection of PPIs in plants. Techniques have been developed that range from using yeast as tools to techniques that allow precise detection of PPIs based on subcellular localization and biochemical properties of the proteins. In vitro and in vivo methods validate dynamic PPIs, while in silico tools like AlphaFold2 and Rosetta predict PPI structures with unprecedented accuracy, integrating deep learning and multiple sequence alignments. Over the next 50 years, AI-driven in silico predictions, high-resolution imaging, and advanced in vitro and in vivo assays will map comprehensive plant interactomes, designing stress-tolerant proteins to enhance crop resilience and address global food security, seamlessly merging computational and experimental approaches.