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<p>Proteins are involved in most cellular functions, typically acting as part of complexes or through dynamic interactions with other proteins and/or molecules. Understanding these interactions is therefore essential for uncovering cellular mechanisms and guiding therapeutic development. However, many interactions are transient, weak or context-dependent, making them difficult to detect. In this Review, we discuss how experimental approaches, including affinity-based methods, genetic reporter systems, proximity labelling and crosslinking, can be combined with computational tools, such as network and homology-based methods, co-evolutionary analysis, machine learning-based methods, and deep learning approaches, to map and predict protein–protein interactions. We highlight the strengths and limitations of each approach and argue that integrating experimental data with computational tools offers a promising path to comprehensive interactome mapping.</p>

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Integrating experimental and computational approaches for protein–protein interaction discovery

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Anastasia Rapti,
  • Bert J. C. Janssen,
  • Alexandre M. J. J. Bonvin

摘要

Proteins are involved in most cellular functions, typically acting as part of complexes or through dynamic interactions with other proteins and/or molecules. Understanding these interactions is therefore essential for uncovering cellular mechanisms and guiding therapeutic development. However, many interactions are transient, weak or context-dependent, making them difficult to detect. In this Review, we discuss how experimental approaches, including affinity-based methods, genetic reporter systems, proximity labelling and crosslinking, can be combined with computational tools, such as network and homology-based methods, co-evolutionary analysis, machine learning-based methods, and deep learning approaches, to map and predict protein–protein interactions. We highlight the strengths and limitations of each approach and argue that integrating experimental data with computational tools offers a promising path to comprehensive interactome mapping.