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Annual Review of Biomedical Data Science

Volume 3, 2020

Protein–Protein Interaction Methods and Protein Phase Separation

Abstract

In the last decade, newly developed experimental methods have made it possible to highlight that macromolecules in the cell milieu physically interact to support physiology. This has shifted the problem of protein–protein interaction from a microscopic, electron-density scale to a mesoscopic one. Further, nowadays there is increasing evidence that proteins in the nucleus and in the cytoplasm can aggregate in membraneless organelles for different physiological reasons. In this scenario, it is urgent to face the problem of biomolecule functional annotation with efficient computational methods, suited to extract knowledge from reliable data and transfer information across different domains of investigation. Here, we revise the present state of the art of our knowledge of protein–protein interaction and the computational methods that differently implement it. Furthermore, we explore experimental and computational features of a set of proteins involved in phase separation.

Keyword(s): Cajal body, computational methods, deep learning, intrinsically disordered proteins, machine learning, PPI database, PPI networks, PPI prediction, protein interacting surfaces, protein phase separation, protein–protein interaction
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/content/journals/10.1146/annurev-biodatasci-011720-104428
2020-07-20
2024-05-06
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/content/journals/10.1146/annurev-biodatasci-011720-104428
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Protein–Protein Interaction Methods and Protein Phase Separation
Annual Review of Biomedical Data Science 3, 89 (2020); https://doi.org/10.1146/annurev-biodatasci-011720-104428
/content/journals/10.1146/annurev-biodatasci-011720-104428
/content/journals/10.1146/annurev-biodatasci-011720-104428
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