Abstract: In cells, the ensemble of billions of reactions in a living organism takes place in heterogeneous and crowded environments that influence the efficiency of the reactivity and the density distribution of participating macromolecules in biological processes and metabolic pathways. Besides the complexity of the inner membrane compartments in Eukaryotic cells, recent advancements in microscopy and liquid phase separation make it possible to highlight some dynamical aspects of open macromolecular assemblies, referred to as membraneless organelles that are common to several types of cells working under physiological conditions (1, 2). Results support the notion that condensation mechanisms are driven by collective protein-protein and protein-nucleic acid interactions, in dynamic equilibria with the surroundings and that phase separation phenomena may indeed link microscopic to mesoscopic structural and functional characteristics of the cell milieu. In this scenario, it is even more urgent to understand which proteins can undergo the single to droplet phase transition for describing and modelling the emergent properties of the complex cell interior. I will sum up our present source of information for protein-protein interactions and briefly describe the never-ending process of generating algorithms in our (ISPRED4, https://ispred4.biocomp.unibo.it) and other groups capable of extracting information from valuable data, with the aim of transferring knowledge by computing properties of never-seen before examples (3, 4). Finally, I will focus on the interesting finding that when considering the membraneless Cajal body proteins, predicted interaction patches well correlates with number of experimentally determined interactors when the interaction patches include residues with an inherent flexibility (4).  

References:

1. Shin Y, Brangwynne CP. 2017. Liquid phase condensation in cell physiology and disease. Science 357:1253-1265.
2. Rivas G, Minton AP. 2018. Toward an understanding of biochemical equilibria within living cells. Biophys Rev. 10:241-253
3. Baldi P. 2018. Deep learning in biomedical data science. Annu.Rev.Biomed.Data.Scie 1:181-205
4. Savojardo C, Martelli PL, Casadio R. 2020. Protein-Protein interaction methods and protein phase separation. Annu.Rev.Biomed.Data.Scie 3:89-112

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