Combining coevolution-driven predictions and experiments to elucidate the complexity of protein interaction networks
Predicting protein-protein interactions and characterizing their structural organization provides essential information to elucidate the molecular mechanisms underlying cross-talk between cellular pathways. Exploiting coevolution information has emerged over the last years as a major strategy for predicting the mode of recognition between proteins. Our recent methodological developments [1, 2] have provided key insights to unravel the regulatory subtleties that might exist between pairs of interacting proteins [3, 4]. The advent of machine learning in the field, culminating in the Alphafold breakthrough, is pushing the boundaries from analyses of binary to multiple interactions. We will discuss strategies to further integrate predictions with experimental assessment and elucidate the complexity of the molecular logic behind large networks of proteins interacting together.
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