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Structural prediction by artificial intelligence (AI) can be powerful new instruments to discover novel protein-protein interactions, but the community still grapples with the implementation, opportunities and limitations. Here, we discuss and re-analyse our in-silico screen for novel pathogen-secreted inhibitors of immune hydrolases to illustrate the power and limitations of structural predictions. We discuss strategies of curating sequences, including controls, and reusing sequence alignments and highlight important limitations originating from platforms, sequence depth and computing times. We hope these experiences will support similar interactomic screens by the research community.