Team CoEvo

The standard way to detect interacting proteins (a method called DCA) assumes both organisms evolved together on the same family tree. But humans and the viruses and bacteria that infect us don't share an evolutionary history — so the textbook method can't be used here. We asked: are there other evolutionary "fingerprints" of protein interaction that don't need a shared tree? We found one. In SARS-CoV-2, the spike residues that grab the human ACE2 receptor mutate 4.2× faster across strains than the rest of that region — a clear, statistically strong signal, and no shared tree required. Testing it across 8 host–pathogen systems, it's strong for some (SARS-CoV-2) and absent for others (HIV, which hides its receptor site), so it's a useful clue rather than a universal rule. We also built a fair benchmark (13 species, 9,172 protein pairs) and tested prediction the hard way, by holding out an entire species the model had never seen. Standard AI protein models only transfer well when a close relative is already in the training data. Adding our evolution-based features gave a small but consistent gain — and helped most on viral proteins, exactly where the AI models struggle. Why it matters: predicting host–pathogen protein interactions drives the search for new antiviral and antibacterial drug targets, but the field's default tool doesn't work in this setting. We show which signals do survive, provide a reusable benchmark others can build on, and corrected an earlier over-claim of our own — making the result more trustworthy.

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