AlphaFold and ESMFold gave us shape at genome scale — the ESM Metagenomic Atlas alone folds ‘more than 617 million metagenomic protein sequences.’ But a fold is not a job. Today’s digest is about the other half of the problem: reading a protein’s function from its sequence or structure. DeepGOPlus predicts GO terms ‘from sequence alone,’ fast enough to ‘annotate around 40 protein sequences per second’; ProteInfer runs a CNN that predicts EC numbers and GO terms ‘directly from an unaligned amino acid sequence,’ even in-browser with ’no data uploaded to remote servers.’ For enzymes, CLEAN uses contrastive learning to assign EC numbers ‘with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp’ — and to correct mislabeled ones. DeepFRI reads function off structure with a graph network, down to ‘site-specific annotations at the residue-level.’ And CAFA — the community benchmark that drove ’new functional annotations for more than 1000 genes’ — keeps everyone honest, admitting some categories still haven’t improved. It’s the parts list a virtual cell needs: not just what each protein looks like, but what it does.
