Fig. 1
From: Cross-protein transfer learning substantially improves disease variant prediction
Method overview. We develop computational missense variant effect predictors by training on functional assay data from very few proteins and achieve substantially improved performance over the state-of-the-art. We combine general protein sequence variation (EVE, ESM-1v), sequence variation at local evolutionary timescales (vertebrate alignments), protein structure (AlphaFold2, ProteinMPNN), and amino acid representations. We assess our models on unseen proteins across the human proteome and release predictions for all missense variants in 90% of human genes