AltereGO fuses protein language models to predict protein function

[bioRxiv] A bioRxiv preprint by Amburn et al. introduces AltereGO, a framework that combines molecular function predictions from three sequence representations, ESM2-3B, ProtT5 and Ankh, and a structure representation, ProstT5-3Di, through weighted score-level fusion. The authors evaluated it on 495,055 proteins spanning 8,446 Gene Ontology molecular function terms and on a low-homology test set of 2,000 proteins with experimental or curated annotations. According to the paper, ESM2-3B was the strongest standalone representation, while four-way fusion achieved the strongest overall classification and ranking performance and outperformed the learned embedding-level fusion architectures tested. In matched comparisons with eight released prediction methods, AltereGO showed significantly improved AUPRC ranking in every comparison and significantly better Fmax and Smin in six; the remaining two showed no significant differences in those metrics. Structure-derived predictions contributed smaller, targeted gains in catalytic, redox and cofactor-associated functions.