Abstract
AlphaFold2 predicts protein structures from structural and functional knowledge. Alternatively, ESMFold does the same adopting protein language models. Here, we map available Pfam domains on pairs of models of the human reference proteome computed with both procedures and we compare the mapped regions relevant for functional annotation. We find that, rather irrespectively of the global superimposition of the pairwise models, Pfam-containing regions overlap with a TM-score above 0.8 and a predicted local distance difference test (pLDDT) which is higher than the rest of the modeled sequence. This indicates that both methods are similarly performing in modeled regions that overlap Pfam domains, carrying structural and functional information, with pLDDT values slightly higher for AlphaFold2. The mapping of 9834 Pfam domains also allows the location of 2578 active sites in 3382 enzymes of the human proteome, including 807 proteins for which the active site is not reported in UniProt.