Sequence analysis Functional classification of CATH superfamilies: a domain-based approach for protein function annotation

Motivation: Computational approaches that can predict protein functions are essential to bridge the widening function annotation gap especially since <1.0 % of all proteins in UniProtKB have been experimentally characterized. We present a domain-based method for protein function classification and prediction of functional sites that exploits functional sub-classification of CATH superfamilies. The superfamilies are sub-classified into functional families (FunFams) using a hier-archical clustering algorithm supervised by a new classification method, FunFHMMer. Results: FunFHMMer generates more functionally coherent groupings of protein sequences than other domain-based protein classifications. This has been validated using known functional infor-mation. The conserved positions predicted by the FunFams are also found to be enriched in known functional residues. Moreover, the functional annotations provided by the FunFams are found to be more precise than other domain-based resources. FunFHMMer currently identifies 110 439 FunFams in 2735 superfamilies which can be used to functionally annotate> 16 million domain sequences. Availability and implementation: All FunFam annotation data are made available through the CATH webpage