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

Abstract

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 hierarchical 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 information. 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 webpages (http://www.cathdb.info). The FunFHMMer webserver (http://www.cathdb.info/search/by_funfhmmer) allows users to submit query sequences for assignment to a CATH FunFam.

Contact: sayoni.das.12@ucl.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Use of SDPs by FunFHMMer to infer functional coherence of cluster alignments. The coloured circles represent the node sequence clusters and each colour denotes a unique function. The schematic representation of the parent node MSA and the child nodes MSA is shown along with the phylogenetic tree. Child nodes are separated by a dashed line. Conserved positions in the MSA are shown in red and the SDPs are shown in green or yellow for different child nodes

Fig. 2.

Function prediction using CATH FunFams. Workflow for making function predictions using CATH Functional Families

Fig. 3.

EC number variation across protein classifications. Percentage of families or superfamilies having a certain number of EC terms for each of the domain-based protein classifications

Fig. 4.

UniProt rollback assessment. Performance of FunFHMMer protocol on the UniProtKB/Swiss-Prot rollback assessment dataset compared with functional annotations predicted by DFX protocol, Pfam (native) family and CDD family assignments

Fig. 5.

Network representation of the HUP Superfamily (CATH 3.40.50.620) showing available functional annotations in FunFams. The coloured nodes indicate FunFams annotated with different EC numbers and the grey nodes indicate FunFams without any Enzyme Commission (EC) annotation which include non-enzymes