FLORA: a novel method to predict protein function from structure in diverse superfamilies.
Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2-3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (alpha, beta, alphabeta) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues.
Redfern
Oliver C
OC
Research Department of Structural and Molecular Biology, University College London, London, United Kingdom.
Dessailly
Benoît H
BH
Dallman
Timothy J
TJ
Sillitoe
Ian
I
Orengo
Christine A
CA
D000465:Algorithms; D020816:Amino Acid Motifs; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D023281:Genomics; D005810:Multigene Family; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods; D012372:ROC Curve; D015203:Reproducibility of Results
D011506:Proteins