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 (α, β, αβ) 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.Author Summary: Understanding how the three-dimensional (3D) molecular structure of proteins influences their function can provide insights into the workings of biological systems. Structural Genomics Initiatives have been set up to investigate these structures on a large scale and make the data available to the wider biological research community. However, in a significant number of cases, there is little known about the functions of the structures that are solved. To address this, computational methods can be used as a predictive tool to guide future experimental investigations. One such approach is to exploit global structural comparison to assign the protein in question to an evolutionary family, which has already been functionally characterised. However, this is problematic in some large evolutionary families, which contain a number of different functional sub-families. We have developed a new method (FLORA) which is able to calculate 3D “motifs” which are specific to each of these sub-families. Any new protein structure can then be compared against these motifs to make a more accurate prediction of its function. Our paper shows that FLORA substantially outperforms other standard approaches for predicting function from structure. We use our method to make confident functional predictions for a set of proteins solved by the structural genomics projects, which could not have been assigned reliably by global structure comparison.