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FLORA: a novel method to predict protein function from structure in diverse superfamilies.

ABSTRACT: 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.

SUBMITTER: Redfern OC

PROVIDER: S-EPMC2721411 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

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FLORA: a novel method to predict protein function from structure in diverse superfamilies.

Redfern Oliver C OC Dessailly Benoît H BH Dallman Timothy J TJ Sillitoe Ian I Orengo Christine A CA

PLoS computational biology 20090828 8

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 embe ...[more]

PMID: 19714201

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Project description:BackgroundRelated protein domains of a superfamily can be specified by proteins of diverse lengths. The structural and functional implications of indels in a domain scaffold have been examined.MethodologyIn this study, domain superfamilies with large length variations (more than 30% difference from average domain size, referred as 'length-deviant' superfamilies and 'length-rigid' domain superfamilies (<10% length difference from average domain size) were analyzed for the functional impact of such structural differences. Our delineated dataset, derived from an objective algorithm, enables us to address indel roles in the presence of peculiar structural repeats, functional variation, protein-protein interactions and to examine 'domain contexts' of proteins tolerant to large length variations. Amongst the top-10 length-deviant superfamilies analyzed, we found that 80% of length-deviant superfamilies possess distant internal structural repeats and nearly half of them acquired diverse biological functions. In general, length-deviant superfamilies have higher chance, than length-rigid superfamilies, to be engaged in internal structural repeats. We also found that approximately 40% of length-deviant domains exist as multi-domain proteins involving interactions with domains from the same or other superfamilies. Indels, in diverse domain superfamilies, were found to participate in the accretion of structural and functional features amongst related domains. With specific examples, we discuss how indels are involved directly or indirectly in the generation of oligomerization interfaces, introduction of substrate specificity, regulation of protein function and stability.ConclusionsOur data suggests a multitude of roles for indels that are specialized for domain members of different domain superfamilies. These specialist roles that we observe and trends in the extent of length variation could influence decision making in modeling of new superfamily members. Likewise, the observed limits of length variation, specific for each domain superfamily would be particularly relevant in the choice of alignment length search filters commonly applied in protein sequence analysis.

Dataset Information

FLORA: a novel method to predict protein function from structure in diverse superfamilies.

Publications

FLORA: a novel method to predict protein function from structure in diverse superfamilies.

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