Redfern, OC;
Dessailly, BH;
Dallman, TJ;
Sillitoe, I;
Orengo, CA;
(2009)
FLORA: A Novel Method to Predict Protein Function from Structure in Diverse Superfamilies.
PLOS COMPUT BIOL
, 5
(8)
, Article e1000485. 10.1371/journal.pcbi.1000485.
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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, alpha beta) 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.
| Type: | Article |
|---|---|
| Title: | FLORA: A Novel Method to Predict Protein Function from Structure in Diverse Superfamilies |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1000485 |
| Publisher version: | http://dx.doi.org/10.1371/journal.pcbi.1000485 |
| Language: | English |
| Additional information: | © 2009 Redfern et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by an NIH Structural Genomics grant (DE-AC02-06CH11357), as part of the Midwest Consortium for Structural Genomics. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
| Keywords: | STRUCTURE ALIGNMENT, BINDING-SITES, FUNCTION ANNOTATION, DATABASE SEARCH, 3D TEMPLATES, ENZYME, SEQUENCE, CLASSIFICATION, EVOLUTION, GENOMICS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1298878 |
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