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...

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Autores principales:	Oliver C Redfern, Benoît H Dessailly, Timothy J Dallman, Ian Sillitoe, Christine A Orengo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c195f611f8764ff080c12c4f6af5ce76
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spelling	oai:doaj.org-article:c195f611f8764ff080c12c4f6af5ce762021-11-25T05:42:12ZFLORA: a novel method to predict protein function from structure in diverse superfamilies.1553-734X1553-735810.1371/journal.pcbi.1000485https://doaj.org/article/c195f611f8764ff080c12c4f6af5ce762009-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19714201/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Predicting 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.Oliver C RedfernBenoît H DessaillyTimothy J DallmanIan SillitoeChristine A OrengoPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 8, p e1000485 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Oliver C Redfern Benoît H Dessailly Timothy J Dallman Ian Sillitoe Christine A Orengo FLORA: a novel method to predict protein function from structure in diverse superfamilies.
description	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.
format	article
author	Oliver C Redfern Benoît H Dessailly Timothy J Dallman Ian Sillitoe Christine A Orengo
author_facet	Oliver C Redfern Benoît H Dessailly Timothy J Dallman Ian Sillitoe Christine A Orengo
author_sort	Oliver C Redfern
title	FLORA: a novel method to predict protein function from structure in diverse superfamilies.
title_short	FLORA: a novel method to predict protein function from structure in diverse superfamilies.
title_full	FLORA: a novel method to predict protein function from structure in diverse superfamilies.
title_fullStr	FLORA: a novel method to predict protein function from structure in diverse superfamilies.
title_full_unstemmed	FLORA: a novel method to predict protein function from structure in diverse superfamilies.
title_sort	flora: a novel method to predict protein function from structure in diverse superfamilies.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/c195f611f8764ff080c12c4f6af5ce76
work_keys_str_mv	AT olivercredfern floraanovelmethodtopredictproteinfunctionfromstructureindiversesuperfamilies AT benoithdessailly floraanovelmethodtopredictproteinfunctionfromstructureindiversesuperfamilies AT timothyjdallman floraanovelmethodtopredictproteinfunctionfromstructureindiversesuperfamilies AT iansillitoe floraanovelmethodtopredictproteinfunctionfromstructureindiversesuperfamilies AT christineaorengo floraanovelmethodtopredictproteinfunctionfromstructureindiversesuperfamilies
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FLORA: a novel method to predict protein function from structure in diverse superfamilies.

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