Dataset Information

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:Few studies document plants in caves. Our field observations of a widespread and seemingly angiosperm-rich cave flora in SW China lead us to test the following hypotheses, 1) SW China caves contain a diverse vascular plant flora, 2) that this is a relic of a largely absent forest type lacking endemic species, and 3) that the light environment plants occupy in caves is not distinct from non-cave habitats. To do so we surveyed 61 caves and used species accumulation curves (SAC) to estimate the total diversity of this flora and used a subsample of 14 caves to characterise the light environment. We used regional floras and existing conservation assessments to evaluate the conservation value of this flora. We used observations on human disturbance within caves to evaluate anthropogenic activities. Four-hundred-and-eighteen vascular plant species were documented with SACs predicting a total diversity of 529-846. Ninety-three percent of the species documented are known karst forest species, 7% are endemic to caves and 81% of the species are angiosperms. We demonstrate that the light environment in caves is distinct to that of terrestrial habitats and that a subset of the flora likely grow in the lowest light levels documented for vascularised plants. Our results suggest that the proportion of species threatened with extinction is like that for the terrestrial habitat and that almost half of the entrance caverns sampled showed signs of human disturbance. We believe that this is the first time that such an extensive sample of cave flora has been undertaken and that such a diverse vascular plant flora has been observed in caves which we predict occurs elsewhere in SE Asia. We argue that the cave flora is an extension of the karst forest understory present prior to catastrophic deforestation in the 20thC. We suggest that within SW China caves serve as both refuges and a valuable source of germplasm for the restoration of karst forest. We also propose that caves represent a distinct habitat for plants that is most similar to that of the forest understory, but distinct with respect to the absence of trees, leaf litter, root mats, higher levels of atmospheric CO2, and lower diurnal and annual variation in temperature and humidity. We highlight tourism, agriculture and the absence of legislated protection of caves as the main current threats to this flora.

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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