FunTree: a resource for exploring the functional evolution of structurally defined enzyme superfamilies

Furnham, Nicholas; Sillitoe, Ian; Holliday, Gemma L.; Cuff, Alison L.; Rahman, Syed Asad; Laskowski, Roman A.; Orengo, Christine A.; Thornton, Janet M.

doi:10.1093/nar/gkr852

Cited by 43 publications

(39 citation statements)

References 30 publications

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“…Section (g), Figure 1 provides a link to the FunTree (22) resource displaying phylogenetic information for enzyme superfamilies in CATH. FunTree is the product of a collaboration between the Thornton and Orengo groups and was developed and is managed by Dr Nicholas Furnham at the EBI.…”

Section: Introducing New Homologous Superfamily Pagesmentioning

confidence: 99%

New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures

Sillitoe

Cuff

Dessailly

et al. 2012

Nucleic Acids Research

203

206

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CATH version 3.5 (Class, Architecture, Topology, Homology, available at http://www.cathdb.info/) contains 173 536 domains, 2626 homologous superfamilies and 1313 fold groups. When focusing on structural genomics (SG) structures, we observe that the number of new folds for CATH v3.5 is slightly less than for previous releases, and this observation suggests that we may now know the majority of folds that are easily accessible to structure determination. We have improved the accuracy of our functional family (FunFams) sub-classification method and the CATH sequence domain search facility has been extended to provide FunFam annotations for each domain. The CATH website has been redesigned. We have improved the display of functional data and of conserved sequence features associated with FunFams within each CATH superfamily.

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Section: Introducing New Homologous Superfamily Pagesmentioning

confidence: 99%

New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures

Sillitoe

Cuff

Dessailly

et al. 2012

Nucleic Acids Research

203

206

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“…These include sequence, structure, ligand conformation, atomic properties, and putative cavity based approaches. Similarly, evolutionary analyses are possible on large and divergent superfamilies using structure‐function relationships or a combination of sequence, structure, and reaction mechanism data . However, a clustering and subsequent phylogenetic analysis based on ligand‐defined active‐sites has not been done.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, evolutionary analyses are possible on large and divergent superfamilies using structure-function relationships 21 or a combination of sequence, structure, and reaction mechanism data. 22 However, a clustering and subsequent phylogenetic analysis based on liganddefined active-sites has not been done. The Comparison of Protein Active-site Structures (CPASS) software and database compares the geometry and amino acid similarity between pairs of experimentally determined ligand-defined active-sites.…”

Section: Introductionmentioning

confidence: 99%

Functional Evolution of Proteins

et al. 2019

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The functional evolution of proteins advances through gene duplication followed by functional drift, whereas molecular evolution occurs through random mutational events. Over time, protein active‐site structures or functional epitopes remain highly conserved, which enables relationships to be inferred between distant orthologs or paralogs. In this study, we present the first functional clustering and evolutionary analysis of the RCSB Protein Data Bank (RCSB PDB) based on similarities between active‐site structures. All of the ligand‐bound proteins within the RCSB PDB were scored using our Comparison of Protein Active‐site Structures (CPASS) software and database (http://cpass.unl.edu/). Principal component analysis was then used to identify 4431 representative structures to construct a phylogenetic tree based on the CPASS comparative scores (http://itol.embl.de/shared/jcatazaro). The resulting phylogenetic tree identified a sequential, step‐wise evolution of protein active‐sites and provides novel insights into the emergence of protein function or changes in substrate specificity based on subtle changes in geometry and amino acid composition.

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“…The metallo-β-lactamase superfamily (CATH 3.60.15.10) (Sillitoe et al 2013 ) consists of a diverse set of enzymes including the A-type flavoproteins, glyoxalase IIs and the metallo-β-lactamases. These are clustered by the protein structure–function phylogeny suite FunTree (Furnham et al 2012a , b ) into two ‘Structurally Similar Groups’: ‘SSG1’, including the metallo-β-lactamases; and a second group, ‘SSG2’, structurally distinct from the first group and including the RNase Z enzymes. The metallo-β-lactamases consist of three subclasses: B1, B2 and B3 (Galleni et al 2001 ).…”

Section: Introductionmentioning

confidence: 99%

One origin for metallo-β-lactamase activity, or two? An investigation assessing a diverse set of reconstructed ancestral sequences based on a sample of phylogenetic trees

2014

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Bacteria use metallo-β-lactamase enzymes to hydrolyse lactam rings found in many antibiotics, rendering them ineffective. Metallo-β-lactamase activity is thought to be polyphyletic, having arisen on more than one occasion within a single functionally diverse homologous superfamily. Since discovery of multiple origins of enzymatic activity conferring antibiotic resistance has broad implications for the continued clinical use of antibiotics, we test the hypothesis of polyphyly further; if lactamase function has arisen twice independently, the most recent common ancestor (MRCA) is not expected to possess lactam-hydrolysing activity. Two major problems present themselves. Firstly, even with a perfectly known phylogeny, ancestral sequence reconstruction is error prone. Secondly, the phylogeny is not known, and in fact reconstructing a single, unambiguous phylogeny for the superfamily has proven impossible. To obtain a more statistical view of the strength of evidence for or against MRCA lactamase function, we reconstructed a sample of 98 MRCAs of the metallo-β-lactamases, each based on a different tree in a bootstrap sample of reconstructed phylogenies. InterPro sequence signatures and homology modelling were then used to assess our sample of MRCAs for lactamase functionality. Only 5 % of these models conform to our criteria for metallo-β-lactamase functionality, suggesting that the ancestor was unlikely to have been a metallo-β-lactamase. On the other hand, given that ancestral proteins may have had metallo-β-lactamase functionality with variation in sequence and structural properties compared with extant enzymes, our criteria are conservative, estimating a lower bound of evidence for metallo-β-lactamase functionality but not an upper bound.Electronic supplementary materialThe online version of this article (doi:10.1007/s00239-014-9639-7) contains supplementary material, which is available to authorized users.

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FunTree: a resource for exploring the functional evolution of structurally defined enzyme superfamilies

Cited by 43 publications

References 30 publications

New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures

New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures

Functional Evolution of Proteins

One origin for metallo-β-lactamase activity, or two? An investigation assessing a diverse set of reconstructed ancestral sequences based on a sample of phylogenetic trees

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