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HydDB: A web tool for hydrogenase classification and analysis

View Article: PubMed Central - PubMed

ABSTRACT

H2 metabolism is proposed to be the most ancient and diverse mechanism of energy-conservation. The metalloenzymes mediating this metabolism, hydrogenases, are encoded by over 60 microbial phyla and are present in all major ecosystems. We developed a classification system and web tool, HydDB, for the structural and functional analysis of these enzymes. We show that hydrogenase function can be predicted by primary sequence alone using an expanded classification scheme (comprising 29 [NiFe], 8 [FeFe], and 1 [Fe] hydrogenase classes) that defines 11 new classes with distinct biological functions. Using this scheme, we built a web tool that rapidly and reliably classifies hydrogenase primary sequences using a combination of k-nearest neighbors’ algorithms and CDD referencing. Demonstrating its capacity, the tool reliably predicted hydrogenase content and function in 12 newly-sequenced bacteria, archaea, and eukaryotes. HydDB provides the capacity to browse the amino acid sequences of 3248 annotated hydrogenase catalytic subunits and also contains a detailed repository of physiological, biochemical, and structural information about the 38 hydrogenase classes defined here. The database and classifier are freely and publicly available at http://services.birc.au.dk/hyddb/

No MeSH data available.


Sequence similarity network of hydrogenase sequences.Nodes represent individual proteins and the edges show the BLAST E-values between them at the logE filter defined at the bottom-left of each panel. The sequences are colored by class as defined in the legends. Figure S1 shows the further delineation of the encircled [NiFe] hydrogenase classes.
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f1: Sequence similarity network of hydrogenase sequences.Nodes represent individual proteins and the edges show the BLAST E-values between them at the logE filter defined at the bottom-left of each panel. The sequences are colored by class as defined in the legends. Figure S1 shows the further delineation of the encircled [NiFe] hydrogenase classes.

Mentions: We initially developed a classification scheme to enable prediction of hydrogenase function by primary sequence alone. To do this, we visualized the relationships between all hydrogenases in sequence similarity networks (SSN)18, in which nodes represent individual proteins and the distances between them reflect BLAST E-values. As reflected by our analysis of other protein superfamilies1920, SSNs allow robust inference of sequence-structure-function relationships for large datasets without the problems associated with phylogenetic trees (e.g. long-branch attraction). Consistent with previous phylogenetic analyses21617, this analysis showed the hydrogenase sequences clustered into eight major groups (Groups 1 to 4 [NiFe]-hydrogenases, Groups A to C [FeFe]-hydrogenases, [Fe]-hydrogenases), six of which separate into multiple functionally-distinct subgroups or subtypes at narrower logE filters (Fig. 1; Figure S1). The SSNs demonstrated that all [NiFe]-hydrogenase subgroups defined through phylogenetic trees in our previous work2 separated into distinct clusters, which is consistent with our evolutionary model that such hydrogenases diverged from a common ancestor to adopt multiple distinct functions2. The only exception were the Group A [FeFe]-hydrogenases, which, as previously-reported217, cannot be classified by sequence alone as they have principally diversified through changes in domain architecture and quaternary structure. It remains necessary to analyze the organization of the genes encoding these enzymes to determine their specific function, e.g. whether they serve fermentative or electron-bifurcating roles.


HydDB: A web tool for hydrogenase classification and analysis
Sequence similarity network of hydrogenase sequences.Nodes represent individual proteins and the edges show the BLAST E-values between them at the logE filter defined at the bottom-left of each panel. The sequences are colored by class as defined in the legends. Figure S1 shows the further delineation of the encircled [NiFe] hydrogenase classes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037454&req=5

f1: Sequence similarity network of hydrogenase sequences.Nodes represent individual proteins and the edges show the BLAST E-values between them at the logE filter defined at the bottom-left of each panel. The sequences are colored by class as defined in the legends. Figure S1 shows the further delineation of the encircled [NiFe] hydrogenase classes.
Mentions: We initially developed a classification scheme to enable prediction of hydrogenase function by primary sequence alone. To do this, we visualized the relationships between all hydrogenases in sequence similarity networks (SSN)18, in which nodes represent individual proteins and the distances between them reflect BLAST E-values. As reflected by our analysis of other protein superfamilies1920, SSNs allow robust inference of sequence-structure-function relationships for large datasets without the problems associated with phylogenetic trees (e.g. long-branch attraction). Consistent with previous phylogenetic analyses21617, this analysis showed the hydrogenase sequences clustered into eight major groups (Groups 1 to 4 [NiFe]-hydrogenases, Groups A to C [FeFe]-hydrogenases, [Fe]-hydrogenases), six of which separate into multiple functionally-distinct subgroups or subtypes at narrower logE filters (Fig. 1; Figure S1). The SSNs demonstrated that all [NiFe]-hydrogenase subgroups defined through phylogenetic trees in our previous work2 separated into distinct clusters, which is consistent with our evolutionary model that such hydrogenases diverged from a common ancestor to adopt multiple distinct functions2. The only exception were the Group A [FeFe]-hydrogenases, which, as previously-reported217, cannot be classified by sequence alone as they have principally diversified through changes in domain architecture and quaternary structure. It remains necessary to analyze the organization of the genes encoding these enzymes to determine their specific function, e.g. whether they serve fermentative or electron-bifurcating roles.

View Article: PubMed Central - PubMed

ABSTRACT

H2 metabolism is proposed to be the most ancient and diverse mechanism of energy-conservation. The metalloenzymes mediating this metabolism, hydrogenases, are encoded by over 60 microbial phyla and are present in all major ecosystems. We developed a classification system and web tool, HydDB, for the structural and functional analysis of these enzymes. We show that hydrogenase function can be predicted by primary sequence alone using an expanded classification scheme (comprising 29 [NiFe], 8 [FeFe], and 1 [Fe] hydrogenase classes) that defines 11 new classes with distinct biological functions. Using this scheme, we built a web tool that rapidly and reliably classifies hydrogenase primary sequences using a combination of k-nearest neighbors’ algorithms and CDD referencing. Demonstrating its capacity, the tool reliably predicted hydrogenase content and function in 12 newly-sequenced bacteria, archaea, and eukaryotes. HydDB provides the capacity to browse the amino acid sequences of 3248 annotated hydrogenase catalytic subunits and also contains a detailed repository of physiological, biochemical, and structural information about the 38 hydrogenase classes defined here. The database and classifier are freely and publicly available at http://services.birc.au.dk/hyddb/

No MeSH data available.