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Open Access Research article

Comprehensive computational analysis of Hmd enzymes and paralogs in methanogenic Archaea

Aaron D Goldman12*, John A Leigh12 and Ram Samudrala1

Author Affiliations

1 Department of Microbiology, University of Washington, Seattle, WA, USA

2 NSF IGERT Program in Astrobiology, University of Washington, Seattle, WA, USA

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BMC Evolutionary Biology 2009, 9:199  doi:10.1186/1471-2148-9-199

Published: 11 August 2009



Methanogenesis is the sole means of energy production in methanogenic Archaea. H2-forming methylenetetrahydromethanopterin dehydrogenase (Hmd) catalyzes a step in the hydrogenotrophic methanogenesis pathway in class I methanogens. At least one hmd paralog has been identified in nine of the eleven complete genome sequences of class I hydrogenotrophic methanogens. The products of these paralog genes have thus far eluded any detailed functional characterization.


Here we present a thorough computational analysis of Hmd enzymes and paralogs that includes state of the art phylogenetic inference, structure prediction, and functional site prediction techniques. We determine that the Hmd enzymes are phylogenetically distinct from Hmd paralogs but share a common overall structure. We predict that the active site of the Hmd enzyme is conserved as a functional site in Hmd paralogs and use this observation to propose possible molecular functions of the paralog that are consistent with previous experimental evidence. We also identify an uncharacterized site in the N-terminal domains of both proteins that is predicted by our methods to directly impart function.


This study contributes to our understanding of the evolutionary history, structural conservation, and functional roles, of the Hmd enzymes and paralogs. The results of our phylogenetic and structural analysis constitute datasets that will aid in the future study of the Hmd protein family. Our functional site predictions generate several testable hypotheses that will guide further experimental characterization of the Hmd paralog. This work also represents a novel approach to protein function prediction in which multiple computational methods are integrated to achieve a detailed characterization of proteins that are not well understood.