Comprehensive computational analysis of Hmd enzymes and paralogs in methanogenic Archaea
© Goldman et al; licensee BioMed Central Ltd. 2009
Received: 24 November 2008
Accepted: 11 August 2009
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.
The methanogens are a diverse, but phylogenetically related, group of Archaea. Methanogenic Archaea have been isolated from habitats ranging from mammalian gut flora to deep sea hydrothermal vents. Methanogens are comprised of two taxonomic classes known as class I and class II [1–3]. Class I methanogens include the orders Methanococcales, Methanobacteriales, and Methanopyrales, while class II methanogens include the orders Methanosarcinales and Methanomicrobiales.
The fourth step in the hydrogenotrophic methanogenesis of class I methanogens involves the reduction of N5,N10-methenyltetrahydromethanopterin (methenyl-H4MPT) to N5,N10-methylene-H4MPT. Class II methanogens differ in their use of methanosarcinapterin rather than H4MPT as the C1 carrier. This step in class I methanogens can be carried out by either of two different enzymes. Coenzyme F420-dependent methylene-H4MPT dehydrogenase (Mtd) reduces methenyl-H4MPT using reduced coenzyme F420 as the electron donor. H2-forming methylene-H4MPT dehydrogenase (Hmd) reduces methenyl-H4MPT to methylene-H4MPT using H2 as an electron source. Afting et al.  observed in Methanothermobacter marbugensis that Hmd has a specific activity greater than that of Mtd under nickel-limited, ammonia-limited, and non-limited conditions while Mtd has a specific activity greater than that of Hmd under hydrogen-limited conditions. Hendrickson et al.  observed in Methanococcus maripaludis that hmd is upregulated proportional to growth rate and mtd is upregulated under hydrogen limitation.
The Hmd holoenzyme is comprised of a homodimer of 38 kDa subunits, two pyridone derivative cofactor molecules, and two iron atoms . Each iron atom coordinates the reduction of methenyl-H4MPT and oxidation of H2 while bound to both Hmd and a cofactor molecule [8, 9]. The apoenzyme of Hmd is stable and can be restored to active holoenzyme by the addition of cofactor . Hmd is the only known hydrogenase that lacks an iron-sulfur cluster and is sometimes referred to as the 'iron-sulfur cluster-free hydrogenase'.
Almost all genomes of class I hydrogenotrophic methanogens contain both an hmd enzyme gene and at least one hmd paralog gene. Several species have two copies of the hmd paralog (referred to in this manuscript with arbitrary numeration as paralog1 and paralog2; see Additional file 1). Afting et al.  first showed in M. marburgensis that the protein products of hmd paralogs are present in the cell. Their study also revealed that Hmd paralog1 is detectable at low H2, while Hmd paralog2 is detectable at high H2 and that neither paralog show any observable hydrogenase activity. Recent unpublished work mentioned in a review by Shima and Thauer  indicates that Hmd paralog1 from Methanocaldococcus jannaschii can competitively bind cofactor and inhibit the activation of Hmd apoenzyme. Curiously, Hmd paralog1 in M. jannaschii was shown by Lipman et al.  to specifically bind prolyl-tRNA synthetase. While these results taken together constitute a partial characterization of Hmd paralogs, our understanding of these proteins and their role in methanogenesis is far from complete.
Here we present advanced computational analyses of Hmd enzymes and their paralogs from the genomes of sixteen class I hydrogenotrophic methanogens. The relationship of hmd enzyme and paralog sequences is demonstrated through phylogenetic analysis. The tertiary structures of Hmd enzymes and paralogs from five representative species are predicted using the top ranking modeling server of the last two CASP competitions [; http://predictioncenter.org/casp8/]. Functional characterization of the Hmd paralogs is performed using a state of the art method recently developed by our group . Taken together, these analyses form a thorough computational characterization of the Hmd enzymes and paralogs and generate several testable hypotheses regarding the molecular functions of both Hmd enzymes and paralogs.
Results and discussion
An exhaustive search for hmd genes was performed using PSI-BLAST  and the MetaCyc multi-genome browser . This process identified thirty hmd enzyme and paralog sequences from sixteen species and strains of class I hydrogenotrophic methanogens. Several methanogen prephenate dehydrogenase genes were also identified by our search. We use these genes as a phylogenetic outgroup in the subsequent analysis. Complete genome sequences are available for eleven of the sixteen species and strains. Of these eleven, only the genomes of Methanocorpusculum labreanum and Methanobrevibacter smithii contain an hmd enzyme but not an hmd paralog. All Methanococcus spp. have only one hmd paralog gene, while Methanocaldococcus jannaschii, Methanothermobacter marburgensis, Methanothermobacter thermautotrophicus, and Methanopyrus kandleri have two hmd paralog genes. No species was found to have an hmd paralog, but not an hmd enzyme. Features of these genes, their GenInfo Identifiers, and their associated references [[16–23]; Copeland et al., unpublished data; Hartmann and Thauer, direct submission to NCBI databases 1996] are presented in Additional file 1. A ClustalW2 alignment of the protein sequences of these genes is included as Additional file 2.
In all three trees, Hmd enzymes and paralogs form two distinct monophyletic groups. Curiously, the Hmd enzyme and paralog subtrees are considerably dissimilar regarding the placement of M. jannaschii sequences. These sequences are more basal in the paralog subtree than the enzyme subtree (with the exception of Hmd paralog1 in the Phylip tree). Bifurcation patterns in the tree suggest that paralog duplication has taken place independently in the lineages leading to M. jannaschii, M. kandleri, and the last common ancestor of M. marburgensis and M. thermautotrophicus. The two Hmd paralogs of M. jannaschii are paraphyletic in the PhyML and MrBayes trees and polyphyletic in the Phylip tree. The paralog duplicates of M. kandleri and the last common ancestor of M. marburgensis and M. thermautotrophicus both produce monophyletic topologies. It should be noted that M. marburgensis and M. thermautotrophicus were considered strains of a single species until recently .
These trees do not provide a conclusive explanation for the lack of a paralog sequence in M. labraenum or M. smithii. M. labraenum and M. smithii enzyme sequences are not basally branching, but were inherited from the last common ancestor of these species and the Methanothermobacter genus. Given that the M. kandleri paralog sequences appear in a subtree with the other paralog sequences, rather than branching from the base of the tree, it is likely that both M. labraenum and M. smithii lost the Hmd paralog late in evolution. It is therefore probable, but not certain, that the last common ancestor of all class I methanogens had both an Hmd enzyme and paralog.
Tertiary structure models of fourteen representative Hmd enzymes and paralogs were generated with I-TASSER [27, 28], which was the best performing structure modeling server in the two most recent CASP competitions [, http://predictioncenter.org/casp8/]. The I-TASSER algorithm is an advanced modeling method that searches the SCOP database  for parent template structures, uses these parent structures to comparatively model short segments of the query protein, and connects these segments using de novo modeling techniques. Because the modeling is not dependent on comparison to a single homolog, this method can be considered a form of de novo structure modeling.
Features of Hmd enzyme and paralog structure models
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All models are composed of two distinct folding regions, a 200–300 amino acid N-terminal domain which contains both α-helices and β-sheets and a ~50 amino acid C-terminal domain containing only α-helices. According to the diffraction structure of the Hmd enzyme, catalytic activity takes place within the N-terminal domains while dimerization occurs between the C-terminal domains of subunits [8, 9]. To gauge the structural conservation between Hmd enzymes and paralogs, root mean square deviations (RMSDs) between the models and the diffraction structure were calculated with respect to the whole protein, the N-terminal domain only, and the C-terminal domain only.
The RMSD between model and diffraction structure is significantly lower with respect to C-terminal domains than N-terminal domains for 10 out of 21 models. These models are Hmd enzymeRC from M. kandleri, Hmd paralog1-R, Hmd paralog1-C, and Hmd paralog2-R from M. thermautotrophicus, Hmd paralog2-R and Hmd paralog2-C from M. marburgensis, Hmd paralogR from M. maripaludis, Hmd paralog1-R, Hmd paralog2-R, and Hmd paralog2-C from M. jannaschii, and Hmd paralog2-R and Hmd paralog2-C from M. kandleri. The RMSD of the C-terminal domains of the Hmd enzymeRC from M. maripaludis and the diffraction structure of Hmd was higher than that of the N-terminal domain. ClustalW2 multiple sequence alignments  of the query protein with its I-TASSER parent structures are available as Additional file 4. Visual analysis of these alignments suggests that the modeling is not biased towards one of the two domains due to sequence similarity with the parent structures. These results therefore indicate that the C-terminal domain is more structurally conserved between Hmd enzyme and paralog than the N-terminal domain.
Function prediction by Protinfo MFS comparison
The Meta-Functional Signature score (MFS) was used in conjunction with multiple sequence alignment to predict functional sites and functional similarity between Hmd enzymes and paralogs. MFS is part of the Protinfo suite of algorithms http://protinfo.compbio.washington.edu/ and predicts the functional sites of a protein with higher accuracy than other currently available algorithms . For a given protein, the MFS algorithm quantifies and measures multiple orthogonal features of each amino acid pertaining to either the evolutionary conservation of the amino acid, the contribution of the amino acid to structural integrity, or the frequency in which the residue type itself is found in known functional sites. These features are combined to give the MFS score, which represents the probability that a given amino acid contributes directly to function.
Four of the five alignment positions in which multiple putative functional residues are conserved between Hmd enzymes and paralogs cluster into a single distinct region (Figure 5B). This cluster is comprised of H174, C176, T177, and H201 in Hmd enzyme and N125, C127, T128, and H154 in Hmd paralog1 from M. jannaschii (Figure 5C). In the Hmd enzyme from M. jannaschii, C176 was previously demonstrated to bind the cofactor and coordinate the iron and substrate [8, 9]. This cluster of putative functional sites therefore represents the active site of the Hmd enzyme. The H174 residue of the Hmd enzyme corresponds to the N125 residue of Hmd paralog1. Thus the functional importance of this site appears to be conserved while the residue type itself is not. These results are consistent with the independent observations that the Hmd paralog1 of M. jannaschii is able to competitively bind the Hmd cofactor  and that both Hmd paralogs of M. marburgensis are unable to catalyze a hydrogenase/dehydrogenase reaction  (see Background). A second predicted common functional site between Hmd enzymes and paralogs is comprised of a single amino acid, D143 in Hmd enzyme and E94 in Hmd paralog1 (Figure 5D). The functional relevance of this region is yet unknown. There is no experimental evidence that all Hmd paralogs are functionally equivalent. Our analysis however is not dependent on all Hmd paralogs having a single common function. Rather all Hmd paralogs are predicted here to have a common ancestral function and still maintain common features of function, such as the locations of functional sites.
Lipman et al.  demonstrated that Hmd paralog1 from M. jannaschii specifically binds prolyl-tRNA synthetase. The biological significance of this binding has not been examined in a published study since this initial work. Lipman et al. observed that mutations V248A and L252A reduced this binding 4-fold. In our MFS calculation for Hmd paralog1 from M. jannaschii, V248 has a score of 0.05 and L252 has a score of 0.22. Val and Leu are typically not conserved within protein-protein binding "hot spots" . It may be the case that V248 and L252 represent structurally important residues in Hmd paralog1 that do not contribute directly to function. Thus, our MFS analysis cannot confirm the biological relevance of Hmd paralog1 binding to prolyl-tRNA synthetase in M. jannaschii.
This study offers an in depth computational analysis of the relationship between the sequences, structures, and functional features of Hmd enzymes and paralogs in class I hydrogenotrophic methanogens. Phylogenetic analysis of thirty hmd enzyme and paralog genes from sixteen species and strains confirms that the genetic predecessors of modern Hmd enzymes and paralogs were present in the last common ancestor of all class I hydrogenotrophic methanogens. Structural modeling of fourteen representative Hmd enzymes and paralogs reveals a common structural arrangement comprised of one large N-terminal domain containing α-helices and β-sheets and one smaller C-terminal domain containing only α-helices.
Functional site prediction was performed by the calculation of Meta-Functional Signature (MFS) scores for the fourteen modeled Hmd enzymes and paralogs . MFS comparison across a multiple sequence alignment revealed five functional sites conserved between Hmd enzymes and paralogs. The superimposition of these sites onto representative structures of the Hmd enzyme and paralog showed that the enzyme active site is maintained as a functional site in the paralog. One of the four functionally conserved residues in this functional site is a His in Hmd enzymes and an Asn in most Hmd paralogs. We conclude from these observations that the molecular function of the Hmd paralog is similar but not identical to the enzyme. Our analysis also predicted a second site of common function between Hmd enzymes and paralogs that is yet uncharacterized. Our MFS data did not substantiate the observation of Lipman et al.  that Hmd paralog1 in M. jannaschii specifically binds to prolyl-tRNA synthetase.
Previous experimental work has demonstrated that Hmd paralogs do not enzymatically catalyze hydrogenase/dehydrogenase reactions , but are able to competitively bind the Hmd enzyme cofactor . Our results indicate that the catalytic site of the Hmd enzyme is conserved as a functional site in Hmd paralogs, but that the molecular function of the paralog differs from that of the enzyme due to at least one key amino acid substitution. Given these observations, it is possible that the Hmd paralog is responsible for acting as a reservoir for the Hmd enzyme cofactor when H2 is low and the Mtd reaction is favored over the Hmd reaction (see Background). Alternatively, the Hmd paralog may act as a scaffold for cofactor synthesis. These hypotheses warrant experimental verification.
The datasets and predictions generated in this study provide a guide for future experimental characterization of the Hmd protein family. This work also serves as an example of detailed protein function prediction that can be achieved by the combination of multiple independent computational techniques. We are currently working to optimize and generalize the method presented here. Such an approach will increase the accuracy of protein function prediction and help to guide the early steps of experimental protein characterization.
Thirty Hmd enzyme and paralog sequences from sixteen species and strains were identified using the NCBI implemetation of PSI-BLAST  and the multi-genome browser on the MetaCyc server . Three sequences of methanogen prephenate dehydrogenase were also identified and used as an outgroup in the phylogenetic analysis. The boundary between N-terminal and C-terminal domains that is presented in Additional file 1 was ascertained by extrapolating this boundary in the diffraction structure of the Hmd enzyme from M. jannaschii across a ClustalW2 multiple sequence alignment  of all thirty Hmd sequences. Sequence identities between each pair of proteins were calculated by ClustalW . All of these data are summarized in Additional file 1 along with references and GenInfo Identifiers for each sequence.
Phylogenies were generated separately using the PhyML webserver , the Phylip software package , and the MrBayes software package . The PhyML phylogeny was calculated using the maximum likelihood method  and the WAG substitution matrix , which was recommended on the server website. Confidence scores for each branch are bootstrap support values obtained from 500 independent resamplings of alignment positions. The Phylip phylogeny was calculated using the neighbor joining method  and the JTT substitution matrix . Confidence scores for each branch are bootstrap support values obtained from 1,000 independent resamplings of alignment positions. The MrBayes phylogenetic trees were calculated using mixed models of amino acid substitution , which converged after 10,000 iterations in 100% usage of the WAG substitution model . The MrBayes tree and conditional probability values of the corresponding branches were estimated from 750 tree topologies sampled along 7,500 iterations, following 2,500 burn-in iterations. All three trees were drawn using the Retree and Drawgram programs from the Phylip software package . Trees were relabeled for clarity using graphics editors.
Structures were modeled using the I-TASSER webserver, which was determined to be the most accurate structure prediction server in both the CASP7 and CASP8 competitions [12, 27, 28]. The algorithm threads the query sequence through experimentally solved structures in the SCOP database  in order to identify up to five parent structures to be used as comparative modeling templates. Comparative modeling is used to model short segments of the query protein. These segments are then attached by physics-based de novo modeling.
I-TASSER returns five models for each amino acid sequence. For all proteins, the most accurate model in each set of five was determined using either the C-score, which is internal to I-TASSER , or the residue-specific all-atom probability discriminatory function (RAPDF) . Both of these scoring functions measure the likelihood that a given model is correct with respect to other models of the same protein. C-score is calculated by clustering the thousands of intermediate structures produced during the I-TASSER run. The score is determined by the size of the cluster surrounding each model. RAPDF determines the quality of a model by calculating the sum of logodds scores for all interatomic distances within the model derived from frequencies observed in diffraction structures. The I-TASSER models of Hmd paralog2 from M. jannaschii had a disconnected main chain. The main chains of these models were made congruent by comparative modeling using the I-TASSER models as templates. This comparative modeling was performed with Protinfo CM [39, 40]. Details of all fourteen models are presented in Table 1. Root mean square deviations (RMSDs) of all heavy atoms between the models were calculated using the compare_structures program in the RAMP modeling suite http://www.ram.org/computing/ramp/. A concatenated PDB formatted file of the models is available as Additional file 3.
Function prediction by Protinfo MFS comparison
Meta-Functional Signature (MFS) scores were calculated for each protein using the Protinfo MFS algorithm [13, 41]. For a given protein, the MFS algorithm quantifies multiple orthogonal features of each amino acid that pertain to either the evolutionary conservation of the residue, the contribution of the residue to the structural integrity of the protein, or the frequency of the residue type in previously characterized functional sites. These features are combined to produce a score from zero to one that represents the likelihood that the residue is a functional site. Raw MFS data for each modeled protein are available as Additional file 5.
The top ten MFS scoring residues from each protein were considered putative functional sites. A multiple sequence alignment of the corresponding sequences was generated using ClustalW2 . The number of putative functional sites appearing in each alignment position was tallied. Alignment positions in which at least 40% of either Hmd enzymes or paralogs had a putative functional site were identified on representative structures from M. jannaschii using the Pymol molecular viewer . An unabridged multiple sequence alignment with highlighted putative functional sites from each protein is available as Additional file 6.
Many thanks to Laura Heath and Sujay Chattopadhyay for critical review of the phylogenetic analysis presented in this manuscript. Thanks also to Jeremy Horst, Michal Guerkuin, Tom Lie, and Jeremy Dodsworth for helpful discussions. This work was funded by the University of Washington NSF IGERT program in Astrobiology and the NSF Career Award DBI-0217241.
- Bapteste E, Brochier C, Boucher Y: Higher-level classification of the Archaea: evolution of methanogenesis and methanogens. Archaea. 2005, 1: 353-363.PubMed CentralView ArticlePubMedGoogle Scholar
- Gribaldo S, Brochier-Armanet C: The origin and evolution of Archaea: a state of the art. Phil Trans R Soc B. 2006, 361: 1007-1022.PubMed CentralView ArticlePubMedGoogle Scholar
- Gao B, Gupta RS: Phylogenomic analysis of proteins that are distinctive of Archaea and its main subgroups and the origin of methanogenesis. BMC Genomics. 2007, 8: 86-PubMed CentralView ArticlePubMedGoogle Scholar
- Deppenmeier U: The unique biochemistry of methanogenesis. Progr Nucleic Acid Res Mol Biol. 2002, 71: 223-283.View ArticleGoogle Scholar
- Reeve JN, Nolling J, Morgan RM, Smith DR: Methanogenesis: genes, genomes and whose on first?. J Bacteriol. 1997, 179 (19): 5975-5986.PubMed CentralPubMedGoogle Scholar
- Afting C, Kremmer E, Brucker C, Hochheimer A, Thauer RK: Regulation of the synthesis of H2-forming methylenetetrahydromethanopterin dehydrogenase (Hmd) and of HmdII and HmdIII in Methanothermobacter marburgensis. Arch Microbiol. 2000, 174: 225-232.View ArticlePubMedGoogle Scholar
- Hendrickson E, Haydock A, Moore B, Whitman W, Leigh J: Functionally distinct genes regulated by hydrogen limitation and growth rate in methanogenic Archaea. Proc Natl Acad Sci. 2007, 104: 8930-8934.PubMed CentralView ArticlePubMedGoogle Scholar
- Pilak O, Mamat B, Vogt S, Hagemeier CH, Thaur RK, Shima S, Vonhrein C, Warkentin E, Ermler U: The crystal structure of the apoenzyme of the iron-sulphur cluster-free hydrogenase. J Mol Biol. 2006, 358: 798-809.View ArticlePubMedGoogle Scholar
- Korbas M, Vogt S, Meyer-Klaucke W, Bill E, Lyon EJ, Thauer RK, Shima S: The iron-sulfur cluster-free hydrogenase (Hmd) is a metalloenzyme with a novel iron binding motif. J Biol Chem. 2006, 281 (41): 30804-30813.View ArticlePubMedGoogle Scholar
- Shima S, Thauer RK: A third type of hydrogenase catalyzing H2 activation. Chem Rec. 2007, 7: 37-46.View ArticlePubMedGoogle Scholar
- Lipman RS, Chen J, Evilia C, Vitseva O, Hou Y-A: Association of an aminoacyl-tRNA synthetase with a putative metabolic protein in archaea. Biochemistry. 2003, 42: 7487-7496.View ArticlePubMedGoogle Scholar
- Zhang Y: Template-based modeling and free modeling by I-TASSER in CASP7. Proteins. 2007, 69 (Suppl 8): 108-117.View ArticlePubMedGoogle Scholar
- Wang K, Horst J, Cheng G, Nickle D, Samudrala R: Protein meta-functional signatures from combining sequence, structure, evolution and amino acid property information. PLoS Comput Biol. 2008, 4 (9): e1000181-PubMed CentralView ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25 (17): 3389-3402.PubMed CentralView ArticlePubMedGoogle Scholar
- Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, Paley S, Rhee SY, Shearer AG, Tissier C, Walk TC, Zhang P, Karp PD: The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2008, 36: D623-D631.PubMed CentralView ArticlePubMedGoogle Scholar
- Bult CJ, White O, Olsen GJ, Zhou L, Fleischmann RD, Sutton GG, Blake JA, FitzGerald LM, Clayton RA, Gocayne JD, Kerlavage AR, Dougherty BA, Tomb JF, Adams MD, Reich CI, Overbeek R, Kirkness EF, Weinstock KG, Merrick JM, Glodek A, Scott JL, Geoghagen NS, Venter JC: Complete genome sequence of the methanogenic archaeon, Methanococcus jannaschii. Science. 1996, 273 (5278): 1058-1073.View ArticlePubMedGoogle Scholar
- Hartmann GC, Klein AR, Linder M, Thauer RK: Purification, properties and primary structure of H2-forming N5,N10-methylenetetrahydromethanopterin dehydrogenase from Methanococcus thermolithotrophicus. Arch Microbiol. 1996, 165 (3): 187-193.PubMedGoogle Scholar
- Hendrickson EL, Kaul R, Zhou Y, Bovee D, Chapman P, Chung J, de Macario EC, Dodsworth JA, Gillett W, Graham DE, Hackett M, Haydock AK, Kang A, Land ML, Levy R, Lie TJ, Major TA, Moore BC, Porat I, Palmeiri A, Rouse G, Saenphimmachak C, Söll D, Van Dien S, Wang T, Whitman WB, Xia Q, Zhang Y, Larimer FW, Olson MV, Leigh JA: Complete Genome Sequence of the Genetically Tractable Hydrogenotrophic Methanogen Methanococcus maripaludis. J Bacteriol. 2004, 186 (20): 6956-6969.PubMed CentralView ArticlePubMedGoogle Scholar
- Nolling J, Pihl TD, Vriesema A, Reeve JN: Organization and growth phase-dependent transcription of methane genes in two regions of the Methanobacterium thermautotrophicus genome. J Bacteriol. 1995, 177 (9): 2460-2468.PubMed CentralPubMedGoogle Scholar
- Smith DR, Doucette-Stamm LA, Deloughery C, Lee H, Dubois J, Aldredge T, Bashirzadeh R, Blakely D, Cook R, Gilbert K, Harrison D, Hoang L, Keagle P, Lumm W, Pothier B, Qiu D, Spadafora R, Vicaire R, Wang Y, Wierzbowski J, Gibson R, Jiwani N, Caruso A, Bush D, Reeve JN: Complete genome sequence of Methanobacterium thermautotrophicus deltaH: functional analysis and comparative genomics. J Bacteriol. 1997, 179 (22): 7135-7155.PubMed CentralPubMedGoogle Scholar
- Wasserfallen A, Nölling J, Pfister P, Reeve J, de Macario EC: Phylogenetic analysis of 18 thermophilic Methanobacterium isolates supports the proposals to create a new genus, Methanothermobacter gen. nov., and to reclassify several isolates in three species, Methanothermobacter thermautotrophicus comb. nov., Methanothermobacter wolfeii comb. nov., and Methanothermobacter marburgensis sp. nov. Int J Syst Evol Microbiol. 2000, 50: 43-53.View ArticlePubMedGoogle Scholar
- Samuel BS, Hansen EE, Manchester JK, Coutinho PM, Henrissat B, Fulton R, Latreille P, Kim K, Wilson RK, Gordon JI: Genomic and metabolic adaptations of Methanobrevibacter smithii to the human gut. Proc Natl Acad Sci. 2007, 104 (25): 10643-10648.PubMed CentralView ArticlePubMedGoogle Scholar
- Slesarev AI, Mezhevaya KV, Makarova KS, Polushin NN, Shcherbinina OV, Shakhova VV, Belova GI, Aravind L, Natale DA, Rogozin IB, Tatusov RL, Wolf YI, Stetter KO, Malykh AG, Koonin EV, Kozyavkin SA: The complete genome of hyperthermophile Methanopyrus kandleri AV19 and monophyly of archaeal methanogens. Proc Natl Acad Sci. 2002, 99 (7): 4644-4649.PubMed CentralView ArticlePubMedGoogle Scholar
- Guindon S, Lethiec F, Duroux P, Gascuel O: PHYML Online – a web server for fast maximum likelihood-based phylogenetic inference. Nucleic Acids Res. 2005, 33: W557-W559.PubMed CentralView ArticlePubMedGoogle Scholar
- Huelsenbeck J, Ronquist F: MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics. 2001, 17 (8): 754-755.View ArticlePubMedGoogle Scholar
- Felsenstein J: PHYLIP – Phylogeny Inference Package (Version 3.2). Cladistics. 1989, 5: 164-166.Google Scholar
- Wu S, Skolnick J, Zhang Y: Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biology. 2007, 5 (17):
- Zhang Y: I-TASSER server for protein 3D structure predictions. BMC Bioinformatics. 2008, 9 (40): 342-348.Google Scholar
- Murzin A, Brenner SE, Hubbard T, Chothia C: SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol. 1995, 247: 536-540.PubMedGoogle Scholar
- Samudrala R, Moult J: An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. J Mol Biol. 1998, 275: 895-916.View ArticlePubMedGoogle Scholar
- Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG: ClustalW and ClustalX version 2. Bioinformatics. 2007, 23 (21): 2947-2948.View ArticlePubMedGoogle Scholar
- Ma B, Elkayam T, Wolfson H, Nussinov R: Protein-protein interactions: Structurally conserved residues distinguish between binding sites and exposed protein surfaces. Proc Natl Acad Sci. 2003, 100 (10): 5772-5777.PubMed CentralView ArticlePubMedGoogle Scholar
- Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, Higgins DG, Thompson JD: Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res. 2003, 31 (13): 3497-3500.PubMed CentralView ArticlePubMedGoogle Scholar
- Guindon S, Gascuel O: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003, 52 (5): 696-704.View ArticlePubMedGoogle Scholar
- Whelan S, Goldman N: A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Mol Biol Evol. 2001, 18 (5): 691-699.View ArticlePubMedGoogle Scholar
- Saitou N, Nei M: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987, 4 (4): 406-425.PubMedGoogle Scholar
- Jones DT, Taylor WR, Thornton JM: The Rapid Generation of Mutation Data Matrices from Protein Sequences. Comput Applic Biosci. 1992, 8: 275-282.Google Scholar
- Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19 (12): 1572-1574.View ArticlePubMedGoogle Scholar
- Hung LH, Ngan SC, Liu T, Samudrala R: PROTINFO: new algorithms for enhanced protein structure predictions. Nucleic Acids Res. 2005, 33: W77-W80.PubMed CentralView ArticlePubMedGoogle Scholar
- Hung LH, Samudrala R: PROTINFO: secondary and teritary protein structure prediction. Nucleic Acids Res. 2003, 31: 3296-3299.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang K, Ram Samudrala: FSSA: A novel method for identifying functional signatures from structural alignments. Bioinformatics. 2005, 21: 2969-2977.View ArticlePubMedGoogle Scholar
- DeLano W: The PyMOL Molecular Graphics System. 2008, DeLano Scientific LLC, Palo Alto, CA, USAGoogle Scholar