BackgroundThe 2-oxoglutarate dependent superfamily is a diverse group of non-haem dioxygenases, and is present in prokaryotes, eukaryotes, and archaea. The enzymes differ in substrate preference and reaction chemistry, a factor that precludes their classification by homology studies and electronic annotation schemes alone. In this work, I propose and explore the rationale of using substrates to classify structurally similar alpha-ketoglutarate dependent enzymes.FindingsDifferential catalysis in phylogenetic clades of 2-OG dependent enzymes, is determined by the interactions of a subset of active-site amino acids. Identifying these with existing computational methods is challenging and not feasible for all proteins. A clustering protocol based on validated mechanisms of catalysis of known molecules, in tandem with group specific hidden markov model profiles is able to differentiate and sequester these enzymes. Access to this repository is by a web server that compares user defined unknown sequences to these pre-defined profiles and outputs a list of predicted catalytic domains. The server is free and is accessible at the following URL ( http://comp-biol.theacms.in/H2OGpred.html).ConclusionsThe proposed stratification is a novel attempt at classifying and predicting 2-oxoglutarate dependent function. In addition, the server will provide researchers with a tool to compare their data to a comprehensive list of HMM profiles of catalytic domains. This work, will aid efforts by investigators to screen and characterize putative 2-OG dependent sequences. The profile database will be updated at regular intervals.

Metabolism is a combination of enzymatic- and non-enzymatic interactions of several macro- and small-molecules and occurs via biochemical networks. Here, we present a mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constant for every reaction of a constrained biochemical network. A reaction outcome is forward, reverse or equivalent, and is computed directly from the null space generated subspace of a stoichiometric number matrix of the reactants/products and reactions of the modelled biochemical network. This is accomplished by iteratively and recursively populating a reaction-specific sequence vector with the combinatorial sums of all unique and non-trivial vectors that span each null space generated subspace. After a finite number of iterations the terms of this reaction-specific sequence vector will diverge and belong to the open intervals \(\left(1,\infty \right)\) and/or \(\left(-\infty ,-1\right)\). Statistical and mathematical descriptors (mean, standard deviation, bounds, linear maps, vector norms, tests of convergence) are used to select and bin terms from the reaction-specific sequence vector into distinct subsets for all three predicted outcomes of a reaction. The terms of each outcome-specific subset are summed, mapped to the open interval \(\left(0,\infty \right)\) and used to populate a reaction-specific outcome vector. The p1-norm of this vector is numerically equal to the probable disassociation constant for that reaction. These steps are continued until every reaction of a modelled network is unambiguously annotated. Numerical studies to ascertain the relevance and suitability of the probable dissociation constant as a parameter are accomplished by characterizing a constrained biochemical network of aerobic glycolysis. This is implemented by the R-package “ReDirection” which is freely available and accessible at the comprehensive R archive network (CRAN) with the URL (https://cran.r-project.org/package=ReDirection).

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BackgroundGlycoside hydrolases of the GH9 family encode cellulases that predominantly function as endoglucanases and have wide applications in the food, paper, pharmaceutical, and biofuel industries. The partitioning of plant GH9 endoglucanases, into classes A, B, and C, is based on the differential presence of transmembrane, signal peptide, and the carbohydrate binding module (CBM49). There is considerable debate on the distribution and the functions of these enzymes which may vary in different organisms. In light of these findings we examined the origin, emergence, and subsequent divergence of plant GH9 endoglucanases, with an emphasis on elucidating the role of CBM49 in the digestion of crystalline cellulose by class C members.ResultsSince, the digestion of crystalline cellulose mandates the presence of a well-defined set of aromatic and polar amino acids and/or an attributable domain that can mediate this conversion, we hypothesize a vertical mode of transfer of genes that could favour the emergence of class C like GH9 endoglucanase activity in land plants from potentially ancestral non plant taxa. We demonstrated the concomitant occurrence of a GH9 domain with CBM49 and other homologous carbohydrate binding modules, in putative endoglucanase sequences from several non-plant taxa. In the absence of comparable full length CBMs, we have characterized several low strength patterns that could approximate the CBM49, thereby, extending support for digestion of crystalline cellulose to other segments of the protein. We also provide data suggestive of the ancestral role of putative class C GH9 endoglucanases in land plants, which includes detailed phylogenetics and the presence and subsequent loss of CBM49, transmembrane, and signal peptide regions in certain populations of early land plants. These findings suggest that classes A and B of modern vascular land plants may have emerged by diverging directly from CBM49 encompassing putative class C enzymes.ConclusionOur detailed phylogenetic and bioinformatics analysis of putative GH9 endoglucanase sequences across major taxa suggests that plant class C enzymes, despite their recent discovery, could function as the last common ancestor of classes A and B. Additionally, research into their ability to digest or inter-convert crystalline and amorphous forms of cellulose could make them lucrative candidates for engineering biofuel feedstock.Electronic supplementary materialThe online version of this article (10.1186/s12862-018-1185-2) contains supplementary material, which is available to authorized users.

The glycoside hydrolase 9 superfamily, mainly comprising the endoglucanases, is represented in all three domains of life. The current division of GH9 enzymes, into three subclasses, namely A, B, and C, is centered on parameters derived from sequence information alone. However, this classification is ambiguous, and is limited by the paralogous ancestry of classes B and C endoglucanases, and paucity of biochemical and structural data. Here, we extend this classification schema to putative GH9 endoglucanases present in green plants, with an emphasis on identifying novel members of the class C subset. These enzymes cleave the β(1 → 4) linkage between non-terminal adjacent D-glucopyranose residues, in both, amorphous and crystalline regions of cellulose. We utilized non redundant plant GH9 enzymes with characterized molecular data, as the training set to construct Hidden Markov Models (HMMs) and train an Artificial Neural Network (ANN). The parameters that were used for predicting dominant enzyme function, were derived from this training set, and subsequently refined on 147 sequences with available expression data. Our knowledge-based approach, can ascribe differential endoglucanase activity (A, B, or C) to a query sequence with high confidence, and was used to construct a local repository of class C GH9 endoglucanases (GH9C = 241) from 32 sequenced green plants.