Distribution and prediction of catalytic domains in 2-oxoglutarate dependent dioxygenases

ProTG4: A Web Server to Approximate the Sequence of a Generic Protein From an in Silico Library of Translatable G-Quadruplex (TG4)-Mapped Peptides

An RNA G-quadruplex in the protein coding segment of mRNA is translatable (TG4) and may potentially impact protein translation. This can be consequent to staggered ribosomal synthesis and/or result in an increased frequency of missense translational events. A mathematical model of the peptides that encompass the substituted amino acids, ie, the TG4-mapped peptidome, has been previously studied. However, the significance and relevance to disease biology of this model remains to be established. ProTG4 computes a confidence-of-sequence-identity (γ)-score, which is the average weighted length of every matched TG4-mapped peptide in a generic protein sequence. The weighted length is the product of the length of the peptide and the probability of its non-random occurrence in a library of randomly generated sequences of equivalent lengths. This is then averaged over the entire length of the protein sequence. ProTG4 is simple to operate, has clear instructions, and is accompanied by a set of ready-to-use examples. The rationale of the study, algorithms deployed, and the computational pipeline deployed are also part of the web page. Analyses by ProTG4 of taxonomically diverse protein sequences suggest that there is significant homology to TG4-mapped peptides. These findings, especially in potentially infectious and infesting agents, offer plausible explanations into the aetiology and pathogenesis of certain proteopathies. ProTG4 can also provide a quantitative measure to identify and annotate the canonical form of a generic protein sequence from its known isoforms. The article presents several case studies and discusses the relevance of ProTG4-assisted peptide analysis in gaining insights into various mechanisms of disease biology (mistranslation, alternate splicing, amino acid substitutions).

Fe(2)OG: an integrated HMM profile-based web server to predict and analyze putative non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenase function in protein sequences

Objective

Non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenases (i2OGdd), are a taxonomically and functionally diverse group of enzymes. The active site comprises ferrous iron in a hexa-coordinated distorted octahedron with the apoenzyme, 2-oxoglutarate and a displaceable water molecule. Current information on novel i2OGdd members is sparse and relies on computationally-derived annotation schema. The dissimilar amino acid composition and variable active site geometry thereof, results in differing reaction chemistries amongst i2OGdd members. An additional need of researchers is a curated list of sequences with putative i2OGdd function which can be probed further for empirical data.

Results

This work reports the implementation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe\left(2\right)OG$$\end{document}Fe2OG, a web server with dual functionality and an extension of previous work on i2OGdd enzymes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(Fe\left(2\right)OG\equiv \{H2OGpred,DB2OG\}\right)$$\end{document}Fe2OG≡{H2OGpred,DB2OG}. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe\left(2\right)OG$$\end{document}Fe2OG, in this form is completely revised, updated (URL, scripts, repository) and will strengthen the knowledge base of investigators on i2OGdd biochemistry and function. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe\left(2\right)OG$$\end{document}Fe2OG, utilizes the superior predictive propensity of HMM-profiles of laboratory validated i2OGdd members to predict probable active site geometries in user-defined protein sequences. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe\left(2\right)OG$$\end{document}Fe2OG, also provides researchers with a pre-compiled list of analyzed and searchable i2OGdd-like sequences, many of which may be clinically relevant. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe(2)OG$$\end{document}Fe(2)OG, is freely available (http://204.152.217.16/Fe2OG.html) and supersedes all previous versions, i.e., H2OGpred, DB2OG.

Iron-Requiring Enzymes in the Spotlight of Oxygen

Iron (Fe) is a cofactor required for a variety of essential redox reactions in plant metabolism. Thus, plants have developed a complex network of interacting pathways to withstand Fe deficiency, including metabolic reprogramming. This opinion aims at revisiting such reprogramming by focusing on: (i) the functional relationships of Fe-requiring enzymes (FeREs) with respect to oxygen; and (ii) the progression of FeREs engagement, occurring under Fe deficiency stress. In particular, we considered such progression of FeREs engagement as strain responses of increasing severity during the stress phases of alarm, resistance, and exhaustion. This approach can contribute to reconcile the variety of experimental results obtained so far from different plant species and/or different Fe supplies.

Mathematical Basis of Predicting Dominant Function in Protein Sequences by a Generic HMM-ANN Algorithm

The accurate annotation of an unknown protein sequence depends on extant data of template sequences. This could be empirical or sets of reference sequences, and provides an exhaustive pool of probable functions. Individual methods of predicting dominant function possess shortcomings such as varying degrees of inter-sequence redundancy, arbitrary domain inclusion thresholds, heterogeneous parameterization protocols, and ill-conditioned input channels. Here, I present a rigorous theoretical derivation of various steps of a generic algorithm that integrates and utilizes several statistical methods to predict the dominant function in unknown protein sequences. The accompanying mathematical proofs, interval definitions, analysis, and numerical computations presented are meant to offer insights not only into the specificity and accuracy of predictions, but also provide details of the operatic mechanisms involved in the integration and its ensuing rigor. The algorithm uses numerically modified raw hidden markov model scores of well defined sets of training sequences and clusters them on the basis of known function. The results are then fed into an artificial neural network, the predictions of which can be refined using the available data. This pipeline is trained recursively and can be used to discern the dominant principal function, and thereby, annotate an unknown protein sequence. Whilst, the approach is complex, the specificity of the final predictions can benefit laboratory workers design their experiments with greater confidence.

Origin, evolution, and divergence of plant class C GH9 endoglucanases

BACKGROUND

Glycoside 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.

RESULTS

Since, 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.

CONCLUSION

Our 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.

Mathematical basis of improved protein subfamily classification by a HMM-based sequence filter

Informative phylogenetic analysis is dependent on the presence of curated and annotated sequences. This may be complemented by the simultaneous availability of empirical data pertaining to their in vivo function. Confounding sequences, with their similarity to more than one functional cluster, can therefore, render any categorization ambiguous, subjective, and imprecise. Here, I analyze and discuss the development of a mathematical expression that can characterize a potential confounding protein sequence. Specifically, statistical descriptors of combinatorially arranged profile HMM scores are computed and evaluated. The resultant data is then incorporated into an index of sequence suitability. The sequence may then be recommended as either suitable for inclusion or be excluded all together. The index is independent of experimental data and, can, be computed from the primary structure of the protein sequence. This can be utilized to trim previously grouped sequences and can either finalize the composition of training set or reduce the search space of sequences to be tested.

In silico Identification and Taxonomic Distribution of Plant Class C GH9 Endoglucanases

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.

Co-operative intermolecular kinetics of 2-oxoglutarate dependent dioxygenases may be essential for system-level regulation of plant cell physiology

Can the stimulus-driven synergistic association of 2-oxoglutarate dependent dioxygenases be influenced by the kinetic parameters of binding and catalysis?In this manuscript, I posit that these indices are necessary and specific for a particular stimulus, and are key determinants of a dynamic clustering that may function to mitigate the effects of this trigger. The protein(s)/sequence(s) that comprise this group are representative of all major kingdoms of life, and catalyze a generic hydroxylation, which is, in most cases accompanied by a specialized conversion of the substrate molecule. Iron is an essential co-factor for this transformation and the response to waning levels is systemic, and mandates the simultaneous participation of molecular sensors, transporters, and signal transducers. Here, I present a proof-of-concept model, that an evolving molecular network of 2OG-dependent enzymes can maintain iron homeostasis in the cytosol of root hair cells of members of the family Gramineae by actuating a non-reductive compensatory chelation by the phytosiderophores. Regression models of empirically available kinetic data (iron and alpha-ketoglutarate) were formulated, analyzed, and compared. The results, when viewed in context of the superfamily responding as a unit, suggest that members can indeed, work together to accomplish system-level function. This is achieved by the establishment of transient metabolic conduits, wherein the flux is dictated by kinetic compatibility of the participating enzymes. The approach adopted, i.e., predictive mathematical modeling, is integral to the hypothesis-driven acquisition of experimental data points and, in association with suitable visualization aids may be utilized for exploring complex plant biochemical systems.

Unity in diversity, a systems approach to regulating plant cell physiology by 2-oxoglutarate-dependent dioxygenases

Could a disjoint group of enzymes synchronize their activities and execute a complex multi-step, measurable, and reproducible response? Here, I surmise that the alpha-ketoglutarate dependent superfamily of non-haem iron (II) dioxygenases could influence cell physiology as a cohesive unit, and that the broad spectra of substrates transformed is an absolute necessity to this portrayal. This eclectic group comprises members from all major taxa, and participates in pesticide breakdown, hypoxia signaling, and osmotic stress neutralization. The oxidative decarboxylation of 2-oxoglutarate to succinate is coupled with a concomitant substrate hydroxylation and, in most cases, is followed by an additional specialized conversion. The domain profile of a protein sequence was used as an index of miscellaneous reaction chemistry and interpreted alongside existent kinetic data in a linear model of integrated function. Statistical parameters were inferred by the creation of a novel, empirically motivated flat-file database of over 3800 sequences (DB2OG) with putative 2-oxoglutarate dependent activity. The collated information was categorized on the basis of existing annotation schema. The data suggests that 2OG-dependent enzymes incorporate several desirable features of a systems level player. DB2OG, is free, accessible without a login to all users, and available at the following URL (http://comp-biol.theacms.in/DB2OG.html).

Crystal structure of the ectoine hydroxylase, a snapshot of the active site

Ectoine and its derivative 5-hydroxyectoine are compatible solutes that are widely synthesized by bacteria to cope physiologically with osmotic stress. They also serve as chemical chaperones and maintain the functionality of macromolecules. 5-Hydroxyectoine is produced from ectoine through a stereo-specific hydroxylation, an enzymatic reaction catalyzed by the ectoine hydroxylase (EctD). The EctD protein is a member of the non-heme-containing iron(II) and 2-oxoglutarate-dependent dioxygenase superfamily and is evolutionarily well conserved. We studied the ectoine hydroxylase from the cold-adapted marine ultra-microbacterium Sphingopyxis alaskensis (Sa) and found that the purified SaEctD protein is a homodimer in solution. We determined the SaEctD crystal structure in its apo-form, complexed with the iron catalyst, and in a form that contained iron, the co-substrate 2-oxoglutarate, and the reaction product of EctD, 5-hydroxyectoine. The iron and 2-oxoglutarate ligands are bound within the EctD active site in a fashion similar to that found in other members of the dioxygenase superfamily. 5-Hydroxyectoine, however, is coordinated by EctD in manner different from that found in high affinity solute receptor proteins operating in conjunction with microbial import systems for ectoines. Our crystallographic analysis provides a detailed view into the active site of the ectoine hydroxylase and exposes an intricate network of interactions between the enzyme and its ligands that collectively ensure the hydroxylation of the ectoine substrate in a position- and stereo-specific manner.