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A New Functional Classification of Glucuronoyl Esterases by Peptide Pattern Recognition.

ABSTRACT: Glucuronoyl esterases are a novel type of enzymes believed to catalyze the hydrolysis of ester linkages between lignin and glucuronoxylan in lignocellulosic biomass, linkages known as lignin carbohydrate complexes. These complexes contribute to the recalcitrance of lignocellulose. Glucuronoyl esterases are a part of the microbial machinery for lignocellulose degradation and coupling their role to the occurrence of lignin carbohydrate complexes in biomass is a desired research goal. Glucuronoyl esterases have been assigned to CAZymes family 15 of carbohydrate esterases, but only few examples of characterized enzymes exist and the exact activity is still uncertain. Here peptide pattern recognition is used as a bioinformatic tool to identify and group new CE15 proteins that are likely to have glucuronoyl esterase activity. 1024 CE15-like sequences were drawn from GenBank and grouped into 24 groups. Phylogenetic analysis of these groups made it possible to pinpoint groups of putative fungal and bacterial glucuronoyl esterases and their sequence variation. Moreover, a number of groups included previously undescribed CE15-like sequences that are distinct from the glucuronoyl esterases and may possibly have different esterase activity. Hence, the CE15 family is likely to comprise other enzyme functions than glucuronoyl esterase alone. Gene annotation in a variety of fungal and bacterial microorganisms showed that coprophilic fungi are rich and diverse sources of CE15 proteins. Combined with the lifestyle and habitat of coprophilic fungi, they are predicted to be excellent candidates for finding new glucuronoyl esterase genes.

SUBMITTER: Agger JW

PROVIDER: S-EPMC5329029 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A New Functional Classification of Glucuronoyl Esterases by Peptide Pattern Recognition.

Agger Jane W JW Busk Peter K PK Pilgaard Bo B Meyer Anne S AS Lange Lene L

Frontiers in microbiology 20170228

Glucuronoyl esterases are a novel type of enzymes believed to catalyze the hydrolysis of ester linkages between lignin and glucuronoxylan in lignocellulosic biomass, linkages known as lignin carbohydrate complexes. These complexes contribute to the recalcitrance of lignocellulose. Glucuronoyl esterases are a part of the microbial machinery for lignocellulose degradation and coupling their role to the occurrence of lignin carbohydrate complexes in biomass is a desired research goal. Glucuronoyl e ...[more]

PMID: 28293230

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Project description:Background:Lignocellulose is highly recalcitrant to enzymatic deconstruction, where the recalcitrance primarily results from chemical linkages between lignin and carbohydrates. Glucuronoyl esterases (GEs) from carbohydrate esterase family 15 (CE15) have been suggested to play key roles in reducing lignocellulose recalcitrance by cleaving covalent ester bonds found between lignin and glucuronoxylan. However, only a limited number of GEs have been biochemically characterized and structurally determined to date, limiting our understanding of these enzymes and their potential exploration. Results:Ten CE15 enzymes from three bacterial species, sharing as little as 20% sequence identity, were characterized on a range of model substrates; two protein structures were solved, and insights into their regulation and biological roles were gained through gene expression analysis and enzymatic assays on complex biomass. Several enzymes with higher catalytic efficiencies on a wider range of model substrates than previously characterized fungal GEs were identified. Similarities and differences regarding substrate specificity between the investigated GEs were observed and putatively linked to their positioning in the CE15 phylogenetic tree. The bacterial GEs were able to utilize substrates lacking 4-OH methyl substitutions, known to be important for fungal enzymes. In addition, certain bacterial GEs were able to efficiently cleave esters of galacturonate, a functionality not previously described within the family. The two solved structures revealed similar overall folds to known structures, but also indicated active site regions allowing for more promiscuous substrate specificities. The gene expression analysis demonstrated that bacterial GE-encoding genes were differentially expressed as response to different carbon sources. Further, improved enzymatic saccharification of milled corn cob by a commercial lignocellulolytic enzyme cocktail when supplemented with GEs showcased their synergistic potential with other enzyme types on native biomass. Conclusions:Bacterial GEs exhibit much larger diversity than fungal counterparts. In this study, we significantly expanded the existing knowledge on CE15 with the in-depth characterization of ten bacterial GEs broadly spanning the phylogenetic tree, and also presented two novel enzyme structures. Variations in transcriptional responses of CE15-encoding genes under different growth conditions suggest nonredundant functions for enzymes found in species with multiple CE15 genes and further illuminate the importance of GEs in native lignin-carbohydrate disassembly.

Project description:Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject's intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition.CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback-Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec.NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers' sensitivity to such redundancy.This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance.

Project description:Background and aimsA pollen grain contains a number of esterases, many of which are released upon contact with the stigma surface. However, the identity and function of most of these esterases remain unknown. In this work, esterases from olive pollen during its germination were identifided and functionally characterized.MethodsThe esterolytic capacity of olive (Olea europaea) pollen was examined using in vitro and in-gel enzymatic assays with different enzyme substrates. The functional analysis of pollen esterases was achieved by inhibition assays by using specific inhibitors. The cellular localization of esterase activities was performed using histochemical methods.Key resultsOlive pollen showed high levels of non-specific esterase activity, which remained steady after hydration and germination. Up to 20 esterolytic bands were identified on polyacrylamide gels. All the inhibitors decreased pollen germinability, but only diisopropyl fluorophosphate (DIFP) hampered pollen tube growth. Non-specific esterase activity is localized on the surface of oil bodies (OBs) and small vesicles, in the pollen intine and in the callose layer of the pollen tube wall. Acetylcholinesterase (AChE) activity was mostly observed in the apertures, exine and pollen coat, and attached to the pollen tube wall surface and to small cytoplasmic vesicles.ConclusionsIn this work, for the first time a systematic functional characterization of esterase enzymes in pollen from a plant species with wet stigma has been carried out. Olive pollen esterases belong to four different functional groups: carboxylesterases, acetylesterases, AChEs and lipases. The cellular localization of esterase activity indicates that the intine is a putative storage site for esterolytic enzymes in olive pollen. Based on inhibition assays and cellular localization of enzymatic activities, it can be concluded that these enzymes are likely to be involved in pollen germination, and pollen tube growth and penetration of the stigma.

Dataset Information

A New Functional Classification of Glucuronoyl Esterases by Peptide Pattern Recognition.

Publications

A New Functional Classification of Glucuronoyl Esterases by Peptide Pattern Recognition.

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