Dataset Information

Xylanolytic Extremozymes Retrieved From Environmental Metagenomes: Characteristics, Genetic Engineering, and Applications.

ABSTRACT: Xylanolytic enzymes have extensive applications in paper, food, and feed, pharmaceutical, and biofuel industries. These industries demand xylanases that are functional under extreme conditions, such as high temperature, acidic/alkaline pH, and others, which are prevailing in bioprocessing industries. Despite the availability of several xylan-hydrolyzing enzymes from cultured microbes, there is a huge gap between what is available and what industries require. DNA manipulations as well as protein-engineering techniques are also not quite satisfactory in generating xylan-hydrolyzing extremozymes. With a compound annual growth rate of 6.6% of xylan-hydrolyzing enzymes in the global market, there is a need for xylanolytic extremozymes. Therefore, metagenomic approaches have been employed to uncover hidden xylanolytic genes that were earlier inaccessible in culture-dependent approaches. Appreciable success has been achieved in retrieving several unusual xylanolytic enzymes with novel and desirable characteristics from different extreme environments using functional and sequence-based metagenomic approaches. Moreover, the Carbohydrate Active Enzymes database includes approximately 400 GH-10 and GH-11 unclassified xylanases. This review discusses sources, characteristics, and applications of xylanolytic enzymes obtained through metagenomic approaches and their amelioration by genetic engineering techniques.

SUBMITTER: Verma D

PROVIDER: S-EPMC7527525 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Xylanolytic Extremozymes Retrieved From Environmental Metagenomes: Characteristics, Genetic Engineering, and Applications.

Verma Digvijay D Satyanarayana Tulasi T

Frontiers in microbiology 20200917

Xylanolytic enzymes have extensive applications in paper, food, and feed, pharmaceutical, and biofuel industries. These industries demand xylanases that are functional under extreme conditions, such as high temperature, acidic/alkaline pH, and others, which are prevailing in bioprocessing industries. Despite the availability of several xylan-hydrolyzing enzymes from cultured microbes, there is a huge gap between what is available and what industries require. DNA manipulations as well as protein- ...[more]

PMID: 33042057

Similar Datasets

Project description:Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed into a routine challenge in the field of Environmental Engineering. Among the main issues identified with parameter estimation, the model-data calibration approach is a crucial, yet an often overlooked and difficult optimization problem. Here, a novel and rigorous global, multi objective, and fully Bayesian optimization approach that overcomes challenges associated with multi-variate, sparse and noisy data, as well as highly non-linear model structures commonly encountered in Environmental Engineering practice is presented. This optimization approach allows an improved definition and targeting of the compromise solution space for all multivariate problems, allowing efficient convergence, and a Bayesian component to thoroughly explore parameter and model prediction uncertainty. This global optimization approach outperformed, in terms of parameter accuracy and precision, standard, local non-linear regression routines and overcomes issues associated with premature convergence and addresses overfitting of different variables in the calibration process. •A sequential single, multi-objective, and Bayesian optimization workflow was developed to accurately and reliably estimate unstructured kinetic model parameters.•The global, single objective approach defines the global optimum (the best compromise solution) and "extreme" parameter solutions for each variable, while the global, multi-objective approach confirms the "best" compromise solution space for the Bayesian search to target and convergence is assessed using the single objective results.•The Approximate Bayesian Computational approach fully explores parameter and model prediction uncertainty targeting the compromise solution space previously identified.

Dataset Information

Xylanolytic Extremozymes Retrieved From Environmental Metagenomes: Characteristics, Genetic Engineering, and Applications.

Publications

Xylanolytic Extremozymes Retrieved From Environmental Metagenomes: Characteristics, Genetic Engineering, and Applications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets