Characterization of a novel AA3_1 xylooligosaccharide dehydrogenase from <i>Thermothelomyces myriococcoides</i> CBS 398.93

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Abstract

Background

The Carbohydrate-Active enZymes (CAZy) auxiliary activity family 3 (AA3) comprises flavin adenine dinucleotide-dependent (FAD) oxidoreductases from the glucose–methanol–choline (GMC) family, which play auxiliary roles in lignocellulose conversion. The AA3 subfamily 1 predominantly consists of cellobiose dehydrogenases (CDHs) that typically comprise a dehydrogenase domain, a cytochrome domain, and a carbohydrate-binding module from family 1 (CBM1).

Results

In this work, an AA3_1 gene from T. myriococcoides CBS 398.93 encoding only a GMC dehydrogenase domain was expressed in Aspergillus niger. Like previously characterized CDHs, this enzyme ( TmXdhA) predominantly accepts linear saccharides with β-(1 → 4) linkage and targets the hydroxyl on the reducing anomeric carbon. TmXdhA was distinguished, however, by its preferential activity towards xylooligosaccharides over cellooligosaccharides. Amino acid sequence analysis showed that TmXdhA possesses a glutamine at the substrate-binding site rather than a threonine or serine that occupies this position in previously characterized CDHs, and structural models suggest the glutamine in TmXdhA could facilitate binding to pentose sugars.

Conclusions

The biochemical analysis of TmXdhA revealed a catalytic preference for xylooligosaccharide substrates. The modeled structure of TmXdhA provides a reference for the screening of oxidoreductases targeting xylooligosaccharides. We anticipate TmXdhA to be a good candidate for the conversion of xylooligosaccharides to added-value chemicals by its exceptional catalytic ability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02231-w.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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MUSCLE: multiple sequence alignment with high accuracy and high throughput.

R. C. Edgar (2004)

We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

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The carbohydrate-active enzyme database: functions and literature

Elodie Drula, Marie-Line Garron, Suzan Dogan … (2021)

Thirty years have elapsed since the emergence of the classification of carbohydrate-active enzymes in sequence-based families that became the CAZy database over 20 years ago, freely available for browsing and download at www.cazy.org . In the era of large scale sequencing and high-throughput Biology, it is important to examine the position of this specialist database that is deeply rooted in human curation. The three primary tasks of the CAZy curators are (i) to maintain and update the family classification of this class of enzymes, (ii) to classify sequences newly released by GenBank and the Protein Data Bank and (iii) to capture and present functional information for each family. The CAZy website is updated once a month. Here we briefly summarize the increase in novel families and the annotations conducted during the last 8 years. We present several important changes that facilitate taxonomic navigation, and allow to download the entirety of the annotations. Most importantly we highlight the considerable amount of work that accompanies the analysis and report of biochemical data from the literature.

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Author and article information

Contributors

Hongbo Zhao: hongbo.zhao@helsinki.fi

Journal

Journal ID (nlm-ta): Biotechnol Biofuels Bioprod

Journal ID (iso-abbrev): Biotechnol Biofuels Bioprod

Title: Biotechnology for Biofuels and Bioproducts

Publisher: BioMed Central (London )

ISSN (Electronic): 2731-3654

Publication date (Electronic): 7 December 2022

Publication date PMC-release: 7 December 2022

Publication date Collection: 2022

Volume: 15

Electronic Location Identifier: 135

Affiliations

[1 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Food and Nutrition, , University of Helsinki, ; Helsinki, Finland

[2 ]GRID grid.5373.2, ISNI 0000000108389418, Department of Bioproducts and Biosystems, , Aalto University, ; Espoo, Finland

[3 ]GRID grid.410319.e, ISNI 0000 0004 1936 8630, Centre for Structural and Functional Genomics, , Concordia University, ; 7141 Sherbrooke Street West, Montreal, QC H4B 1R6 Canada

[4 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Chemical Engineering and Applied Chemistry, , University of Toronto, ; Toronto, ON Canada

Article

Publisher ID: 2231

DOI: 10.1186/s13068-022-02231-w

PMC ID: 9730589

PubMed ID: 36476312

SO-VID: 848bb53f-d34e-4830-9516-43915f4d2c0e

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 19 September 2022

Date accepted : 17 November 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002341, Academy of Finland;

Award ID: 308997

Funded by: FundRef http://dx.doi.org/10.13039/501100003125, Suomen Kulttuurirahasto;

Funded by: FundRef http://dx.doi.org/10.13039/501100009708, Novo Nordisk Fonden;

Custom metadata

Keywords: xylooligosaccharide dehydrogenase,cellobiose dehydrogenase,thermothelomyces myriococcoides,cazy aa3,aa3_1

Data availability:

Keywords: xylooligosaccharide dehydrogenase, cellobiose dehydrogenase, thermothelomyces myriococcoides, cazy aa3, aa3_1

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Characterization of a novel AA3_1 xylooligosaccharide dehydrogenase from Thermothelomyces myriococcoides CBS 398.93

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Abstract

Background

Results

Conclusions

Supplementary Information

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Coronaviruses, COVID-19 and SARS-CoV-2

Most cited references 51

Highly accurate protein structure prediction with AlphaFold

MUSCLE: multiple sequence alignment with high accuracy and high throughput.

The carbohydrate-active enzyme database: functions and literature

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