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The discovery of novel LPMO families with a new Hidden Markov model
BACKGROUND: Renewable biopolymers, such as cellulose, starch and chitin are highly resistance to enzymatic degradation. Therefore, there is a need to upgrade current degradation processes by including novel enzymes. Lytic polysaccharide mono-oxygenases (LPMOs) can disrupt recalcitrant biopolymers, t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320794/ https://www.ncbi.nlm.nih.gov/pubmed/28222763 http://dx.doi.org/10.1186/s13104-017-2429-8 |
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author | Voshol, Gerben P. Vijgenboom, Erik Punt, Peter J. |
author_facet | Voshol, Gerben P. Vijgenboom, Erik Punt, Peter J. |
author_sort | Voshol, Gerben P. |
collection | PubMed |
description | BACKGROUND: Renewable biopolymers, such as cellulose, starch and chitin are highly resistance to enzymatic degradation. Therefore, there is a need to upgrade current degradation processes by including novel enzymes. Lytic polysaccharide mono-oxygenases (LPMOs) can disrupt recalcitrant biopolymers, thereby enhancing hydrolysis by conventional enzymes. However, novel LPMO families are difficult to identify using existing methods. Therefore, we developed a novel profile Hidden Markov model (HMM) and used it to mine genomes of ascomycetous fungi for novel LPMOs. RESULTS: We constructed a structural alignment and verified that the alignment was correct. In the alignment we identified several known conserved features, such as the histidine brace and the N/Q/E-X-F/Y motif and previously unidentified conserved proline and glycine residues. These residues are distal from the active site, suggesting a role in structure rather than activity. The multiple protein alignment was subsequently used to build a profile Hidden Markov model. This model was initially tested on manually curated datasets and proved to be both sensitive (no false negatives) and specific (no false positives). In some of the genomes analyzed we identified a yet unknown LPMO family. This new family is mostly confined to the phyla of Ascomycota and Basidiomycota and the class of Oomycota. Genomic clustering indicated that at least some members might be involved in the degradation of β-glucans, while transcriptomic data suggested that others are possibly involved in the degradation of pectin. CONCLUSIONS: The newly developed profile hidden Markov Model was successfully used to mine fungal genomes for a novel family of LPMOs. However, the model is not limited to bacterial and fungal genomes. This is illustrated by the fact that the model was also able to identify another new LPMO family in Drosophila melanogaster. Furthermore, the Hidden Markov model was used to verify the more distant blast hits from the new fungal family of LPMOs, which belong to the Bivalves, Stony corals and Sea anemones. So this Hidden Markov model (Additional file 3) will help the broader scientific community in identifying other yet unknown LPMOs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-017-2429-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5320794 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53207942017-02-24 The discovery of novel LPMO families with a new Hidden Markov model Voshol, Gerben P. Vijgenboom, Erik Punt, Peter J. BMC Res Notes Research Article BACKGROUND: Renewable biopolymers, such as cellulose, starch and chitin are highly resistance to enzymatic degradation. Therefore, there is a need to upgrade current degradation processes by including novel enzymes. Lytic polysaccharide mono-oxygenases (LPMOs) can disrupt recalcitrant biopolymers, thereby enhancing hydrolysis by conventional enzymes. However, novel LPMO families are difficult to identify using existing methods. Therefore, we developed a novel profile Hidden Markov model (HMM) and used it to mine genomes of ascomycetous fungi for novel LPMOs. RESULTS: We constructed a structural alignment and verified that the alignment was correct. In the alignment we identified several known conserved features, such as the histidine brace and the N/Q/E-X-F/Y motif and previously unidentified conserved proline and glycine residues. These residues are distal from the active site, suggesting a role in structure rather than activity. The multiple protein alignment was subsequently used to build a profile Hidden Markov model. This model was initially tested on manually curated datasets and proved to be both sensitive (no false negatives) and specific (no false positives). In some of the genomes analyzed we identified a yet unknown LPMO family. This new family is mostly confined to the phyla of Ascomycota and Basidiomycota and the class of Oomycota. Genomic clustering indicated that at least some members might be involved in the degradation of β-glucans, while transcriptomic data suggested that others are possibly involved in the degradation of pectin. CONCLUSIONS: The newly developed profile hidden Markov Model was successfully used to mine fungal genomes for a novel family of LPMOs. However, the model is not limited to bacterial and fungal genomes. This is illustrated by the fact that the model was also able to identify another new LPMO family in Drosophila melanogaster. Furthermore, the Hidden Markov model was used to verify the more distant blast hits from the new fungal family of LPMOs, which belong to the Bivalves, Stony corals and Sea anemones. So this Hidden Markov model (Additional file 3) will help the broader scientific community in identifying other yet unknown LPMOs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-017-2429-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-21 /pmc/articles/PMC5320794/ /pubmed/28222763 http://dx.doi.org/10.1186/s13104-017-2429-8 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Article Voshol, Gerben P. Vijgenboom, Erik Punt, Peter J. The discovery of novel LPMO families with a new Hidden Markov model |
title | The discovery of novel LPMO families with a new Hidden Markov model |
title_full | The discovery of novel LPMO families with a new Hidden Markov model |
title_fullStr | The discovery of novel LPMO families with a new Hidden Markov model |
title_full_unstemmed | The discovery of novel LPMO families with a new Hidden Markov model |
title_short | The discovery of novel LPMO families with a new Hidden Markov model |
title_sort | discovery of novel lpmo families with a new hidden markov model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320794/ https://www.ncbi.nlm.nih.gov/pubmed/28222763 http://dx.doi.org/10.1186/s13104-017-2429-8 |
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