Evidence map›Paper›PMID 28222763›Full record

ArticleBMC research notes2017

The discovery of novel LPMO families with a new Hidden Markov model.

Gerben P Voshol, Erik Vijgenboom, Peter J Punt

Open access · goldAbstract read
In one paragraph

Article in BMC research notes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.6field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Efficient Secretory Production of Lytic Polysaccharide MonooxygenaseInternational journal of molecular sciences · 2023
    Article
  5. Article
  6. Review
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  8. Article
  9. Article
  10. Review
  11. Article
  12. Physiological and Molecular Understanding of Bacterial Polysaccharide Monooxygenases.Microbiology and molecular biology reviews : MMBR · 2017
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Gerben P VosholMolecular Microbiology and Health, Institute of Biology Leiden, Leiden University, Leiden, The Netherlands.
Erik VijgenboomMolecular Microbiology and Health, Institute of Biology Leiden, Leiden University, Leiden, The Netherlands.
Peter J PuntMolecular Microbiology and Health, Institute of Biology Leiden, Leiden University, Leiden, The Netherlands. peter.punt@ddna-biotech.com.
Leiden University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRenewable 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.

resultsWe 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.

conclusionsThe 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.

Indexed as

Data MiningGenome, FungalMarkov ChainsAmino Acid MotifsAnimalsAscomycotaBasidiomycotaBiodegradation, EnvironmentalBivalviaCelluloseChitinDrosophila melanogasterDrosophila ProteinsFungal ProteinsHydrolysisMixed Function OxygenasesCelluloseChitinDrosophila ProteinsFungal ProteinsMixed Function OxygenasesStarchFungiGenome miningHidden Markov model, HMMLPMOLytic polysaccharide mono-oxygenasesPectinβ-Glucan

Identifiers

PMID28222763
PMCPMC5320794
OpenAlexW2589285490

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.