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

ABSTRACT: 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.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 ?-glucans, while transcriptomic data suggested that others are possibly involved in the degradation of pectin.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.

SUBMITTER: Voshol GP

PROVIDER: S-EPMC5320794 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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

Voshol Gerben P GP Vijgenboom Erik E Punt Peter J PJ

BMC research notes 20170221 1

<h4>Background</h4>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 ( ...[more]

PMID: 28222763

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Project description:BackgroundIt has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a "context-sensitive" Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification.ResultsThe probabilities of the CSHMM model were estimated using known human miRNA sequences. A classifier for miRNAs based on the likelihood score of this "trained" CSHMM was evaluated by: (a) cross-validation estimates using known human sequences, (b) predictions on a dataset of known miRNAs, and (c) prediction on a dataset of non coding RNAs. The CSHMM is compared with two recently developed methods, miPred and CID-miRNA. The results suggest that the CSHMM performs better than these methods. In addition, the CSHMM was used in a pipeline that includes filters that check for the presence of EST matches and the presence of Drosha cutting sites. This pipeline was used to scan and identify potential miRNAs from the human chromosome 19. It was also used to identify novel miRNAs from small RNA sequences of human normal leukocytes obtained by the Deep sequencing (Solexa) methodology. A total of 49 and 308 novel miRNAs were predicted from chromosome 19 and from the small RNA sequences respectively.ConclusionThe results suggest that the CSHMM is likely to be a useful tool for miRNA discovery either for analysis of individual sequences or for genome scan. Our pipeline, consisting of a CSHMM and filters to reduce false positives shows promise as an approach for ab initio identification of novel miRNAs.

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

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

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