Dataset Information

Chemical, Bioactivity, and Biosynthetic Screening of Epiphytic Fungus Zasmidium pseudotsugae.

ABSTRACT: We report the first secondary metabolite, 8,8'-bijuglone, obtained from pure cultures of the slow growing Douglas fir- (Pseudotsuga menziesii var. menziesii) foliage-associated fungus Zasmidium pseudotsugae. The quinone was characterized using extensive LC/MS and NMR-based spectroscopic methods. 8,8'-Bijuglone exhibited moderate antibiotic activity against Gram-positive pathogens and weak cytotoxic activity in the NCI-60 cell line panel and in our in-house human colon carcinoma (HCT-116) cell line. An analysis of the fungal genome sequence to assess its metabolic potential was implemented using the bioinformatic tool antiSMASH. In total, 36 putative biosynthetic gene clusters were found with a majority encoding for polyketides (17), followed by non-ribosomal peptides (14), terpenes (2), ribosomal peptides (1), and compounds with mixed biosynthetic origin (2). This study demonstrates that foliage associated fungi of conifers produce antimicrobial metabolites and suggests this guild of fungi may present a rich source of novel molecules.

SUBMITTER: Gonzalez-Montiel GA

PROVIDER: S-EPMC7287617 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Chemical, Bioactivity, and Biosynthetic Screening of Epiphytic Fungus Zasmidium pseudotsugae.

González-Montiel Gisela A GA Kaweesa Elizabeth N EN Feau Nicolas N Hamelin Richard C RC Stone Jeffrey K JK Loesgen Sandra S

Molecules (Basel, Switzerland) 20200519 10

We report the first secondary metabolite, 8,8'-bijuglone, obtained from pure cultures of the slow growing Douglas fir- (Pseudotsuga menziesii var. menziesii) foliage-associated fungus Zasmidium pseudotsugae. The quinone was characterized using extensive LC/MS and NMR-based spectroscopic methods. 8,8'-Bijuglone exhibited moderate antibiotic activity against Gram-positive pathogens and weak cytotoxic activity in the NCI-60 cell line panel and in our in-house human colon carcin ...[more]

PMID: 32438585

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Project description:BackgroundIn silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds.ResultsA Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76.ConclusionsThe inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models. The realised target prediction protocol is available at https://github.com/lhm30/PIDGIN.Graphical abstractThe inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models.

Dataset Information

Chemical, Bioactivity, and Biosynthetic Screening of Epiphytic Fungus Zasmidium pseudotsugae.

Publications

Chemical, Bioactivity, and Biosynthetic Screening of Epiphytic Fungus <i>Zasmidium pseudotsugae</i>.

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