Discovery of novel potential plant growth regulators from Corydalis mucronifera

Zhang, Jun; Shi, Lianjie; Yin, Xu; Xu, Fuchun; Zhang, Qingying; Tu, Peng‐Fei; Liang, Hong

doi:10.1016/j.fitote.2020.104776

“…Among thirteen recently reported NPs from plants, fungi, or bacteria, eleven were correctly assigned to their origin, while only two were misassigned (Table 3, Figure 3). In details, the fungal epicospirocin 1, [33] penicimeroterpenoid A, [34] and beetleane A, [35] the bacterial bosamycin A, [36] and the plant hunzeylanine A, [37] hyperfol B, [38] pegaharmol A, [39] mucroniferal A, [40] meloyunnanine A, [41] perovsfolin A, [42], and horienoid A [43] were correctly classified. On the other hand, the fungal rhizolutin [44] and the bacterial marinoterpin A [45] were misclassified as plant NP.…”

Section: Using the Map4 Svm To Assign The Origin Of Npsmentioning

confidence: 99%

Classifying Natural Products from Plants, Fungi or Bacteria in the COCONUT Database

Capecchi

¹

,

Reymond

²

2021

Preprint

0

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Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin (https://tm.gdb.tools/map4/coconut_tmap/), and a support vector machine (SVM) trained with MAP4 correctly assigned the origin for 94% of plant, 89% of fungal, and 89% of bacterial NPs in this subset. An online tool based on an SVM trained with the entire subset correctly assigned the origin of further NPs with similar performance (https://np-svm-map4.gdb.tools/). Origin information might be useful when searching for biosynthetic genes of NPs isolated from plants but produced by endophytic microorganisms.

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“…3). In details, the fungal epicospirocin 1 [33], penicimeroterpenoid A [34], beetleane A [35], funiculolide D [36], and fusoxypenes A [37], the bacterial vertirhodin A [38], bosamycin A [39], and dumulmycin [40], and the plant fortuneicyclidin A [41], meloyunnanine A [42], hyperfol B [43], pegaharmol A [44], hunzeylanine A [45], mucroniferal A [46], perovsfolin A [47], horienoid A [48], and erythrivarine J [49] were correctly classified. On the other hand, the fungal rhizolutin [50] and myxadazoles A [51] and the bacterial marinoterpin A [52] were misclassified.…”

Section: Using the Map4 Svm To Assign The Origin Of Npsmentioning

confidence: 99%

Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning

Capecchi

¹

,

Reymond

²

2021

View full text Add to dashboard Cite

Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin (https://tm.gdb.tools/map4/coconut_tmap/), and a support vector machine (SVM) trained with MAP4 correctly assigned the origin for 94% of plant, 89% of fungal, and 89% of bacterial NPs in this subset. An online tool based on an SVM trained with the entire subset correctly assigned the origin of further NPs with similar performance (https://np-svm-map4.gdb.tools/). Origin information might be useful when searching for biosynthetic genes of NPs isolated from plants but produced by endophytic microorganisms.

show abstract

“…3). In details, the fungal epicospirocin 1 [33], penicimeroterpenoid A [34], beetleane A [35], funiculolide D [36], and fusoxypenes A [37], the bacterial vertirhodin A [38], bosamycin A [39], and dumulmycin [40], and the plant fortuneicyclidin A [41], meloyunnanine A [42], hyperfol B [43], pegaharmol A [44], hunzeylanine A [45], mucroniferal A [46], perovsfolin A [47], horienoid A [48], and erythrivarine J [49] were correctly classified. On the other hand, the fungal rhizolutin [50] and myxadazoles A [51] and the bacterial marinoterpin A [52] were misclassified.…”

Section: Using the Map4 Svm To Assign The Origin Of Npsmentioning

confidence: 99%

Classifying Natural Products from Plants, Fungi or Bacteria using the COCONUT Database and Machine Learning

Capecchi

¹

,

Reymond

²

2021

Preprint

View full text Add to dashboard Cite

Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin (https://tm.gdb.tools/map4/coconut_tmap/), and a support vector machine (SVM) trained with MAP4 correctly assigned the origin for 94% of plant, 89% of fungal, and 89% of bacterial NPs in this subset. An online tool based on an SVM trained with the entire subset correctly assigned the origin of further NPs with similar performance (https://np-svm-map4.gdb.tools/). Origin information might be useful when searching for biosynthetic genes of NPs isolated from plants but produced by endophytic microorganisms.

show abstract

Discovery of novel potential plant growth regulators from Corydalis mucronifera

Cited by 6 publications

References 15 publications

Classifying Natural Products from Plants, Fungi or Bacteria in the COCONUT Database

Classifying Natural Products from Plants, Fungi or Bacteria in the COCONUT Database

Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning

Classifying Natural Products from Plants, Fungi or Bacteria using the COCONUT Database and Machine Learning

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