Aspermicrones A-C, novel dibenzospiroketals from the seaweed-derived endophytic fungus Aspergillus micronesiensis

Luyen, Nguyen Dinh; Hương, Lê Mai; Ha, Tran Thi Hong; Cuong, Le Huu; Yến, Dương Thị Hải; Nhiệm, Nguyễn Xuân; Tài, Bùi Hữu; Gärdes, Astrid; Kopprio, Gérmán A.; Kiệm, Phan Văn

doi:10.1038/s41429-019-0214-8

Cited by 18 publications

(15 citation statements)

References 8 publications

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“…To our delight, the classifier correctly predicted the fungal origin for the newly reported epicospirocins 1 [56], penicimeroterpenoid A [57], and rhizolutin [58], as well as the bacterial origin of the recently reported bosamycin A [59]. The correct origin assignment is probably related to the presence of structurally similar NPs within the NPAtlas training set, illustrated here by the MAP4 nearest-neighbor NPs aspermicrone A [60], isocitreohybridone H [61], Monacolin K [62], and AIP I [63] (Figure 3). When challenged with the recently reported NP phakefustatin A, isolated from the marine sponge Phakellia fusca [64] (Figure 3), the MAP4 SVM classifier predicted a bacterial origin (Table 4).…”

Section: Predicting the Origin Of Newly Discovered Npsmentioning

confidence: 73%

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Capecchi¹,

Reymond²

2020

Preprint

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<p>Microbial natural products (NPs) are an important source of drugs. However, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin downloaded from <a href="https://www.npatlas.org/joomla/">https://www.npatlas.org/joomla/</a>. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP) (<a href="http://tmap.gdb.tools/">http://tmap.gdb.tools</a>). The resulting interactive map (<a href="https://tm.gdb.tools/map4/npatlas_map_tmap/">https://tm.gdb.tools/map4/npatlas_map_tmap/</a>) organizes molecules by physico-chemical properties and compound families such as peptides, glycosides, polyphenols or terpenoids. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite of their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin. </p>

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Section: Predicting the Origin Of Newly Discovered Npsmentioning

confidence: 73%

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Capecchi¹,

Reymond²

2020

Preprint

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show abstract

“…To our delight, the classifier correctly predicted the fungal origin for the newly reported epicospirocins 1 [ 56 ], penicimeroterpenoid A [ 57 ], and rhizolutin [ 58 ], as well as the bacterial origin of the recently reported bosamycin A [ 59 ]. The correct origin assignment is probably related to the presence of structurally similar NPs within the NPAtlas training set, illustrated here by the MAP4 nearest-neighbor NPs aspermicrone A [ 60 ], isocitreohybridone H [ 61 ], Monacolin K [ 62 ], and AIP I [ 63 ] ( Figure 3 ).…”

Section: Resultsmentioning

confidence: 99%

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Capecchi

Reymond

2020

Biomolecules

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Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.

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“…The other known compounds were identied as aspermicrone B (2), 22 aspermicrone C (3), 22 4,5,6-trihydroxy-7-methyl phthalide (5), 23 4,5,6-trihydroxy-7-methyl-1,3-dihydroisobenzofuran (6) 24 by comparison of their spectroscopic data with those reported in the literature.…”

Section: Structure Elucidationmentioning

confidence: 99%

Fused multicyclic polyketides with a two-spiro-carbon skeleton from mangrove-derived endophytic fungus Epicoccum nigrum SCNU-F0002

Yan

et al. 2020

RSC Adv.

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Aspermicrones A-C, novel dibenzospiroketals from the seaweed-derived endophytic fungus Aspergillus micronesiensis

Cited by 18 publications

References 8 publications

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Fused multicyclic polyketides with a two-spiro-carbon skeleton from mangrove-derived endophytic fungus Epicoccum nigrum SCNU-F0002

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