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

Capecchi, Alice; Reymond, Jean-Louis (2020). Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning. Biomolecules, 10(10), p. 1385. MDPI 10.3390/biom10101385

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP)

UniBE Contributor:

Capecchi, Alice and Reymond, Jean-Louis

Subjects:

500 Science > 570 Life sciences; biology
500 Science > 540 Chemistry

ISSN:

2218-273X

Publisher:

MDPI

Language:

English

Submitter:

Sandra Tanja Zbinden Di Biase

Date Deposited:

19 Jan 2021 08:30

Last Modified:

24 Jan 2021 02:58

Publisher DOI:

10.3390/biom10101385

BORIS DOI:

10.48350/148882

URI:

https://boris.unibe.ch/id/eprint/148882

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