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 https://www.npatlas.org/joomla/. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP) (http://tmap.gdb.tools). The resulting interactive map (https://tm.gdb.tools/map4/npatlas_map_tmap/) 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.
Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning
02 September 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.