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

Alice Capecchi; Jean-Louis Reymond

doi:10.1186/s13321-021-00559-3

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

J Cheminform. 2021 Oct 18;13(1):82. doi: 10.1186/s13321-021-00559-3.

Authors

Alice Capecchi¹, Jean-Louis Reymond²

Affiliations

¹ 1 Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland.
² 1 Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland. jean-louis.reymond@dcb.unibe.ch.

Abstract

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.

Keywords: Chemical space; Cheminformatics; Machine learning; Molecular fingerprints; Natural products; Support vector machine; Visualization.

Abstract

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