Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning.

ABSTRACT: Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cy

SUBMITTER: Riedling O 

PROVIDER: S-EPMC10515863 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature