A Machine Learning Bioinformatics Method to Predict Biological Activity from Biosynthetic Gene Clusters.

ABSTRACT: Research in natural products, the genetically encoded small molecules produced by organisms in an idiosyncratic fashion, deals with molecular structure, biosynthesis, and biological activity. Bioinformatics analyses of microbial genomes can successfully reveal the genetic instructions, biosynthetic gene clusters, that produce many natural products. Genes to molecule predictions made on biosynthetic gene clusters have revealed many important new structures. There is no comparable method for genes to biological activity predictions. To address this missing pathway, we developed a machine learning bioinformatics method for predicting a natural product's antibiotic activity directly from the sequence of its biosynthetic gene cluster. We trained commonly used machine learning classifiers to pre

SUBMITTER: Walker AS 

PROVIDER: S-EPMC8243324 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature