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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

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

Allison S Walker¹, Jon Clardy¹

¹Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, United States.

Journal of Chemical Information and Modeling

|May 27, 2021

Summary

Researchers developed a machine learning method to predict natural product antibiotic activity from biosynthetic gene clusters. This bioinformatics tool achieves 80% accuracy, identifying key enzymes and molecular features linked to antibiotic properties.

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Area of Science:

Biochemistry
Bioinformatics
Microbiology

Background:

Natural products are genetically encoded small molecules with diverse biological activities.
Bioinformatics analysis of microbial genomes identifies biosynthetic gene clusters (BGCs) for natural product discovery.
Predicting molecular structures from BGCs is established, but predicting biological activity remains a challenge.

Purpose of the Study:

To develop a machine learning (ML) bioinformatics method for predicting natural product antibiotic activity directly from BGC sequences.
To address the gap in predicting biological activity from genetic information.

Main Methods:

Utilized machine learning classifiers trained on features extracted from known natural product BGCs.
Input features included sequence data and characteristics of the BGCs.
Output predictions focused on antibacterial and antifungal activity.

Main Results:

Achieved prediction accuracies as high as 80% for antibiotic activity.
Identified specific biosynthetic enzymes and molecular features associated with antibiotic activity.
Demonstrated the potential of ML for predicting functional properties of natural products.

Conclusions:

The developed ML method offers a powerful approach for predicting antibiotic activity of natural products from their BGCs.
This tool can accelerate the discovery of novel antibiotics by prioritizing BGCs with predicted antimicrobial potential.
Facilitates the identification of key enzymatic and molecular determinants of antibiotic activity.