Dataset Information

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

ABSTRACT: This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

SUBMITTER: Reher R

PROVIDER: S-EPMC7210566 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

Reher Raphael R Kim Hyun Woo HW Zhang Chen C Mao Huanru Henry HH Wang Mingxun M Nothias Louis-Félix LF Caraballo-Rodriguez Andres Mauricio AM Glukhov Evgenia E Teke Bahar B Leao Tiago T Alexander Kelsey L KL Duggan Brendan M BM Van Everbroeck Ezra L EL Dorrestein Pieter C PC Cottrell Garrison W GW Gerwick William H WH

Journal of the American Chemical Society 20200221 9

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental <i>Symploca</i> extract was roughly fractionated, and then prioritized and guided by c ...[more]

PMID: 32045230

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Project description:BackgroundThe genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework. As sites targeted by natural selection are likely to harbor important functionalities for the carrier, the identification of selection signatures in the genome has the potential to unveil the genetic mechanisms underpinning human phenotypes. Popular methods of detecting such signals rely on compressing genomic information into summary statistics, resulting in the loss of information. Furthermore, few methods are able to quantify the strength of selection. Here we explored the use of deep learning in evolutionary biology and implemented a program, called ImaGene, to apply convolutional neural networks on population genomic data for the detection and quantification of natural selection.ResultsImaGene enables genomic information from multiple individuals to be represented as abstract images. Each image is created by stacking aligned genomic data and encoding distinct alleles into separate colors. To detect and quantify signatures of positive selection, ImaGene implements a convolutional neural network which is trained using simulations. We show how the method implemented in ImaGene can be affected by data manipulation and learning strategies. In particular, we show how sorting images by row and column leads to accurate predictions. We also demonstrate how the misspecification of the correct demographic model for producing training data can influence the quantification of positive selection. We finally illustrate an approach to estimate the selection coefficient, a continuous variable, using multiclass classification techniques.ConclusionsWhile the use of deep learning in evolutionary genomics is in its infancy, here we demonstrated its potential to detect informative patterns from large-scale genomic data. We implemented methods to process genomic data for deep learning in a user-friendly program called ImaGene. The joint inference of the evolutionary history of mutations and their functional impact will facilitate mapping studies and provide novel insights into the molecular mechanisms associated with human phenotypes.

Dataset Information

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

Publications

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

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