Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

Wang, Kate; Romm, Eden L.; Kouznetsova, Valentina L.; Tsigelny, Igor F.

doi:10.3390/molecules25173886

Open AccessFeature PaperArticle

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

by

Kate Wang

^1,†,

Eden L. Romm

^2,†,

Valentina L. Kouznetsova

^3,† and

Igor F. Tsigelny

^2,3,4,*

¹

MAP program, University of California San Diego (UCSD), La Jolla, CA 92093, USA

²

Curematch Inc., 6440 Lusk Blvd, Suite D206, San Diego, CA 92121, USA

³

San Diego Supercomputer Center, University of California San Diego (UCSD), La Jolla, CA 92093, USA

⁴

Dept. of Neurosciences, University of California San Diego (UCSD), La Jolla, CA 92093, USA

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Molecules 2020, 25(17), 3886; https://doi.org/10.3390/molecules25173886

Submission received: 25 June 2020 / Revised: 14 August 2020 / Accepted: 20 August 2020 / Published: 26 August 2020

(This article belongs to the Special Issue AI in Drug Design)

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Abstract

A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing compounds (PTCSC) have been developed in order to restore protein translation by allowing the incorporation of an amino acid in place of a stop codon. However, limitations exist in terms of efficacy and toxicity. To identify new compounds that have PTC-suppressing ability, we selected and clustered existing PTCSC, allowing for the construction of a common pharmacophore model. Machine learning (ML) and deep learning (DL) models were developed for prediction of new PTCSC based on known compounds. We conducted a search of the NCI compounds database using the pharmacophore-based model and a search of the DrugBank database using pharmacophore-based, ML and DL models. Sixteen drug compounds were selected as a consensus of pharmacophore-based, ML, and DL searches. Our results suggest notable correspondence of the pharmacophore-based, ML, and DL models in prediction of new PTC-suppressing compounds.

Keywords: Duchenne muscular dystrophy; stop codon; machine learning; deep learning; pharmacophore; PTC-suppressing compounds

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MDPI and ACS Style

Wang, K.; Romm, E.L.; Kouznetsova, V.L.; Tsigelny, I.F. Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning. Molecules 2020, 25, 3886. https://doi.org/10.3390/molecules25173886

AMA Style

Wang K, Romm EL, Kouznetsova VL, Tsigelny IF. Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning. Molecules. 2020; 25(17):3886. https://doi.org/10.3390/molecules25173886

Chicago/Turabian Style

Wang, Kate, Eden L. Romm, Valentina L. Kouznetsova, and Igor F. Tsigelny. 2020. "Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning" Molecules 25, no. 17: 3886. https://doi.org/10.3390/molecules25173886

APA Style

Wang, K., Romm, E. L., Kouznetsova, V. L., & Tsigelny, I. F. (2020). Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning. Molecules, 25(17), 3886. https://doi.org/10.3390/molecules25173886

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Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

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