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Data-driven discovery of cardiolipin-selective small molecules by computational active learning

Subtle variations in the lipid composition of mitochondrial membranes can have a profound impact on mitochondrial function. The inner mitochondrial membrane contains the phospholipid cardiolipin, which has been demonstrated to act as a biomarker for a number of diverse pathologies. Small molecule dy...

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Autores principales: Mohr, Bernadette, Shmilovich, Kirill, Kleinwächter, Isabel S., Schneider, Dirk, Ferguson, Andrew L., Bereau, Tristan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019913/
https://www.ncbi.nlm.nih.gov/pubmed/35656132
http://dx.doi.org/10.1039/d2sc00116k
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author Mohr, Bernadette
Shmilovich, Kirill
Kleinwächter, Isabel S.
Schneider, Dirk
Ferguson, Andrew L.
Bereau, Tristan
author_facet Mohr, Bernadette
Shmilovich, Kirill
Kleinwächter, Isabel S.
Schneider, Dirk
Ferguson, Andrew L.
Bereau, Tristan
author_sort Mohr, Bernadette
collection PubMed
description Subtle variations in the lipid composition of mitochondrial membranes can have a profound impact on mitochondrial function. The inner mitochondrial membrane contains the phospholipid cardiolipin, which has been demonstrated to act as a biomarker for a number of diverse pathologies. Small molecule dyes capable of selectively partitioning into cardiolipin membranes enable visualization and quantification of the cardiolipin content. Here we present a data-driven approach that combines a deep learning-enabled active learning workflow with coarse-grained molecular dynamics simulations and alchemical free energy calculations to discover small organic compounds able to selectively permeate cardiolipin-containing membranes. By employing transferable coarse-grained models we efficiently navigate the all-atom design space corresponding to small organic molecules with molecular weight less than ≈500 Da. After direct simulation of only 0.42% of our coarse-grained search space we identify molecules with considerably increased levels of cardiolipin selectivity compared to a widely used cardiolipin probe 10-N-nonyl acridine orange. Our accumulated simulation data enables us to derive interpretable design rules linking coarse-grained structure to cardiolipin selectivity. The findings are corroborated by fluorescence anisotropy measurements of two compounds conforming to our defined design rules. Our findings highlight the potential of coarse-grained representations and multiscale modelling for materials discovery and design.
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spelling pubmed-90199132022-06-01 Data-driven discovery of cardiolipin-selective small molecules by computational active learning Mohr, Bernadette Shmilovich, Kirill Kleinwächter, Isabel S. Schneider, Dirk Ferguson, Andrew L. Bereau, Tristan Chem Sci Chemistry Subtle variations in the lipid composition of mitochondrial membranes can have a profound impact on mitochondrial function. The inner mitochondrial membrane contains the phospholipid cardiolipin, which has been demonstrated to act as a biomarker for a number of diverse pathologies. Small molecule dyes capable of selectively partitioning into cardiolipin membranes enable visualization and quantification of the cardiolipin content. Here we present a data-driven approach that combines a deep learning-enabled active learning workflow with coarse-grained molecular dynamics simulations and alchemical free energy calculations to discover small organic compounds able to selectively permeate cardiolipin-containing membranes. By employing transferable coarse-grained models we efficiently navigate the all-atom design space corresponding to small organic molecules with molecular weight less than ≈500 Da. After direct simulation of only 0.42% of our coarse-grained search space we identify molecules with considerably increased levels of cardiolipin selectivity compared to a widely used cardiolipin probe 10-N-nonyl acridine orange. Our accumulated simulation data enables us to derive interpretable design rules linking coarse-grained structure to cardiolipin selectivity. The findings are corroborated by fluorescence anisotropy measurements of two compounds conforming to our defined design rules. Our findings highlight the potential of coarse-grained representations and multiscale modelling for materials discovery and design. The Royal Society of Chemistry 2022-03-02 /pmc/articles/PMC9019913/ /pubmed/35656132 http://dx.doi.org/10.1039/d2sc00116k Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Mohr, Bernadette
Shmilovich, Kirill
Kleinwächter, Isabel S.
Schneider, Dirk
Ferguson, Andrew L.
Bereau, Tristan
Data-driven discovery of cardiolipin-selective small molecules by computational active learning
title Data-driven discovery of cardiolipin-selective small molecules by computational active learning
title_full Data-driven discovery of cardiolipin-selective small molecules by computational active learning
title_fullStr Data-driven discovery of cardiolipin-selective small molecules by computational active learning
title_full_unstemmed Data-driven discovery of cardiolipin-selective small molecules by computational active learning
title_short Data-driven discovery of cardiolipin-selective small molecules by computational active learning
title_sort data-driven discovery of cardiolipin-selective small molecules by computational active learning
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019913/
https://www.ncbi.nlm.nih.gov/pubmed/35656132
http://dx.doi.org/10.1039/d2sc00116k
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