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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2022
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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. |
format | Online Article Text |
id | pubmed-9019913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
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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