Cargando…

Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning

Stacking interactions play a crucial role in drug design, as we can find aromatic cores or scaffolds in almost any available small molecule drug. To predict optimal binding geometries and enhance stacking interactions, usually high-level quantum mechanical calculations are performed. These calculati...

Descripción completa

Detalles Bibliográficos
Autores principales: Loeffler, Johannes R., Fernández-Quintero, Monica L., Waibl, Franz, Quoika, Patrick K., Hofer, Florian, Schauperl, Michael, Liedl, Klaus R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032969/
https://www.ncbi.nlm.nih.gov/pubmed/33842433
http://dx.doi.org/10.3389/fchem.2021.641610
_version_ 1783676321731182592
author Loeffler, Johannes R.
Fernández-Quintero, Monica L.
Waibl, Franz
Quoika, Patrick K.
Hofer, Florian
Schauperl, Michael
Liedl, Klaus R.
author_facet Loeffler, Johannes R.
Fernández-Quintero, Monica L.
Waibl, Franz
Quoika, Patrick K.
Hofer, Florian
Schauperl, Michael
Liedl, Klaus R.
author_sort Loeffler, Johannes R.
collection PubMed
description Stacking interactions play a crucial role in drug design, as we can find aromatic cores or scaffolds in almost any available small molecule drug. To predict optimal binding geometries and enhance stacking interactions, usually high-level quantum mechanical calculations are performed. These calculations have two major drawbacks: they are very time consuming, and solvation can only be considered using implicit solvation. Therefore, most calculations are performed in vacuum. However, recent studies have revealed a direct correlation between the desolvation penalty, vacuum stacking interactions and binding affinity, making predictions even more difficult. To overcome the drawbacks of quantum mechanical calculations, in this study we use neural networks to perform fast geometry optimizations and molecular dynamics simulations of heteroaromatics stacked with toluene in vacuum and in explicit solvation. We show that the resulting energies in vacuum are in good agreement with high-level quantum mechanical calculations. Furthermore, we show that using explicit solvation substantially influences the favored orientations of heteroaromatic rings thereby emphasizing the necessity to include solvation properties starting from the earliest phases of drug design.
format Online
Article
Text
id pubmed-8032969
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-80329692021-04-10 Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning Loeffler, Johannes R. Fernández-Quintero, Monica L. Waibl, Franz Quoika, Patrick K. Hofer, Florian Schauperl, Michael Liedl, Klaus R. Front Chem Chemistry Stacking interactions play a crucial role in drug design, as we can find aromatic cores or scaffolds in almost any available small molecule drug. To predict optimal binding geometries and enhance stacking interactions, usually high-level quantum mechanical calculations are performed. These calculations have two major drawbacks: they are very time consuming, and solvation can only be considered using implicit solvation. Therefore, most calculations are performed in vacuum. However, recent studies have revealed a direct correlation between the desolvation penalty, vacuum stacking interactions and binding affinity, making predictions even more difficult. To overcome the drawbacks of quantum mechanical calculations, in this study we use neural networks to perform fast geometry optimizations and molecular dynamics simulations of heteroaromatics stacked with toluene in vacuum and in explicit solvation. We show that the resulting energies in vacuum are in good agreement with high-level quantum mechanical calculations. Furthermore, we show that using explicit solvation substantially influences the favored orientations of heteroaromatic rings thereby emphasizing the necessity to include solvation properties starting from the earliest phases of drug design. Frontiers Media S.A. 2021-03-26 /pmc/articles/PMC8032969/ /pubmed/33842433 http://dx.doi.org/10.3389/fchem.2021.641610 Text en Copyright © 2021 Loeffler, Fernández-Quintero, Waibl, Quoika, Hofer, Schauperl and Liedl. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Chemistry
Loeffler, Johannes R.
Fernández-Quintero, Monica L.
Waibl, Franz
Quoika, Patrick K.
Hofer, Florian
Schauperl, Michael
Liedl, Klaus R.
Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning
title Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning
title_full Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning
title_fullStr Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning
title_full_unstemmed Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning
title_short Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning
title_sort conformational shifts of stacked heteroaromatics: vacuum vs. water studied by machine learning
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032969/
https://www.ncbi.nlm.nih.gov/pubmed/33842433
http://dx.doi.org/10.3389/fchem.2021.641610
work_keys_str_mv AT loefflerjohannesr conformationalshiftsofstackedheteroaromaticsvacuumvswaterstudiedbymachinelearning
AT fernandezquinteromonical conformationalshiftsofstackedheteroaromaticsvacuumvswaterstudiedbymachinelearning
AT waiblfranz conformationalshiftsofstackedheteroaromaticsvacuumvswaterstudiedbymachinelearning
AT quoikapatrickk conformationalshiftsofstackedheteroaromaticsvacuumvswaterstudiedbymachinelearning
AT hoferflorian conformationalshiftsofstackedheteroaromaticsvacuumvswaterstudiedbymachinelearning
AT schauperlmichael conformationalshiftsofstackedheteroaromaticsvacuumvswaterstudiedbymachinelearning
AT liedlklausr conformationalshiftsofstackedheteroaromaticsvacuumvswaterstudiedbymachinelearning