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Big Data analytics for improved prediction of ligand binding and conformational selection

This research introduces new machine learning and deep learning approaches, collectively referred to as Big Data analytics techniques that are unique to address the protein conformational selection mechanism for protein:ligands complexes. The novel Big Data analytics techniques presented in this wor...

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Detalles Bibliográficos
Autores principales: Gupta, Shivangi, Baudry, Jerome, Menon, Vineetha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878559/
https://www.ncbi.nlm.nih.gov/pubmed/36710883
http://dx.doi.org/10.3389/fmolb.2022.953984
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author Gupta, Shivangi
Baudry, Jerome
Menon, Vineetha
author_facet Gupta, Shivangi
Baudry, Jerome
Menon, Vineetha
author_sort Gupta, Shivangi
collection PubMed
description This research introduces new machine learning and deep learning approaches, collectively referred to as Big Data analytics techniques that are unique to address the protein conformational selection mechanism for protein:ligands complexes. The novel Big Data analytics techniques presented in this work enables efficient data processing of a large number of protein:ligand complexes, and provides better identification of specific protein properties that are responsible for a high probability of correct prediction of protein:ligand binding. The GPCR proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2), OPRD1 (Opioid receptor Delta 1) and OPRK1 (Opioid Receptor Kappa 1) are examined in this study using Big Data analytics techniques, which can efficiently process a huge ensemble of protein conformations, and significantly enhance the prediction of binding protein conformation (i.e., the protein conformations that will be selected by the ligands for binding) about 10–38 times better than its random selection counterpart for protein conformation selection. In addition to providing a Big Data approach to the conformational selection mechanism, this also opens the door to the systematic identification of such “binding conformations” for proteins. The physico-chemical features that are useful in predicting the “binding conformations” are largely, but not entirely, shared among the test proteins, indicating that the biophysical properties that drive the conformation selection mechanism may, to an extent, be protein-specific for the protein properties used in this work.
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spelling pubmed-98785592023-01-27 Big Data analytics for improved prediction of ligand binding and conformational selection Gupta, Shivangi Baudry, Jerome Menon, Vineetha Front Mol Biosci Molecular Biosciences This research introduces new machine learning and deep learning approaches, collectively referred to as Big Data analytics techniques that are unique to address the protein conformational selection mechanism for protein:ligands complexes. The novel Big Data analytics techniques presented in this work enables efficient data processing of a large number of protein:ligand complexes, and provides better identification of specific protein properties that are responsible for a high probability of correct prediction of protein:ligand binding. The GPCR proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2), OPRD1 (Opioid receptor Delta 1) and OPRK1 (Opioid Receptor Kappa 1) are examined in this study using Big Data analytics techniques, which can efficiently process a huge ensemble of protein conformations, and significantly enhance the prediction of binding protein conformation (i.e., the protein conformations that will be selected by the ligands for binding) about 10–38 times better than its random selection counterpart for protein conformation selection. In addition to providing a Big Data approach to the conformational selection mechanism, this also opens the door to the systematic identification of such “binding conformations” for proteins. The physico-chemical features that are useful in predicting the “binding conformations” are largely, but not entirely, shared among the test proteins, indicating that the biophysical properties that drive the conformation selection mechanism may, to an extent, be protein-specific for the protein properties used in this work. Frontiers Media S.A. 2023-01-12 /pmc/articles/PMC9878559/ /pubmed/36710883 http://dx.doi.org/10.3389/fmolb.2022.953984 Text en Copyright © 2023 Gupta, Baudry and Menon. 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 Molecular Biosciences
Gupta, Shivangi
Baudry, Jerome
Menon, Vineetha
Big Data analytics for improved prediction of ligand binding and conformational selection
title Big Data analytics for improved prediction of ligand binding and conformational selection
title_full Big Data analytics for improved prediction of ligand binding and conformational selection
title_fullStr Big Data analytics for improved prediction of ligand binding and conformational selection
title_full_unstemmed Big Data analytics for improved prediction of ligand binding and conformational selection
title_short Big Data analytics for improved prediction of ligand binding and conformational selection
title_sort big data analytics for improved prediction of ligand binding and conformational selection
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878559/
https://www.ncbi.nlm.nih.gov/pubmed/36710883
http://dx.doi.org/10.3389/fmolb.2022.953984
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