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Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins
[Image: see text] Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a spec...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8790751/ https://www.ncbi.nlm.nih.gov/pubmed/35020376 http://dx.doi.org/10.1021/acs.jcim.1c00823 |
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author | Smilova, Mihaela D. Curran, Peter R. Radoux, Chris J. von Delft, Frank Cole, Jason C. Bradley, Anthony R. Marsden, Brian D. |
author_facet | Smilova, Mihaela D. Curran, Peter R. Radoux, Chris J. von Delft, Frank Cole, Jason C. Bradley, Anthony R. Marsden, Brian D. |
author_sort | Smilova, Mihaela D. |
collection | PubMed |
description | [Image: see text] Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family. |
format | Online Article Text |
id | pubmed-8790751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-87907512022-01-27 Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins Smilova, Mihaela D. Curran, Peter R. Radoux, Chris J. von Delft, Frank Cole, Jason C. Bradley, Anthony R. Marsden, Brian D. J Chem Inf Model [Image: see text] Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family. American Chemical Society 2022-01-12 2022-01-24 /pmc/articles/PMC8790751/ /pubmed/35020376 http://dx.doi.org/10.1021/acs.jcim.1c00823 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Smilova, Mihaela D. Curran, Peter R. Radoux, Chris J. von Delft, Frank Cole, Jason C. Bradley, Anthony R. Marsden, Brian D. Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins |
title | Fragment Hotspot Mapping to Identify Selectivity-Determining
Regions between Related Proteins |
title_full | Fragment Hotspot Mapping to Identify Selectivity-Determining
Regions between Related Proteins |
title_fullStr | Fragment Hotspot Mapping to Identify Selectivity-Determining
Regions between Related Proteins |
title_full_unstemmed | Fragment Hotspot Mapping to Identify Selectivity-Determining
Regions between Related Proteins |
title_short | Fragment Hotspot Mapping to Identify Selectivity-Determining
Regions between Related Proteins |
title_sort | fragment hotspot mapping to identify selectivity-determining
regions between related proteins |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8790751/ https://www.ncbi.nlm.nih.gov/pubmed/35020376 http://dx.doi.org/10.1021/acs.jcim.1c00823 |
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