The druggable genome: Twenty years later
The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the P...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580872/ https://www.ncbi.nlm.nih.gov/pubmed/36304325 http://dx.doi.org/10.3389/fbinf.2022.958378 |
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author | Radoux, Chris J. Vianello, Francesca McGreig, Jake Desai, Nikita Bradley, Anthony R. |
author_facet | Radoux, Chris J. Vianello, Francesca McGreig, Jake Desai, Nikita Bradley, Anthony R. |
author_sort | Radoux, Chris J. |
collection | PubMed |
description | The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future. |
format | Online Article Text |
id | pubmed-9580872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95808722022-10-26 The druggable genome: Twenty years later Radoux, Chris J. Vianello, Francesca McGreig, Jake Desai, Nikita Bradley, Anthony R. Front Bioinform Bioinformatics The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future. Frontiers Media S.A. 2022-09-30 /pmc/articles/PMC9580872/ /pubmed/36304325 http://dx.doi.org/10.3389/fbinf.2022.958378 Text en Copyright © 2022 Radoux, Vianello, McGreig, Desai and Bradley. 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 | Bioinformatics Radoux, Chris J. Vianello, Francesca McGreig, Jake Desai, Nikita Bradley, Anthony R. The druggable genome: Twenty years later |
title | The druggable genome: Twenty years later |
title_full | The druggable genome: Twenty years later |
title_fullStr | The druggable genome: Twenty years later |
title_full_unstemmed | The druggable genome: Twenty years later |
title_short | The druggable genome: Twenty years later |
title_sort | druggable genome: twenty years later |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580872/ https://www.ncbi.nlm.nih.gov/pubmed/36304325 http://dx.doi.org/10.3389/fbinf.2022.958378 |
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