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High-throughput binding affinity calculations at extreme scales
BACKGROUND: Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overco...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302294/ https://www.ncbi.nlm.nih.gov/pubmed/30577753 http://dx.doi.org/10.1186/s12859-018-2506-6 |
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author | Dakka, Jumana Turilli, Matteo Wright, David W. Zasada, Stefan J. Balasubramanian, Vivek Wan, Shunzhou Coveney, Peter V. Jha, Shantenu |
author_facet | Dakka, Jumana Turilli, Matteo Wright, David W. Zasada, Stefan J. Balasubramanian, Vivek Wan, Shunzhou Coveney, Peter V. Jha, Shantenu |
author_sort | Dakka, Jumana |
collection | PubMed |
description | BACKGROUND: Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance. RESULTS: We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations. CONCLUSIONS: As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery. |
format | Online Article Text |
id | pubmed-6302294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63022942018-12-31 High-throughput binding affinity calculations at extreme scales Dakka, Jumana Turilli, Matteo Wright, David W. Zasada, Stefan J. Balasubramanian, Vivek Wan, Shunzhou Coveney, Peter V. Jha, Shantenu BMC Bioinformatics Research BACKGROUND: Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance. RESULTS: We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations. CONCLUSIONS: As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery. BioMed Central 2018-12-21 /pmc/articles/PMC6302294/ /pubmed/30577753 http://dx.doi.org/10.1186/s12859-018-2506-6 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Dakka, Jumana Turilli, Matteo Wright, David W. Zasada, Stefan J. Balasubramanian, Vivek Wan, Shunzhou Coveney, Peter V. Jha, Shantenu High-throughput binding affinity calculations at extreme scales |
title | High-throughput binding affinity calculations at extreme scales |
title_full | High-throughput binding affinity calculations at extreme scales |
title_fullStr | High-throughput binding affinity calculations at extreme scales |
title_full_unstemmed | High-throughput binding affinity calculations at extreme scales |
title_short | High-throughput binding affinity calculations at extreme scales |
title_sort | high-throughput binding affinity calculations at extreme scales |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302294/ https://www.ncbi.nlm.nih.gov/pubmed/30577753 http://dx.doi.org/10.1186/s12859-018-2506-6 |
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