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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...

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Autores principales: Dakka, Jumana, Turilli, Matteo, Wright, David W., Zasada, Stefan J., Balasubramanian, Vivek, Wan, Shunzhou, Coveney, Peter V., Jha, Shantenu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
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.
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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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