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The DINGO dataset: a comprehensive set of data for the SAMPL challenge

Part of the latest SAMPL challenge was to predict how a small fragment library of 500 commercially available compounds would bind to a protein target. In order to assess the modellers’ work, a reasonably comprehensive set of data was collected using a number of techniques. These included surface pla...

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Autores principales: Newman, Janet, Dolezal, Olan, Fazio, Vincent, Caradoc-Davies, Tom, Peat, Thomas S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382646/
https://www.ncbi.nlm.nih.gov/pubmed/22187139
http://dx.doi.org/10.1007/s10822-011-9521-2
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author Newman, Janet
Dolezal, Olan
Fazio, Vincent
Caradoc-Davies, Tom
Peat, Thomas S.
author_facet Newman, Janet
Dolezal, Olan
Fazio, Vincent
Caradoc-Davies, Tom
Peat, Thomas S.
author_sort Newman, Janet
collection PubMed
description Part of the latest SAMPL challenge was to predict how a small fragment library of 500 commercially available compounds would bind to a protein target. In order to assess the modellers’ work, a reasonably comprehensive set of data was collected using a number of techniques. These included surface plasmon resonance, isothermal titration calorimetry, protein crystallization and protein crystallography. Using these techniques we could determine the kinetics of fragment binding, the energy of binding, how this affects the ability of the target to crystallize, and when the fragment did bind, the pose or orientation of binding. Both the final data set and all of the raw images have been made available to the community for scrutiny and further work. This overview sets out to give the parameters of the experiments done and what might be done differently for future studies.
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spelling pubmed-33826462012-07-05 The DINGO dataset: a comprehensive set of data for the SAMPL challenge Newman, Janet Dolezal, Olan Fazio, Vincent Caradoc-Davies, Tom Peat, Thomas S. J Comput Aided Mol Des Article Part of the latest SAMPL challenge was to predict how a small fragment library of 500 commercially available compounds would bind to a protein target. In order to assess the modellers’ work, a reasonably comprehensive set of data was collected using a number of techniques. These included surface plasmon resonance, isothermal titration calorimetry, protein crystallization and protein crystallography. Using these techniques we could determine the kinetics of fragment binding, the energy of binding, how this affects the ability of the target to crystallize, and when the fragment did bind, the pose or orientation of binding. Both the final data set and all of the raw images have been made available to the community for scrutiny and further work. This overview sets out to give the parameters of the experiments done and what might be done differently for future studies. Springer Netherlands 2011-12-21 2012 /pmc/articles/PMC3382646/ /pubmed/22187139 http://dx.doi.org/10.1007/s10822-011-9521-2 Text en © The Author(s) 2011 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Article
Newman, Janet
Dolezal, Olan
Fazio, Vincent
Caradoc-Davies, Tom
Peat, Thomas S.
The DINGO dataset: a comprehensive set of data for the SAMPL challenge
title The DINGO dataset: a comprehensive set of data for the SAMPL challenge
title_full The DINGO dataset: a comprehensive set of data for the SAMPL challenge
title_fullStr The DINGO dataset: a comprehensive set of data for the SAMPL challenge
title_full_unstemmed The DINGO dataset: a comprehensive set of data for the SAMPL challenge
title_short The DINGO dataset: a comprehensive set of data for the SAMPL challenge
title_sort dingo dataset: a comprehensive set of data for the sampl challenge
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382646/
https://www.ncbi.nlm.nih.gov/pubmed/22187139
http://dx.doi.org/10.1007/s10822-011-9521-2
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