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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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S669

Authors: Pancotti, Corrado; Benevenuta, Silvia; Birolo, Giovanni; Alberini, Virginia; Repetto Valeria; Sanavia, Tiziana; Capriotti, Emidio; +1 Authors
Abstract

A dataset of protein variants with annotated with their experimental DDG values, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. It includes the protein structures (both wild-type and mutated) for the variants. Please refer to the original publication: Corrado Pancotti, Silvia Benevenuta, Giovanni Birolo, Virginia Alberini, Valeria Repetto, Tiziana Sanavia, Emidio Capriotti, Piero Fariselli, Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset, Briefings in Bioinformatics, Volume 23, Issue 2, March 2022, bbab555, https://doi.org/10.1093/bib/bbab555

{"references": ["Corrado Pancotti, Silvia Benevenuta, Giovanni Birolo, Virginia Alberini, Valeria Repetto, Tiziana Sanavia, Emidio Capriotti, Piero Fariselli, Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset,\u00a0Briefings in Bioinformatics, Volume 23, Issue 2, March 2022, bbab555,\u00a0https://doi.org/10.1093/bib/bbab555"]}

Keywords

protein stability, single-point mutation, stability change, antisymmetry, machine learning

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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