Machine learning: how much does it tell about protein folding rates?
The prediction of protein folding rates is a necessary step towards understanding the principles of protein folding. Due to the increasing amount of experimental data, numerous protein folding models and predictors of protein folding rates have been developed in the last decade. The problem has also...
| Autores: | , , , , , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/58480 |
| Acesso em linha: | http://hdl.handle.net/10230/58480 http://dx.doi.org/10.1371/journal.pone.0143166 |
| Access Level: | acceso abierto |
| Palavra-chave: | Aprenentatge automàtic Logaritmes Proteïnes Estadística |
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Machine learning: how much does it tell about protein folding rates?Corrales, MarcCuscó, PolUsmanova, Dinara R.Chen, Heng-ChangBogatyreva, Natalya S.Filion, GuillaumeIvankov, Dmitry N.Aprenentatge automàticLogaritmesProteïnesEstadísticaThe prediction of protein folding rates is a necessary step towards understanding the principles of protein folding. Due to the increasing amount of experimental data, numerous protein folding models and predictors of protein folding rates have been developed in the last decade. The problem has also attracted the attention of scientists from computational fields, which led to the publication of several machine learning-based models to predict the rate of protein folding. Some of them claim to predict the logarithm of protein folding rate with an accuracy greater than 90%. However, there are reasons to believe that such claims are exaggerated due to large fluctuations and overfitting of the estimates. When we confronted three selected published models with new data, we found a much lower predictive power than reported in the original publications. Overly optimistic predictive powers appear from violations of the basic principles of machine-learning. We highlight common misconceptions in the studies claiming excessive predictive power and propose to use learning curves as a safeguard against those mistakes. As an example, we show that the current amount of experimental data is insufficient to build a linear predictor of logarithms of folding rates based on protein amino acid composition.NSB was supported by the Russian Science Foundation Grant 14-24-00157. DRU and DNI were supported by ERC grant 335980_EinME. The authors acknowledge support of the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013-2017’, SEV-2012-0208. HCC and PC were supported by the Spanish Ministry of Economy and Competitiveness (including State Training Subprogram: predoctoral fellowships for the training of PhD students (FPI) 2013). MC and GF were supported by the CRG. The publication cost was covered by CRG.Public Library of Science (PLoS)202320232015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/58480http://dx.doi.org/10.1371/journal.pone.0143166reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésPLoS ONE. 2015 Nov 25;10(11):e0143166info:eu-repo/grantAgreement/EC/FP7/335980© 2015 Corrales et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/584802026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
Machine learning: how much does it tell about protein folding rates? |
| title |
Machine learning: how much does it tell about protein folding rates? |
| spellingShingle |
Machine learning: how much does it tell about protein folding rates? Corrales, Marc Aprenentatge automàtic Logaritmes Proteïnes Estadística |
| title_short |
Machine learning: how much does it tell about protein folding rates? |
| title_full |
Machine learning: how much does it tell about protein folding rates? |
| title_fullStr |
Machine learning: how much does it tell about protein folding rates? |
| title_full_unstemmed |
Machine learning: how much does it tell about protein folding rates? |
| title_sort |
Machine learning: how much does it tell about protein folding rates? |
| dc.creator.none.fl_str_mv |
Corrales, Marc Cuscó, Pol Usmanova, Dinara R. Chen, Heng-Chang Bogatyreva, Natalya S. Filion, Guillaume Ivankov, Dmitry N. |
| author |
Corrales, Marc |
| author_facet |
Corrales, Marc Cuscó, Pol Usmanova, Dinara R. Chen, Heng-Chang Bogatyreva, Natalya S. Filion, Guillaume Ivankov, Dmitry N. |
| author_role |
author |
| author2 |
Cuscó, Pol Usmanova, Dinara R. Chen, Heng-Chang Bogatyreva, Natalya S. Filion, Guillaume Ivankov, Dmitry N. |
| author2_role |
author author author author author author |
| dc.subject.none.fl_str_mv |
Aprenentatge automàtic Logaritmes Proteïnes Estadística |
| topic |
Aprenentatge automàtic Logaritmes Proteïnes Estadística |
| description |
The prediction of protein folding rates is a necessary step towards understanding the principles of protein folding. Due to the increasing amount of experimental data, numerous protein folding models and predictors of protein folding rates have been developed in the last decade. The problem has also attracted the attention of scientists from computational fields, which led to the publication of several machine learning-based models to predict the rate of protein folding. Some of them claim to predict the logarithm of protein folding rate with an accuracy greater than 90%. However, there are reasons to believe that such claims are exaggerated due to large fluctuations and overfitting of the estimates. When we confronted three selected published models with new data, we found a much lower predictive power than reported in the original publications. Overly optimistic predictive powers appear from violations of the basic principles of machine-learning. We highlight common misconceptions in the studies claiming excessive predictive power and propose to use learning curves as a safeguard against those mistakes. As an example, we show that the current amount of experimental data is insufficient to build a linear predictor of logarithms of folding rates based on protein amino acid composition. |
| publishDate |
2015 |
| dc.date.none.fl_str_mv |
2015 2023 2023 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10230/58480 http://dx.doi.org/10.1371/journal.pone.0143166 |
| url |
http://hdl.handle.net/10230/58480 http://dx.doi.org/10.1371/journal.pone.0143166 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
PLoS ONE. 2015 Nov 25;10(11):e0143166 info:eu-repo/grantAgreement/EC/FP7/335980 |
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http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf application/pdf |
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Public Library of Science (PLoS) |
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Public Library of Science (PLoS) |
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reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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