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Performance of Web tools for predicting changes in protein stability caused by mutations

BACKGROUND: Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the fie...

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Autores principales: Marabotti, Anna, Del Prete, Eugenio, Scafuri, Bernardina, Facchiano, Angelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8256537/
https://www.ncbi.nlm.nih.gov/pubmed/34225665
http://dx.doi.org/10.1186/s12859-021-04238-w
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author Marabotti, Anna
Del Prete, Eugenio
Scafuri, Bernardina
Facchiano, Angelo
author_facet Marabotti, Anna
Del Prete, Eugenio
Scafuri, Bernardina
Facchiano, Angelo
author_sort Marabotti, Anna
collection PubMed
description BACKGROUND: Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. RESULTS: The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. CONCLUSIONS: We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04238-w.
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spelling pubmed-82565372021-07-06 Performance of Web tools for predicting changes in protein stability caused by mutations Marabotti, Anna Del Prete, Eugenio Scafuri, Bernardina Facchiano, Angelo BMC Bioinformatics Research BACKGROUND: Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. RESULTS: The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. CONCLUSIONS: We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04238-w. BioMed Central 2021-07-05 /pmc/articles/PMC8256537/ /pubmed/34225665 http://dx.doi.org/10.1186/s12859-021-04238-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Marabotti, Anna
Del Prete, Eugenio
Scafuri, Bernardina
Facchiano, Angelo
Performance of Web tools for predicting changes in protein stability caused by mutations
title Performance of Web tools for predicting changes in protein stability caused by mutations
title_full Performance of Web tools for predicting changes in protein stability caused by mutations
title_fullStr Performance of Web tools for predicting changes in protein stability caused by mutations
title_full_unstemmed Performance of Web tools for predicting changes in protein stability caused by mutations
title_short Performance of Web tools for predicting changes in protein stability caused by mutations
title_sort performance of web tools for predicting changes in protein stability caused by mutations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8256537/
https://www.ncbi.nlm.nih.gov/pubmed/34225665
http://dx.doi.org/10.1186/s12859-021-04238-w
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