Cargando…
PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate
Most proteins fold into characteristic three-dimensional structures. The rate of folding and unfolding varies widely and can be affected by variations in proteins. We developed a novel machine-learning-based method for the prediction of the folding rate effects of amino acid substitutions in two-sta...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455311/ https://www.ncbi.nlm.nih.gov/pubmed/37629203 http://dx.doi.org/10.3390/ijms241613023 |
_version_ | 1785096421800673280 |
---|---|
author | Yang, Yang Chong, Zhang Vihinen, Mauno |
author_facet | Yang, Yang Chong, Zhang Vihinen, Mauno |
author_sort | Yang, Yang |
collection | PubMed |
description | Most proteins fold into characteristic three-dimensional structures. The rate of folding and unfolding varies widely and can be affected by variations in proteins. We developed a novel machine-learning-based method for the prediction of the folding rate effects of amino acid substitutions in two-state folding proteins. We collected a data set of experimentally defined folding rates for variants and used them to train a gradient boosting algorithm starting with 1161 features. Two predictors were designed. The three-class classifier had, in blind tests, specificity and sensitivity ranging from 0.324 to 0.419 and from 0.256 to 0.451, respectively. The other tool was a regression predictor that showed a Pearson correlation coefficient of 0.525. The error measures, mean absolute error and mean squared error, were 0.581 and 0.603, respectively. One of the previously presented tools could be used for comparison with the blind test data set, our method called PON-Fold showed superior performance on all used measures. The applicability of the tool was tested by predicting all possible substitutions in a protein domain. Predictions for different conformations of proteins, open and closed forms of a protein kinase, and apo and holo forms of an enzyme indicated that the choice of the structure had a large impact on the outcome. PON-Fold is freely available. |
format | Online Article Text |
id | pubmed-10455311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104553112023-08-26 PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate Yang, Yang Chong, Zhang Vihinen, Mauno Int J Mol Sci Article Most proteins fold into characteristic three-dimensional structures. The rate of folding and unfolding varies widely and can be affected by variations in proteins. We developed a novel machine-learning-based method for the prediction of the folding rate effects of amino acid substitutions in two-state folding proteins. We collected a data set of experimentally defined folding rates for variants and used them to train a gradient boosting algorithm starting with 1161 features. Two predictors were designed. The three-class classifier had, in blind tests, specificity and sensitivity ranging from 0.324 to 0.419 and from 0.256 to 0.451, respectively. The other tool was a regression predictor that showed a Pearson correlation coefficient of 0.525. The error measures, mean absolute error and mean squared error, were 0.581 and 0.603, respectively. One of the previously presented tools could be used for comparison with the blind test data set, our method called PON-Fold showed superior performance on all used measures. The applicability of the tool was tested by predicting all possible substitutions in a protein domain. Predictions for different conformations of proteins, open and closed forms of a protein kinase, and apo and holo forms of an enzyme indicated that the choice of the structure had a large impact on the outcome. PON-Fold is freely available. MDPI 2023-08-21 /pmc/articles/PMC10455311/ /pubmed/37629203 http://dx.doi.org/10.3390/ijms241613023 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Yang Chong, Zhang Vihinen, Mauno PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate |
title | PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate |
title_full | PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate |
title_fullStr | PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate |
title_full_unstemmed | PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate |
title_short | PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate |
title_sort | pon-fold: prediction of substitutions affecting protein folding rate |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455311/ https://www.ncbi.nlm.nih.gov/pubmed/37629203 http://dx.doi.org/10.3390/ijms241613023 |
work_keys_str_mv | AT yangyang ponfoldpredictionofsubstitutionsaffectingproteinfoldingrate AT chongzhang ponfoldpredictionofsubstitutionsaffectingproteinfoldingrate AT vihinenmauno ponfoldpredictionofsubstitutionsaffectingproteinfoldingrate |