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DDGun: an untrained predictor of protein stability changes upon amino acid variants

Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server o...

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Autores principales: Montanucci, Ludovica, Capriotti, Emidio, Birolo, Giovanni, Benevenuta, Silvia, Pancotti, Corrado, Lal, Dennis, Fariselli, Piero
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252764/
https://www.ncbi.nlm.nih.gov/pubmed/35524565
http://dx.doi.org/10.1093/nar/gkac325
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author Montanucci, Ludovica
Capriotti, Emidio
Birolo, Giovanni
Benevenuta, Silvia
Pancotti, Corrado
Lal, Dennis
Fariselli, Piero
author_facet Montanucci, Ludovica
Capriotti, Emidio
Birolo, Giovanni
Benevenuta, Silvia
Pancotti, Corrado
Lal, Dennis
Fariselli, Piero
author_sort Montanucci, Ludovica
collection PubMed
description Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔG prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔG values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔG of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔG, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔG prediction.
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spelling pubmed-92527642022-07-05 DDGun: an untrained predictor of protein stability changes upon amino acid variants Montanucci, Ludovica Capriotti, Emidio Birolo, Giovanni Benevenuta, Silvia Pancotti, Corrado Lal, Dennis Fariselli, Piero Nucleic Acids Res Web Server Issue Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔG prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔG values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔG of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔG, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔG prediction. Oxford University Press 2022-05-07 /pmc/articles/PMC9252764/ /pubmed/35524565 http://dx.doi.org/10.1093/nar/gkac325 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server Issue
Montanucci, Ludovica
Capriotti, Emidio
Birolo, Giovanni
Benevenuta, Silvia
Pancotti, Corrado
Lal, Dennis
Fariselli, Piero
DDGun: an untrained predictor of protein stability changes upon amino acid variants
title DDGun: an untrained predictor of protein stability changes upon amino acid variants
title_full DDGun: an untrained predictor of protein stability changes upon amino acid variants
title_fullStr DDGun: an untrained predictor of protein stability changes upon amino acid variants
title_full_unstemmed DDGun: an untrained predictor of protein stability changes upon amino acid variants
title_short DDGun: an untrained predictor of protein stability changes upon amino acid variants
title_sort ddgun: an untrained predictor of protein stability changes upon amino acid variants
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252764/
https://www.ncbi.nlm.nih.gov/pubmed/35524565
http://dx.doi.org/10.1093/nar/gkac325
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