Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.

The ability to rationally modify targeted physical and biological features of a protein of interest holds promise in numerous academic and industrial applications and paves the way towards de novo protein design. In particular, bioprocesses that utilize the remarkable properties of enzymes would oft...

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Autores principales:	Fabrizio Pucci, Malik Dhanani, Yves Dehouck, Marianne Rooman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1f64d8ea864949c194cad0984d97acf4
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spelling	oai:doaj.org-article:1f64d8ea864949c194cad0984d97acf42021-11-18T08:27:23ZProtein thermostability prediction within homologous families using temperature-dependent statistical potentials.1932-620310.1371/journal.pone.0091659https://doaj.org/article/1f64d8ea864949c194cad0984d97acf42014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24646884/?tool=EBIhttps://doaj.org/toc/1932-6203The ability to rationally modify targeted physical and biological features of a protein of interest holds promise in numerous academic and industrial applications and paves the way towards de novo protein design. In particular, bioprocesses that utilize the remarkable properties of enzymes would often benefit from mutants that remain active at temperatures that are either higher or lower than the physiological temperature, while maintaining the biological activity. Many in silico methods have been developed in recent years for predicting the thermodynamic stability of mutant proteins, but very few have focused on thermostability. To bridge this gap, we developed an algorithm for predicting the best descriptor of thermostability, namely the melting temperature Tm, from the protein's sequence and structure. Our method is applicable when the Tm of proteins homologous to the target protein are known. It is based on the design of several temperature-dependent statistical potentials, derived from datasets consisting of either mesostable or thermostable proteins. Linear combinations of these potentials have been shown to yield an estimation of the protein folding free energies at low and high temperatures, and the difference of these energies, a prediction of the melting temperature. This particular construction, that distinguishes between the interactions that contribute more than others to the stability at high temperatures and those that are more stabilizing at low T, gives better performances compared to the standard approach based on T-independent potentials which predict the thermal resistance from the thermodynamic stability. Our method has been tested on 45 proteins of known Tm that belong to 11 homologous families. The standard deviation between experimental and predicted Tm's is equal to 13.6°C in cross validation, and decreases to 8.3°C if the 6 worst predicted proteins are excluded. Possible extensions of our approach are discussed.Fabrizio PucciMalik DhananiYves DehouckMarianne RoomanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 3, p e91659 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fabrizio Pucci Malik Dhanani Yves Dehouck Marianne Rooman Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.
description	The ability to rationally modify targeted physical and biological features of a protein of interest holds promise in numerous academic and industrial applications and paves the way towards de novo protein design. In particular, bioprocesses that utilize the remarkable properties of enzymes would often benefit from mutants that remain active at temperatures that are either higher or lower than the physiological temperature, while maintaining the biological activity. Many in silico methods have been developed in recent years for predicting the thermodynamic stability of mutant proteins, but very few have focused on thermostability. To bridge this gap, we developed an algorithm for predicting the best descriptor of thermostability, namely the melting temperature Tm, from the protein's sequence and structure. Our method is applicable when the Tm of proteins homologous to the target protein are known. It is based on the design of several temperature-dependent statistical potentials, derived from datasets consisting of either mesostable or thermostable proteins. Linear combinations of these potentials have been shown to yield an estimation of the protein folding free energies at low and high temperatures, and the difference of these energies, a prediction of the melting temperature. This particular construction, that distinguishes between the interactions that contribute more than others to the stability at high temperatures and those that are more stabilizing at low T, gives better performances compared to the standard approach based on T-independent potentials which predict the thermal resistance from the thermodynamic stability. Our method has been tested on 45 proteins of known Tm that belong to 11 homologous families. The standard deviation between experimental and predicted Tm's is equal to 13.6°C in cross validation, and decreases to 8.3°C if the 6 worst predicted proteins are excluded. Possible extensions of our approach are discussed.
format	article
author	Fabrizio Pucci Malik Dhanani Yves Dehouck Marianne Rooman
author_facet	Fabrizio Pucci Malik Dhanani Yves Dehouck Marianne Rooman
author_sort	Fabrizio Pucci
title	Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.
title_short	Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.
title_full	Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.
title_fullStr	Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.
title_full_unstemmed	Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.
title_sort	protein thermostability prediction within homologous families using temperature-dependent statistical potentials.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/1f64d8ea864949c194cad0984d97acf4
work_keys_str_mv	AT fabriziopucci proteinthermostabilitypredictionwithinhomologousfamiliesusingtemperaturedependentstatisticalpotentials AT malikdhanani proteinthermostabilitypredictionwithinhomologousfamiliesusingtemperaturedependentstatisticalpotentials AT yvesdehouck proteinthermostabilitypredictionwithinhomologousfamiliesusingtemperaturedependentstatisticalpotentials AT mariannerooman proteinthermostabilitypredictionwithinhomologousfamiliesusingtemperaturedependentstatisticalpotentials
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Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.

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