Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins

Abstract Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present E...

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Autores principales:	Daniele Raimondi, Gabriele Orlando, Rita Pancsa, Taushif Khan, Wim F. Vranken
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f11ec48e3bdc45e38bd7fe3ddc31b01f
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spelling	oai:doaj.org-article:f11ec48e3bdc45e38bd7fe3ddc31b01f2021-12-02T11:53:10ZExploring the Sequence-based Prediction of Folding Initiation Sites in Proteins10.1038/s41598-017-08366-32045-2322https://doaj.org/article/f11ec48e3bdc45e38bd7fe3ddc31b01f2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08366-3https://doaj.org/toc/2045-2322Abstract Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present EFoldMine, a method that predicts, from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. The method is based on early folding data from hydrogen deuterium exchange (HDX) data from NMR pulsed labelling experiments, and uses backbone and sidechain dynamics as well as secondary structure propensities as features. The EFoldMine predictions give insights into the folding process, as illustrated by a qualitative comparison with independent experimental observations. Furthermore, on a quantitative proteome scale, the predicted early folding residues tend to become the residues that interact the most in the folded structure, and they are often residues that display evolutionary covariation. The connection of the EFoldMine predictions with both folding pathway data and the folded protein structure suggests that the initial statistical behavior of the protein chain with respect to local structure formation has a lasting effect on its subsequent states.Daniele RaimondiGabriele OrlandoRita PancsaTaushif KhanWim F. VrankenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniele Raimondi Gabriele Orlando Rita Pancsa Taushif Khan Wim F. Vranken Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins
description	Abstract Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present EFoldMine, a method that predicts, from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. The method is based on early folding data from hydrogen deuterium exchange (HDX) data from NMR pulsed labelling experiments, and uses backbone and sidechain dynamics as well as secondary structure propensities as features. The EFoldMine predictions give insights into the folding process, as illustrated by a qualitative comparison with independent experimental observations. Furthermore, on a quantitative proteome scale, the predicted early folding residues tend to become the residues that interact the most in the folded structure, and they are often residues that display evolutionary covariation. The connection of the EFoldMine predictions with both folding pathway data and the folded protein structure suggests that the initial statistical behavior of the protein chain with respect to local structure formation has a lasting effect on its subsequent states.
format	article
author	Daniele Raimondi Gabriele Orlando Rita Pancsa Taushif Khan Wim F. Vranken
author_facet	Daniele Raimondi Gabriele Orlando Rita Pancsa Taushif Khan Wim F. Vranken
author_sort	Daniele Raimondi
title	Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins
title_short	Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins
title_full	Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins
title_fullStr	Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins
title_full_unstemmed	Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins
title_sort	exploring the sequence-based prediction of folding initiation sites in proteins
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f11ec48e3bdc45e38bd7fe3ddc31b01f
work_keys_str_mv	AT danieleraimondi exploringthesequencebasedpredictionoffoldinginitiationsitesinproteins AT gabrieleorlando exploringthesequencebasedpredictionoffoldinginitiationsitesinproteins AT ritapancsa exploringthesequencebasedpredictionoffoldinginitiationsitesinproteins AT taushifkhan exploringthesequencebasedpredictionoffoldinginitiationsitesinproteins AT wimfvranken exploringthesequencebasedpredictionoffoldinginitiationsitesinproteins
_version_	1718394901544042496

Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins

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