Cargando…

DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins

The natively unfolded nature of intrinsically disordered proteins (IDPs) relies on several physicochemical principles, of which the balance between a low sequence hydrophobicity and a high net charge appears to be critical. Under this premise, it is well-known that disordered proteins populate a def...

Descripción completa

Detalles Bibliográficos
Autores principales: Santos, Jaime, Iglesias, Valentín, Pintado, Carlos, Santos-Suárez, Juan, Ventura, Salvador
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461198/
https://www.ncbi.nlm.nih.gov/pubmed/32823616
http://dx.doi.org/10.3390/ijms21165814
_version_ 1783576727379771392
author Santos, Jaime
Iglesias, Valentín
Pintado, Carlos
Santos-Suárez, Juan
Ventura, Salvador
author_facet Santos, Jaime
Iglesias, Valentín
Pintado, Carlos
Santos-Suárez, Juan
Ventura, Salvador
author_sort Santos, Jaime
collection PubMed
description The natively unfolded nature of intrinsically disordered proteins (IDPs) relies on several physicochemical principles, of which the balance between a low sequence hydrophobicity and a high net charge appears to be critical. Under this premise, it is well-known that disordered proteins populate a defined region of the charge–hydropathy (C–H) space and that a linear boundary condition is sufficient to distinguish between folded and disordered proteins, an approach widely applied for the prediction of protein disorder. Nevertheless, it is evident that the C–H relation of a protein is not unalterable but can be modulated by factors extrinsic to its sequence. Here, we applied a C–H-based analysis to develop a computational approach that evaluates sequence disorder as a function of pH, assuming that both protein net charge and hydrophobicity are dependent on pH solution. On that basis, we developed DispHred, the first pH-dependent predictor of protein disorder. Despite its simplicity, DispHred displays very high accuracy in identifying pH-induced order/disorder protein transitions. DispHred might be useful for diverse applications, from the analysis of conditionally disordered segments to the synthetic design of disorder tags for biotechnological applications. Importantly, since many disorder predictors use hydrophobicity as an input, the here developed framework can be implemented in other state-of-the-art algorithms.
format Online
Article
Text
id pubmed-7461198
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-74611982020-09-14 DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins Santos, Jaime Iglesias, Valentín Pintado, Carlos Santos-Suárez, Juan Ventura, Salvador Int J Mol Sci Article The natively unfolded nature of intrinsically disordered proteins (IDPs) relies on several physicochemical principles, of which the balance between a low sequence hydrophobicity and a high net charge appears to be critical. Under this premise, it is well-known that disordered proteins populate a defined region of the charge–hydropathy (C–H) space and that a linear boundary condition is sufficient to distinguish between folded and disordered proteins, an approach widely applied for the prediction of protein disorder. Nevertheless, it is evident that the C–H relation of a protein is not unalterable but can be modulated by factors extrinsic to its sequence. Here, we applied a C–H-based analysis to develop a computational approach that evaluates sequence disorder as a function of pH, assuming that both protein net charge and hydrophobicity are dependent on pH solution. On that basis, we developed DispHred, the first pH-dependent predictor of protein disorder. Despite its simplicity, DispHred displays very high accuracy in identifying pH-induced order/disorder protein transitions. DispHred might be useful for diverse applications, from the analysis of conditionally disordered segments to the synthetic design of disorder tags for biotechnological applications. Importantly, since many disorder predictors use hydrophobicity as an input, the here developed framework can be implemented in other state-of-the-art algorithms. MDPI 2020-08-13 /pmc/articles/PMC7461198/ /pubmed/32823616 http://dx.doi.org/10.3390/ijms21165814 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Santos, Jaime
Iglesias, Valentín
Pintado, Carlos
Santos-Suárez, Juan
Ventura, Salvador
DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins
title DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins
title_full DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins
title_fullStr DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins
title_full_unstemmed DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins
title_short DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins
title_sort disphred: a server to predict ph-dependent order–disorder transitions in intrinsically disordered proteins
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7461198/
https://www.ncbi.nlm.nih.gov/pubmed/32823616
http://dx.doi.org/10.3390/ijms21165814
work_keys_str_mv AT santosjaime disphredaservertopredictphdependentorderdisordertransitionsinintrinsicallydisorderedproteins
AT iglesiasvalentin disphredaservertopredictphdependentorderdisordertransitionsinintrinsicallydisorderedproteins
AT pintadocarlos disphredaservertopredictphdependentorderdisordertransitionsinintrinsicallydisorderedproteins
AT santossuarezjuan disphredaservertopredictphdependentorderdisordertransitionsinintrinsicallydisorderedproteins
AT venturasalvador disphredaservertopredictphdependentorderdisordertransitionsinintrinsicallydisorderedproteins