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b2bTools: online predictions for protein biophysical features and their conservation

We provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of our predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or...

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Detalles Bibliográficos
Autores principales: Kagami, Luciano Porto, Orlando, Gabriele, Raimondi, Daniele, Ancien, Francois, Dixit, Bhawna, Gavaldá-García, Jose, Ramasamy, Pathmanaban, Roca-Martínez, Joel, Tzavella, Konstantina, Vranken, Wim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262692/
https://www.ncbi.nlm.nih.gov/pubmed/34057475
http://dx.doi.org/10.1093/nar/gkab425
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author Kagami, Luciano Porto
Orlando, Gabriele
Raimondi, Daniele
Ancien, Francois
Dixit, Bhawna
Gavaldá-García, Jose
Ramasamy, Pathmanaban
Roca-Martínez, Joel
Tzavella, Konstantina
Vranken, Wim
author_facet Kagami, Luciano Porto
Orlando, Gabriele
Raimondi, Daniele
Ancien, Francois
Dixit, Bhawna
Gavaldá-García, Jose
Ramasamy, Pathmanaban
Roca-Martínez, Joel
Tzavella, Konstantina
Vranken, Wim
author_sort Kagami, Luciano Porto
collection PubMed
description We provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of our predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or text input of a sequence provides integrated predictions from DynaMine backbone and side-chain dynamics, conformational propensities, and derived EFoldMine early folding, DisoMine disorder, and Agmata β-sheet aggregation. These predictions, several of which were previously not available online, capture ‘emergent’ properties of proteins, i.e. the inherent biophysical propensities encoded in their sequence, rather than context-dependent behaviour (e.g. final folded state). In addition, upload of a multiple sequence alignment (MSA) in a variety of formats enables exploration of the biophysical variation observed in homologous proteins. The associated plots indicate the biophysical limits of functionally relevant protein behaviour, with unusual residues flagged by a Gaussian mixture model analysis. The prediction results are available as JSON or CSV files and directly accessible via an API. Online visualisation is available as interactive plots, with brief explanations and tutorial pages included. The server and API employ an email-free token-based system that can be used to anonymously access previously generated results.
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spelling pubmed-82626922021-07-08 b2bTools: online predictions for protein biophysical features and their conservation Kagami, Luciano Porto Orlando, Gabriele Raimondi, Daniele Ancien, Francois Dixit, Bhawna Gavaldá-García, Jose Ramasamy, Pathmanaban Roca-Martínez, Joel Tzavella, Konstantina Vranken, Wim Nucleic Acids Res Web Server Issue We provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of our predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or text input of a sequence provides integrated predictions from DynaMine backbone and side-chain dynamics, conformational propensities, and derived EFoldMine early folding, DisoMine disorder, and Agmata β-sheet aggregation. These predictions, several of which were previously not available online, capture ‘emergent’ properties of proteins, i.e. the inherent biophysical propensities encoded in their sequence, rather than context-dependent behaviour (e.g. final folded state). In addition, upload of a multiple sequence alignment (MSA) in a variety of formats enables exploration of the biophysical variation observed in homologous proteins. The associated plots indicate the biophysical limits of functionally relevant protein behaviour, with unusual residues flagged by a Gaussian mixture model analysis. The prediction results are available as JSON or CSV files and directly accessible via an API. Online visualisation is available as interactive plots, with brief explanations and tutorial pages included. The server and API employ an email-free token-based system that can be used to anonymously access previously generated results. Oxford University Press 2021-05-31 /pmc/articles/PMC8262692/ /pubmed/34057475 http://dx.doi.org/10.1093/nar/gkab425 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Kagami, Luciano Porto
Orlando, Gabriele
Raimondi, Daniele
Ancien, Francois
Dixit, Bhawna
Gavaldá-García, Jose
Ramasamy, Pathmanaban
Roca-Martínez, Joel
Tzavella, Konstantina
Vranken, Wim
b2bTools: online predictions for protein biophysical features and their conservation
title b2bTools: online predictions for protein biophysical features and their conservation
title_full b2bTools: online predictions for protein biophysical features and their conservation
title_fullStr b2bTools: online predictions for protein biophysical features and their conservation
title_full_unstemmed b2bTools: online predictions for protein biophysical features and their conservation
title_short b2bTools: online predictions for protein biophysical features and their conservation
title_sort b2btools: online predictions for protein biophysical features and their conservation
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262692/
https://www.ncbi.nlm.nih.gov/pubmed/34057475
http://dx.doi.org/10.1093/nar/gkab425
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