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PredictProtein—an open resource for online prediction of protein structural and functional features

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembra...

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Autores principales: Yachdav, Guy, Kloppmann, Edda, Kajan, Laszlo, Hecht, Maximilian, Goldberg, Tatyana, Hamp, Tobias, Hönigschmid, Peter, Schafferhans, Andrea, Roos, Manfred, Bernhofer, Michael, Richter, Lothar, Ashkenazy, Haim, Punta, Marco, Schlessinger, Avner, Bromberg, Yana, Schneider, Reinhard, Vriend, Gerrit, Sander, Chris, Ben-Tal, Nir, Rost, Burkhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086098/
https://www.ncbi.nlm.nih.gov/pubmed/24799431
http://dx.doi.org/10.1093/nar/gku366
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author Yachdav, Guy
Kloppmann, Edda
Kajan, Laszlo
Hecht, Maximilian
Goldberg, Tatyana
Hamp, Tobias
Hönigschmid, Peter
Schafferhans, Andrea
Roos, Manfred
Bernhofer, Michael
Richter, Lothar
Ashkenazy, Haim
Punta, Marco
Schlessinger, Avner
Bromberg, Yana
Schneider, Reinhard
Vriend, Gerrit
Sander, Chris
Ben-Tal, Nir
Rost, Burkhard
author_facet Yachdav, Guy
Kloppmann, Edda
Kajan, Laszlo
Hecht, Maximilian
Goldberg, Tatyana
Hamp, Tobias
Hönigschmid, Peter
Schafferhans, Andrea
Roos, Manfred
Bernhofer, Michael
Richter, Lothar
Ashkenazy, Haim
Punta, Marco
Schlessinger, Avner
Bromberg, Yana
Schneider, Reinhard
Vriend, Gerrit
Sander, Chris
Ben-Tal, Nir
Rost, Burkhard
author_sort Yachdav, Guy
collection PubMed
description PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.
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spelling pubmed-40860982014-12-02 PredictProtein—an open resource for online prediction of protein structural and functional features Yachdav, Guy Kloppmann, Edda Kajan, Laszlo Hecht, Maximilian Goldberg, Tatyana Hamp, Tobias Hönigschmid, Peter Schafferhans, Andrea Roos, Manfred Bernhofer, Michael Richter, Lothar Ashkenazy, Haim Punta, Marco Schlessinger, Avner Bromberg, Yana Schneider, Reinhard Vriend, Gerrit Sander, Chris Ben-Tal, Nir Rost, Burkhard Nucleic Acids Res Article PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org. Oxford University Press 2014-07-01 2014-05-05 /pmc/articles/PMC4086098/ /pubmed/24799431 http://dx.doi.org/10.1093/nar/gku366 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Yachdav, Guy
Kloppmann, Edda
Kajan, Laszlo
Hecht, Maximilian
Goldberg, Tatyana
Hamp, Tobias
Hönigschmid, Peter
Schafferhans, Andrea
Roos, Manfred
Bernhofer, Michael
Richter, Lothar
Ashkenazy, Haim
Punta, Marco
Schlessinger, Avner
Bromberg, Yana
Schneider, Reinhard
Vriend, Gerrit
Sander, Chris
Ben-Tal, Nir
Rost, Burkhard
PredictProtein—an open resource for online prediction of protein structural and functional features
title PredictProtein—an open resource for online prediction of protein structural and functional features
title_full PredictProtein—an open resource for online prediction of protein structural and functional features
title_fullStr PredictProtein—an open resource for online prediction of protein structural and functional features
title_full_unstemmed PredictProtein—an open resource for online prediction of protein structural and functional features
title_short PredictProtein—an open resource for online prediction of protein structural and functional features
title_sort predictprotein—an open resource for online prediction of protein structural and functional features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086098/
https://www.ncbi.nlm.nih.gov/pubmed/24799431
http://dx.doi.org/10.1093/nar/gku366
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