CPSP-web-tools: a server for 3D lattice protein studies

Summary: Studies on proteins are often restricted to highly simplified models to face the immense computational complexity of the associated problems. Constraint-based protein structure prediction (CPSP) tools is a package of very fast algorithms for ab initio optimal structure prediction and relate...

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Detalles Bibliográficos
Autores principales: Mann, Martin, Smith, Cameron, Rabbath, Mohamad, Edwards, Marlien, Will, Sebastian, Backofen, Rolf
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2647832/
https://www.ncbi.nlm.nih.gov/pubmed/19151096
http://dx.doi.org/10.1093/bioinformatics/btp034
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author Mann, Martin
Smith, Cameron
Rabbath, Mohamad
Edwards, Marlien
Will, Sebastian
Backofen, Rolf
author_facet Mann, Martin
Smith, Cameron
Rabbath, Mohamad
Edwards, Marlien
Will, Sebastian
Backofen, Rolf
author_sort Mann, Martin
collection PubMed
description Summary: Studies on proteins are often restricted to highly simplified models to face the immense computational complexity of the associated problems. Constraint-based protein structure prediction (CPSP) tools is a package of very fast algorithms for ab initio optimal structure prediction and related problems in 3D HP-models [cubic and face centered cubic (FCC)]. Here, we present CPSP-web-tools, an interactive online interface of these programs for their immediate use. They include the first method for the direct prediction of optimal energies and structures in 3D HP side-chain models. This newest extension of the CPSP approach is described here for the first time. Availability and Implementation: Free access at http://cpsp.informatik.uni-freiburg.de Contact: cpsp@informatik.uni-freiburg.de; cpsp@informatik.uni-freiburg.de
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spelling pubmed-26478322009-04-02 CPSP-web-tools: a server for 3D lattice protein studies Mann, Martin Smith, Cameron Rabbath, Mohamad Edwards, Marlien Will, Sebastian Backofen, Rolf Bioinformatics Applications Note Summary: Studies on proteins are often restricted to highly simplified models to face the immense computational complexity of the associated problems. Constraint-based protein structure prediction (CPSP) tools is a package of very fast algorithms for ab initio optimal structure prediction and related problems in 3D HP-models [cubic and face centered cubic (FCC)]. Here, we present CPSP-web-tools, an interactive online interface of these programs for their immediate use. They include the first method for the direct prediction of optimal energies and structures in 3D HP side-chain models. This newest extension of the CPSP approach is described here for the first time. Availability and Implementation: Free access at http://cpsp.informatik.uni-freiburg.de Contact: cpsp@informatik.uni-freiburg.de; cpsp@informatik.uni-freiburg.de Oxford University Press 2009-03-01 2009-01-16 /pmc/articles/PMC2647832/ /pubmed/19151096 http://dx.doi.org/10.1093/bioinformatics/btp034 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Mann, Martin
Smith, Cameron
Rabbath, Mohamad
Edwards, Marlien
Will, Sebastian
Backofen, Rolf
CPSP-web-tools: a server for 3D lattice protein studies
title CPSP-web-tools: a server for 3D lattice protein studies
title_full CPSP-web-tools: a server for 3D lattice protein studies
title_fullStr CPSP-web-tools: a server for 3D lattice protein studies
title_full_unstemmed CPSP-web-tools: a server for 3D lattice protein studies
title_short CPSP-web-tools: a server for 3D lattice protein studies
title_sort cpsp-web-tools: a server for 3d lattice protein studies
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2647832/
https://www.ncbi.nlm.nih.gov/pubmed/19151096
http://dx.doi.org/10.1093/bioinformatics/btp034
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