Pcons.net: protein structure prediction meta server
The Pcons.net Meta Server (http://pcons.net) provides improved automated tools for protein structure prediction and analysis using consensus. It essentially implements all the steps necessary to produce a high quality model of a protein. The whole process is fully automated and a potential user only...
| Main Authors: | , , |
|---|---|
| Format: | Online |
| Language: | English |
| Published: |
Oxford University Press
2007
|
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1933226/ |
| id |
pubmed-1933226 |
|---|---|
| recordtype |
oai_dc |
| spelling |
pubmed-19332262007-07-31 Pcons.net: protein structure prediction meta server Wallner, Björn Larsson, Per Elofsson, Arne Articles The Pcons.net Meta Server (http://pcons.net) provides improved automated tools for protein structure prediction and analysis using consensus. It essentially implements all the steps necessary to produce a high quality model of a protein. The whole process is fully automated and a potential user only submits the protein sequence. For PSI-BLAST detectable targets, an accurate model is generated within minutes of submission. For more difficult targets the sequence is automatically submitted to publicly available fold-recognition servers that use more advanced approaches to find distant structural homologs. The results from these servers are analyzed and assessed for structural correctness using Pcons and ProQ; and the user is presented with a ranked list of possible models. In addition, if the protein sequence contains more than one domain, these are automatically parsed out and resubmitted to the server as individual queries. Oxford University Press 2007-07 2007-06-21 /pmc/articles/PMC1933226/ /pubmed/17584798 http://dx.doi.org/10.1093/nar/gkm319 Text en © 2007 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. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Wallner, Björn Larsson, Per Elofsson, Arne |
| spellingShingle |
Wallner, Björn Larsson, Per Elofsson, Arne Pcons.net: protein structure prediction meta server |
| author_facet |
Wallner, Björn Larsson, Per Elofsson, Arne |
| author_sort |
Wallner, Björn |
| title |
Pcons.net: protein structure prediction meta server |
| title_short |
Pcons.net: protein structure prediction meta server |
| title_full |
Pcons.net: protein structure prediction meta server |
| title_fullStr |
Pcons.net: protein structure prediction meta server |
| title_full_unstemmed |
Pcons.net: protein structure prediction meta server |
| title_sort |
pcons.net: protein structure prediction meta server |
| description |
The Pcons.net Meta Server (http://pcons.net) provides improved automated tools for protein structure prediction and analysis using consensus. It essentially implements all the steps necessary to produce a high quality model of a protein. The whole process is fully automated and a potential user only submits the protein sequence. For PSI-BLAST detectable targets, an accurate model is generated within minutes of submission. For more difficult targets the sequence is automatically submitted to publicly available fold-recognition servers that use more advanced approaches to find distant structural homologs. The results from these servers are analyzed and assessed for structural correctness using Pcons and ProQ; and the user is presented with a ranked list of possible models. In addition, if the protein sequence contains more than one domain, these are automatically parsed out and resubmitted to the server as individual queries. |
| publisher |
Oxford University Press |
| publishDate |
2007 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1933226/ |
| _version_ |
1611398605151666176 |