Back to Search Start Over

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

Authors :
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center]
Yachdav, Guy
Kloppman
Kajan
Hecht
Goldberg
Hamp
Honigschmid
Schafferhans
Roos, Manfred
Bernhofer
Richter
Ashkenazy
Punta
Schlessinger
Bromberg
Schneider, Reinhard
Vriend
Sander
Ben-Tal
Rost
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center]
Yachdav, Guy
Kloppman
Kajan
Hecht
Goldberg
Hamp
Honigschmid
Schafferhans
Roos, Manfred
Bernhofer
Richter
Ashkenazy
Punta
Schlessinger
Bromberg
Schneider, Reinhard
Vriend
Sander
Ben-Tal
Rost
Publication Year :
2014

Abstract

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.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1134802983
Document Type :
Electronic Resource