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PredictProtein--an open resource for online prediction of protein structural and functional features.
- Source :
-
Nucleic acids research [Nucleic Acids Res] 2014 Jul; Vol. 42 (Web Server issue), pp. W337-43. Date of Electronic Publication: 2014 May 05. - 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.<br /> (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)
- Subjects :
- Amino Acid Substitution
Binding Sites
Gene Ontology
Internet
Intrinsically Disordered Proteins chemistry
Membrane Proteins chemistry
Mutation
Protein Interaction Mapping
Proteins analysis
Proteins genetics
Proteins metabolism
Sequence Alignment
Sequence Analysis, Protein
Sequence Homology, Amino Acid
Protein Conformation
Software
Subjects
Details
- Language :
- English
- ISSN :
- 1362-4962
- Volume :
- 42
- Issue :
- Web Server issue
- Database :
- MEDLINE
- Journal :
- Nucleic acids research
- Publication Type :
- Academic Journal
- Accession number :
- 24799431
- Full Text :
- https://doi.org/10.1093/nar/gku366