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

Guy Yachdav(Technical University of Munich), Edda Kloppmann(Technical University of Munich), László Kaján(Technical University of Munich), Maximilian Hecht(Technical University of Munich), Tatyana Goldberg(Technical University of Munich), Tobias Hamp(Technical University of Munich), Peter Hönigschmid(Weihenstephan-Triesdorf University of Applied Sciences), Andrea Schafferhans(Technical University of Munich), Manfred Roos(Technical University of Munich), Michael Bernhofer(Technical University of Munich), Lothar Richter(Technical University of Munich), Haim Ashkenazy(Tel Aviv University), Marco Punta(Wellcome Sanger Institute), Avner Schlessinger(European Bioinformatics Institute), Yana Bromberg(Icahn School of Medicine at Mount Sinai), Reinhard Schneider(Rutgers, The State University of New Jersey), Gerrit Vriend(University of Luxembourg), Chris Sander(Radboud University Nijmegen), Nir Ben‐Tal(Memorial Sloan Kettering Cancer Center), Burkhard Rost(Technical University of Munich)
Nucleic Acids Research
May 5, 2014
Cited by 627Open Access
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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.


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