Sequence-based feature prediction and annotation of proteins

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Abstract

The combination of prediction tools in complex workflows and pipelines facilitates prediction of protein features from sequence.

Abstract

A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome.

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Most cited references 45

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Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.

H. Nielsen, J Engelbrecht, S Brunak … (1997)

We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The method performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server.

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PredGPI: a GPI-anchor predictor

Andrea Pierleoni, Pier Luigi Martelli, Rita Casadio (2008)

Background Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called ω-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes. Results Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the ω-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature. Conclusion PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes.

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STRING 7—recent developments in the integration and prediction of protein interactions

Christian von Mering, Lars J. Jensen, Michael Kuhn … (2006)

Information on protein–protein interactions is still mostly limited to a small number of model organisms, and originates from a wide variety of experimental and computational techniques. The database and online resource STRING generalizes access to protein interaction data, by integrating known and predicted interactions from a variety of sources. The underlying infrastructure includes a consistent body of completely sequenced genomes and exhaustive orthology classifications, based on which interaction evidence is transferred between organisms. Although primarily developed for protein interaction analysis, the resource has also been successfully applied to comparative genomics, phylogenetics and network studies, which are all facilitated by programmatic access to the database backend and the availability of compact download files. As of release 7, STRING has almost doubled to 373 distinct organisms, and contains more than 1.5 million proteins for which associations have been pre-computed. Novel features include AJAX-based web-navigation, inclusion of additional resources such as BioGRID, and detailed protein domain annotation. STRING is available at

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Journal

Journal ID (nlm-ta): Genome Biol

Title: Genome Biology

Publisher: BioMed Central

ISSN (Print): 1465-6906

ISSN (Electronic): 1465-6914

Publication date (Print): 2009

Publication date (Electronic): 2 February 2009

Volume: 10

Issue: 2

Page: 206

Affiliations

[1 ]Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark

[2 ]European Molecular Biology Laboratory, D-69117 Heidelberg, Germany

[3 ]University of Bologna, Biocomputing Group, Via San Giacomo 9/2, 40126 Bologna, Italy

[4 ]Center for Biomembrane Research and Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden

[5 ]Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, E-28029, Madrid, Spain

[6 ]KCL Centre for Bioinformatics, School of Physical Sciences and Engineering, King's College London, London WC2R 2LS, UK

Article

Publisher ID: gb-2009-10-2-206

DOI: 10.1186/gb-2009-10-2-206

PMC ID: 2688272

PubMed ID: 19226438

SO-VID: e02a8e1f-12f6-4498-8bb5-00268684584e

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Sequence-based feature prediction and annotation of proteins

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Most cited references 45

Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.

PredGPI: a GPI-anchor predictor

STRING 7—recent developments in the integration and prediction of protein interactions

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