CiteULike: neils's biology

The Bioperl toolkit: Perl modules for the life sciences.

Jason Stajich — 2007-12-04T03:22:10-00:00

Genome Res, Vol. 12, No. 10. (Oct 2002), pp. 1611-1618.

The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.

Systematic discovery of in vivo phosphorylation networks.

Rune Linding — 2007-12-04T03:22:10-00:00

Cell, Vol. 129, No. 7. (Jun 2007), pp. 1415-1426.

Protein kinases control cellular decision processes by phosphorylating specific substrates. Thousands of in vivo phosphorylation sites have been identified, mostly by proteome-wide mapping. However, systematically matching these sites to specific kinases is presently infeasible, due to limited specificity of consensus motifs, and the influence of contextual factors, such as protein scaffolds, localization, and expression, on cellular substrate specificity. We have developed an approach (NetworKIN) that augments motif-based predictions with the network context of kinases and phosphoproteins. The latter provides 60\%-80\% of the computational capability to assign in vivo substrate specificity. NetworKIN pinpoints kinases responsible for specific phosphorylations and yields a 2.5-fold improvement in the accuracy with which phosphorylation networks can be constructed. Applying this approach to DNA damage signaling, we show that 53BP1 and Rad50 are phosphorylated by CDK1 and ATM, respectively. We describe a scalable strategy to evaluate predictions, which suggests that BCLAF1 is a GSK-3 substrate.

Substrate specificity of protein kinases and computational prediction of substrates.

Bostjan Kobe — 2007-12-04T03:22:10-00:00

Biochim Biophys Acta, Vol. 1754, No. 1-2. (Dec 2005), pp. 200-209.

To ensure signalling fidelity, kinases must act only on a defined subset of cellular targets. Appreciating the basis for this substrate specificity is essential for understanding the role of an individual protein kinase in a particular cellular process. The specificity in the cell is determined by a combination of "peptide specificity" of the kinase (the molecular recognition of the sequence surrounding the phosphorylation site), substrate recruitment and phosphatase activity. Peptide specificity plays a crucial role and depends on the complementarity between the kinase and the substrate and therefore on their three-dimensional structures. Methods for experimental identification of kinase substrates and characterization of specificity are expensive and laborious, therefore, computational approaches are being developed to reduce the amount of experimental work required in substrate identification. We discuss the structural basis of substrate specificity of protein kinases and review the experimental and computational methods used to obtain specificity information.

Kinomics: methods for deciphering the kinome.

Sam Johnson — 2007-12-04T03:22:10-00:00

Nat Methods, Vol. 2, No. 1. (Jan 2005), pp. 17-25.

Phosphorylation by protein kinases is the most widespread and well-studied signaling mechanism in eukaryotic cells. Phosphorylation can regulate almost every property of a protein and is involved in all fundamental cellular processes. Cataloging and understanding protein phosphorylation is no easy task: many kinases may be expressed in a cell, and one-third of all intracellular proteins may be phosphorylated, representing as many as 20,000 distinct phosphoprotein states. Defining the kinase complement of the human genome, the kinome, has provided an excellent starting point for understanding the scale of the problem. The kinome consists of 518 kinases, and every active protein kinase phosphorylates a distinct set of substrates in a regulated manner. Deciphering the complex network of phosphorylation-based signaling is necessary for a thorough and therapeutically applicable understanding of the functioning of a cell in physiological and pathological states. We review contemporary techniques for identifying physiological substrates of the protein kinases and studying phosphorylation in living cells.

KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites.

Hsien Da Huang — 2007-12-04T03:22:10-00:00

Nucleic Acids Res, Vol. 33, No. Web Server issue. (Jul 2005), pp. W226-W229.

KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at http://KinasePhos.mbc.nctu.edu.tw/.

The protein kinase complement of the human genome.

G Manning — 2007-12-04T01:53:36-00:00

Science, Vol. 298, No. 5600. (Dec 2002), pp. 1912-1934.

We have catalogued the protein kinase complement of the human genome (the "kinome") using public and proprietary genomic, complementary DNA, and expressed sequence tag (EST) sequences. This provides a starting point for comprehensive analysis of protein phosphorylation in normal and disease states, as well as a detailed view of the current state of human genome analysis through a focus on one large gene family. We identify 518 putative protein kinase genes, of which 71 have not previously been reported or described as kinases, and we extend or correct the protein sequences of 56 more kinases. New genes include members of well-studied families as well as previously unidentified families, some of which are conserved in model organisms. Classification and comparison with model organism kinomes identified orthologous groups and highlighted expansions specific to human and other lineages. We also identified 106 protein kinase pseudogenes. Chromosomal mapping revealed several small clusters of kinase genes and revealed that 244 kinases map to disease loci or cancer amplicons.

Proteomic and computational analysis of secreted proteins with type I signal peptides from the Antarctic archaeon Methanococcoides burtonii.

Neil Saunders — 2007-11-23T05:17:27-00:00

J Proteome Res, Vol. 5, No. 9. (Sep 2006), pp. 2457-2464.

LC-MS/MS was used to identify secreted proteins in the Antarctic archaeon Methanococcoides burtonii. Seven proteins possessing a classical class 1 signal peptide were identified in the supernatant from cultures grown at 4 and 23 degrees C. The proteins included a putative S-layer cell surface protein, cell surface protein involved with cell adhesion, and trypsin-like serine protease. Protease activity was detected in the secreted fraction, and the signal peptide cleavage site of the protease was confirmed using Edman sequencing. The expression profile of putative cell surface proteins suggests a requirement for cell interactions during growth at low temperature. Sequences of the secreted proteins were used to compile a dataset containing a further 32 predicted secreted proteins from the Methanosarcinaceae. Many of these proteins were also S-layer cell surface proteins with a variety of predicted roles, particularly in cell-cell interaction. Computational analysis of signal peptides revealed a preference for lysine in the n-region, leucine in the h-region, and a eucaryal-type cleavage site, highlighting the mosaic nature of signal peptides in Archaea. This is the first study to experimentally characterize secreted proteins from a cold-adapted archaeon and provides new insight and a functional dataset for studying secretion in Archaea.