Current Proteomics
ISSN: 1570-1646
Current Proteomics
Volume 2, Number 3, October 2005
Contents
Comparison of Large Proteomic Datasets Pp.179
M.H. Maurer
[Abstract]
Identification and Characterization of Peptides and Proteins
in Doping Control Analysis Pp.191
M. Thevis and W. Schänzer
[Abstract]
Unraveling the Dopamine Receptor Signalplex by DRIPs
and DRAPs Pp.209
N. Kabbani, M.A. Hannan and R. Levenson
[Abstract]
Defining Viral Protein Interactomes Using the Yeast
Two-Hybrid Assay Pp.225
E. Diefenbach, A.L. Cunningham and R.J. Diefenbach
[Abstract]
Protein Structure Prediction Using an Augmented Homology
Modeling Method: Key Importance of Iterative-Procedures for
Obtaining Consis-tent Quality Models Pp.233
S. McDonald, S. Mylvaganam, M. Shenderovich, V. Tseitin,
C. Fisher, G. Raghunathan, J. Zheng, R. Kodandapani, M. Dudek,
M. Prabhakaran and K. Ramnarayan
[Abstract]
Abstracts
[Back to top]
Comparison of Large Proteomic Datasets
M.H. Maurer
Proteomic analysis does inherently involve the handling
and interpretation of a huge amount of data. Originally, proteome
analysis aimed at collecting comprehensive information on
all proteins that are present in a specified sample. Proteome
inventories developed to be highly sophisticated databases
collecting all different kinds of data formats, including
two-dimensional gel images, mass spectra, protein sequences,
and post-translational modifications. These databases lay
the foundations for identifying proteins in proteomic studies
aiming to find differentially expressed proteins, thus promoting
proteomics from constructing "descriptive" databases
to the design of "functional" experiments. With
the quest for finding differentially expressed proteins, data
have to be compared between two or more experimental groups.
Therefore, a new field of bioinformatic tools had to be developed
for the proteomic high-throughput technologies, or these tools
had to be adapted from other applications, such as genomic
and transcriptomic analysis based on nucleotide microarrays.
In this review, the strategies for comparing protein concentration
and functional activity by different statistical means, as
well as the methodology of comparing whole sets of genes or
proteins have been evaluated. Comments on the statistical
algorithms incorporated in 2D gel analysis software, and discussions
on alternatives for data comparison have been incorporated.
The use of supervised and unsupervised data analysis and its
application in proteomic experiments, including the use of
hierarchical clustering for identification of functional pathways
in proteome analysis have also been reviewed.
[Back to top]
Identification and Characterization of Peptides and Proteins
in Doping Control Analysis
M. Thevis and W. Schänzer
Analysis of peptides and proteins has become an important
component of doping control laboratories. Several peptide
hormones such as insulin, insulin-like growth factor-I, growth
hormone, erythropoietin, and hemoglobin-based oxygen carriers
are considered to possess an enormous potential to artificially
increase athletic performance and belong to the list of prohibited
compounds and methods established by regulatory authorities
such as the World Anti-Doping Agency (WADA). In order to reveal
abuse of those drugs in professional as well as amateur sports,
doping control laboratories have been developing various strategies
to identify target analytes in blood or urine specimens employing
different biochemical techniques such as immunoaffinity purification,
isoelectric focusing, gel electrophoresis, double-blotting
as well as concomitant top-down and bottom-up mass spectrometry
based proteomic approaches. These enable the quali-tative
determination of derivatives of naturally occurring peptides
and proteins such as insulin and hemoglobin as well as possibilities
to distinguish between endogenously produced and presumably
identical recombinant proteins such as growth hormone and
erythropoietin. Most applied strategies are common proteomics
procedures, but they have been modified to meet the specific
requirements and limitations of doping control, i.e. type
of specimens, available amounts, required specificity and
sensitivity, unambiguousness of results, and speed of analysis.
[Back to top]
Unraveling the Dopamine Receptor Signalplex by DRIPs
and DRAPs
N. Kabbani, M.A. Hannan and R. Levenson
In the postgenomic era, the study of G-protein coupled receptor
(GPCR)-mediated signal transduction has taken a complicated
turn, fueled by the discovery that individual GPCRs are organized
within a supramolecular signaling complex termed the signalplex.
It has now become clear that a vast amount of cellular information
is transmitted via the activity of these multiprotein
signaling complexes. In turn, the detailed characterization
of several signalplexes has led to a critical reevaluation
of the mechanisms underlying the activation and selectivity
of GPCR-mediated signaling within cells. This review examines
the role of protein-protein interactions in D2 dopamine receptor
(D2R) signaling within the brain. Based on studies utilizing
yeast two-hybrid, proteomic, and cell biochemical approaches,
the known direct and indirect interactions between D2 receptors
and an array of cellular proteins which functionally can be
subdivided into scaffolding, cytoskeletal, signaling, receptor,
and ion channel molecules, have been summarized. Interactions
between signalplex components are found to establish and maintain
key aspects of receptor function including the trafficking
and assembly of dopamine receptors within various cellular
compartments. Understanding the molecular complexity of the
D2R signalplex provides a new platform for defining the cellular
mechanisms of dopamine signaling in the brain as well as the
development of novel drugs for antipsychotic and antiparkinsonian
therapy.
[Back to top]
Defining Viral Protein Interactomes Using the Yeast
Two-Hybrid Assay
E. Diefenbach, A.L. Cunningham and R.J. Diefenbach
The yeast two-hybrid assay has proved a powerful tool in
identifying and characterising binary protein-protein interactions.
Not only can it be used to map interacting protein domains,
it can also be used to screen cDNA libraries with a desired
bait to identify novel binding partners. A number of factors
including ease of use, cost effectiveness and suitability
for high throughput analysis have made yeast-two hybrid one
of the assays of choice for defining protein-protein interaction
networks or interactomes for a range of organisms. The focus
of this review is on the definition of viral interactomes
using the yeast two-hybrid assay and the relevance of such
studies to our understanding of viral pathogenesis.
[Back to top]
Protein Structure Prediction Using an Augmented Homology
Modeling Method: Key Importance of Iterative-Procedures for
Obtaining Consis-tent Quality Models
S. McDonald, S. Mylvaganam, M. Shenderovich, V. Tseitin,
C. Fisher, G. Raghunathan, J. Zheng, R. Kodandapani, M. Dudek,
M. Prabhakaran and K. Ramnarayan
Deciphering of the human genome and other model genomes
presents the challenge of unraveling the gene products that
are expressed by these genomes, and identifying the functional
role of the expressed proteins. Rapid determination of the
three-dimensional (3D) structures of the gene products is
vital in this process and is the focus of various Structural
Genomics initiatives around the world. However, determination
of the structures of thousands of new proteins by experimental
methods, such as X-ray crystallography and NMR remains a formidable
task. Here we review a novel approach using an Augmented Homology
Modeling technology termed “ProMax” to provide
high-quality 3D protein structure from protein sequence data,
rigorously assessed by comparison of the Root Mean Square
Deviation (RMSD) of Cα
carbons in modeled versus subsequently determined X-ray structures,
normalized residue energies, and Ramachandran analyses. The
modeling procedure starts with determination of the appropriate
template 3D structures and proceeds from initial structure
generation through multiple iterative energy-based structure
refinements assessed against an elaborate panel of 3D structure
quality assessment tests. The method is general, applicable
to a broad cross-section of protein families, and reasonably
accurate even for protein families with low sequence homology
to available template structures. The Augmented Homology Modeling
approach was tested on various target sequences proposed for
the Critical Assessment of techniques for protein Structure
Prediction (CASP) competitions: CASP3, CASP4 and CASP5. Also,
more than 40 models derived with the “ProMax”
approach were compared with X-ray and NMR structures released
in the Protein Data Bank after the models were built. The
comparison showed good agreement between our models and experimental
structures within the core Cα atom RMSD < 2.0 Å,
and with a stereochemical quality of the models approaching
that of experimental structures. While this method is not
a replacement for experimental methods such as X-ray crystallography,
it is highly useful to derive 3D structures of protein homologues
within or across genomes as a first approximation, with the
accuracy and quality sufficient to use these models in rational
experimental projects involving protein engineering, mutagenesis
design, virtual screening and docking simulations.
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