The human proteome project will demand faster, easier, and more
reliable methods to isolate and purify protein targets. Membrane
proteins are the most valuable group of proteins since they are
the target for 70&#8211;80&#37; of all drugs. Perbio Science has
developed a protocol for the quick, easy, and reproducible
isolation of integral membrane proteins from eukaryotic cells.
This procedure utilizes a proprietary formulation to facilitate
cell membrane disruption in a mild, nondenaturing environment and
efficiently solubilizes membrane proteins. The technique utilizes
a two-phase partitioning system that enables the class separation
of hydrophobic and hydrophilic proteins. A variety of protein
markers were used to investigate the partitioning efficiency of
the membrane protein extraction reagents (Mem-PER) (Mem-PER is a
registered trademark of Pierce Biotechnology, Inc) system. These
included membrane proteins with one or more transmembrane
spanning domains as well as peripheral and cytosolic proteins.
Based on densitometry analyses of our Western blots, we obtained
excellent solubilization of membrane proteins with less than
10&#37; contamination of the hydrophobic fraction with hydrophilic
proteins. Compared to other methodologies for membrane protein
solubilization that use time-consuming protocols or expensive and
cumbersome instrumentation, the Mem-PER reagents system for
eukaryotic membrane protein extraction offers an easy, efficient,
and reproducible method to isolate membrane proteins from
mammalian and yeast cells.

M. Walid Qoronfleh

Betsy  Benton

Ray  Ignacio

Barbara  Kaboord

Selective Enrichment of Membrane Proteins by Partition Phase Separation for Proteomic Studies

George L. Wright Jr

O. John Semmes

Proteomics in Health and Disease

Discovery of “signature” protein profiles that distinguish
disease states (eg, malignant, benign, and normal) is a key step
towards translating recent advancements in proteomic technologies
into clinical utilities. Protein data generated from mass
spectrometers are, however, large in size and have complex
features due to complexities in both biological specimens and
interfering biochemical/physical processes of the measurement
procedure. Making sense out of such high-dimensional complex
data is challenging and necessitates the use of a systematic data
analytic strategy. We propose here a data processing strategy for
two major issues in the analysis of such
mass-spectrometry-generated proteomic data: (1) separation of
protein “signals” from background “noise” in protein
intensity measurements and (2) calibration of protein mass/charge
measurements across samples. We illustrate the two issues and
the utility of the proposed strategy using data from a prostate
cancer biomarker discovery project as an example.

Yutaka Yasui

Dale McLerran

Bao-Ling Adam

Marcy Winget

Mark Thornquist

Ziding Feng

An Automated Peak Identification/Calibration Procedure for
High-Dimensional Protein Measures From Mass Spectrometers

Now that the human genome is completed, the characterization of
the proteins encoded by the sequence remains a challenging task.
The study of the complete protein complement of the genome, the
&#8220;proteome,&#8221; referred to as proteomics, will be essential if new
therapeutic drugs and new disease biomarkers for early diagnosis
are to be developed. Research efforts are already underway to
develop the technology necessary to compare the specific protein
profiles of diseased versus nondiseased states. These
technologies provide a wealth of information and rapidly generate
large quantities of data. Processing the large amounts of data
will lead to useful predictive mathematical descriptions of
biological systems which will permit rapid identification of
novel therapeutic targets and identification of metabolic
disorders. Here, we present an overview of the current status and
future research approaches in defining the cancer cell's proteome
in combination with different bioinformatics and computational
biology tools toward a better understanding of health and disease.

Halima  Bensmail

Abdelali  Haoudi

Postgenomics: Proteomics and Bioinformatics in Cancer Research

Predictive medicine, utilizing the ProteinChip&#174; Array technology, will develop through the implementation of novel biomarkers and multimarker patterns for detecting disease, determining patient prognosis, monitoring drug effects such as efficacy or toxicity, and for defining treatment options. These biomarkers may also serve as novel protein drug candidates or protein drug targets. In addition, the technology can be used for discovering small molecule drugs or for defining their mode of action utilizing protein-based assays. In this review, we describe the following applications of the ProteinChip Array technology: (1) discovery and identification of novel inhibitors of HIV-1 replication, (2) serum and tissue proteome analysis for the discovery and development of novel multimarker clinical assays for prostate, breast, ovarian, and other cancers, and (3) biomarker and drug discovery applications for neurological disorders.

Guru  Reddy

Enrique A. Dalmasso

SELDI ProteinChip&#174; Array Technology: Protein-Based Predictive Medicine and Drug Discovery Applications

In the growing field of proteomics, tools for the in silico
analysis of proteins and even of whole proteomes are of crucial
importance to make best use of the accumulating amount of data.
To utilise this data for healthcare and drug development, first
the characteristics of proteomes of entire species&#8212;mainly the
human&#8212;have to be understood, before secondly differentiation
between individuals can be surveyed. Specialised databases about
nucleic acid sequences, protein sequences, protein tertiary
structure, genome analysis, and proteome analysis represent
useful resources for analysis, characterisation, and
classification of protein sequences. Different from most
proteomics tools focusing on similarity searches, structure
analysis and prediction, detection of specific regions,
alignments, data mining, 2D PAGE analysis, or protein modelling,
respectively, comprehensive databases like the proteome analysis
database benefit from the information stored in different
databases and make use of different protein analysis tools to
provide computational analysis of whole proteomes.

Manuela  Pruess

Rolf  Apweiler

Bioinformatics Resources for In Silico Proteome Analysis

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BioMed Research International | Hindawi

BioMed Research International

This Issue is now open for submissions.
Papers are published upon acceptance, regardless of the Special Issue publication date.

BioMed Research International

Proteomics in Health and Disease Part I

Proteomics in Health and Disease Part I

Articles

Selective Enrichment of Membrane Proteins by Partition Phase Separation for Proteomic Studies

Proteomics in Health and Disease

An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers

Postgenomics: Proteomics and Bioinformatics in Cancer Research

SELDI ProteinChip^® Array Technology: Protein-Based Predictive Medicine and Drug Discovery Applications

Bioinformatics Resources for In Silico Proteome Analysis