1 and O. John Semmes
This article has been One of the major goals of the postgenomic era is understanding
the structures, interactions, and functions of all cell proteins.
This becomes a daunting task considering the estimation that
there are between 100 000 and 200 000 individual proteins
resulting from alternative splicing of the 30 000 genes encoded
by the human genome. Since the cellular proteome is a dynamic
profile, subject to change in response to various signals through
posttranslational modification, translocation, and
protein-protein and protein-nucleic acid interactions, the task
becomes even more complex looming to a million or more
modification events. Proteomics encompasses the study of
expressed proteins, including identification and elucidation of
the structure-function interrelationships which define healthy
and disease conditions. Information at the level of the proteome
is critical to understanding the function of cellular phenotype
and its role in health and disease. Since posttranslational
events and, indeed, an accurate assessment of protein expression
levels cannot always be predicted by mRNA analysis, proteomics,
used in concert with genomics, can provide a holistic
understanding of the biology underlying the disease process. The
challenge in deciphering the proteome is the development and
integration of analytical instrumentation combined with
bioinformatics that provide rapid, high-throughput, sensitive,
and reproducible tools.
This issue of the Journal of Biomedicine and Biotechnology
presents the first of a two-part series consisting of ten papers
that describe both technical and bioinformatic advances to define
the cell proteome towards a better understanding of health and
disease. The current issue consists of the first five articles
beginning with papers by Bensmail and Haoudi, and Pruess and
Apweiler that describe bioinformatics approaches for defining the
cancer cell proteome and for in silico proteomic analyses.
Because of the high dimensionality of the data generated by
proteomic methodologies, such as protein microarrays and mass
spectral analyses, more efficient and accurate bioinformatics
tools are required to mine and analyze the data. Major
advances in mass spectrometry have resulted in rapid,
high-throughput technologies for protein biomarker discovery,
protein identification, disease analyses, and identification of
posttranslational modifications. One advance, SELDI ProteinChip
mass spectrometry, is the subject of the next two papers that
describe its use for biomarker discovery and its potential as a
platform for development of multimarker clinical assays. The
first paper by Reddy and Dalmasso presents a review describing the
use of SELDI for biomarker discovery, drug discovery, protein
identification, and for development of multiplex clinical assays,
citing examples for cancer, neurological disorders, and
infectious diseases. Feng and associates then describe an
automated peak identification and calibration procedure for more
precise mass analyses when attempting to differentiate disease
from nondisease protein patterns. The last paper of this issue by
Qoronfleh and associates describes a method for the isolation of
membrane proteins for proteomic analysis.
The next issue (volume 2003, issue 5) presents the remaining five
papers. This issue begins with a review by Xu and Lam on protein
and chemical microarray approaches being utilized for proteomic
studies. Then Flower and colleagues describe bioinformatics
approaches for defining the immunome for discovery of novel
vaccines. This is followed by a paper by Qoronfleh and colleagues
who describe improved methods for detecting protein: protein
interactions. Piccoli's research team then report a method for
optimizing the rolling circle application technology for
generating a sensitive high-throughput multiplex protein
microarray for analysis of protein expression and molecular
diagnosis. The final paper in this issue is by Vlahou and
associates who describe the use of SELDI protein profiling
coupled with a commercial decision tree learning algorithm for
biomarker discovery and diagnosis of ovarian cancer.
The content of this special issue, although broad and addressing
several key issues in proteomics research, still leaves many
issues to be covered, especially functional and structural
proteomics, in this fast evolving field of research. We
anticipate addressing other new discoveries and applications in
the proteomics field in future issues of the Journal of
Biomedicine and Biotechnology.