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Journal ArticleDOI

Proteomics to study genes and genomes

TLDR
Proteomics can be divided into three main areas: protein micro-characterization for large-scale identification of proteins and their post-translational modifications; ‘differential display’ proteomics for comparison of protein levels with potential application in a wide range of diseases; and studies of protein–protein interactions using techniques such as mass spectrometry or the yeast two-hybrid system.
Abstract
Proteomics, the large-scale analysis of proteins, will contribute greatly to our understanding of gene function in the post-genomic era. Proteomics can be divided into three main areas: (1) protein micro-characterization for large-scale identification of proteins and their post-translational modifications; (2) 'differential display' proteomics for comparison of protein levels with potential application in a wide range of diseases; and (3) studies of protein-protein interactions using techniques such as mass spectrometry or the yeast two-hybrid system. Because it is often difficult to predict the function of a protein based on homology to other proteins or even their three-dimensional structure, determination of components of a protein complex or of a cellular structure is central in functional analysis. This aspect of proteomic studies is perhaps the area of greatest promise. After the revolution in molecular biology exemplified by the ease of cloning by DNA methods, proteomics will add to our understanding of the biochemistry of proteins, processes and pathways for years to come.

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Complex networks: Structure and dynamics

TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
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Mass spectrometry-based proteomics

TL;DR: The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.
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Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

TL;DR: A statistical model is presented to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST, demonstrating that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides.
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Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

TL;DR: MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date, identifying 131 proteins with three or more predicted transmembrane domains which allowed us to map the soluble domains of many of the integral membrane proteins.
References
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Journal ArticleDOI

High resolution two-dimensional electrophoresis of proteins.

TL;DR: This technique provides a method for estimation of the number of proteins made by any biological system and can resolve proteins differing in a single charge and consequently can be used in the analysis of in vivo modifications resulting in a change in charge.
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Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans

TL;DR: To their surprise, it was found that double-stranded RNA was substantially more effective at producing interference than was either strand individually, arguing against stochiometric interference with endogenous mRNA and suggesting that there could be a catalytic or amplification component in the interference process.
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Journal ArticleDOI

A novel genetic system to detect protein-protein interactions.

TL;DR: A novel genetic system to study protein-protein interactions between two proteins by taking advantage of the properties of the GAL4 protein of the yeast Saccharomyces cerevisiae, which may be applicable as a general method to identify proteins that interact with a known protein by the use of a simple galactose selection.
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