Proteomics and drug discovery.
Explore the source record for details and available documents.
Biotechnology↗
SEARCH · PubMed Health
Results for “discovery proteomics”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Proteomic-based biomarker discovery with emphasis on cerebrospinal fluid and multiple sclerosis.
Discovery of disease specific biomarkers in human body fluids has become an important challenge in clinical proteomics. Facing the increasing threat of degenerative and disabling diseases like cancer, cardiovascular, neurological and inflammatory diseases in large parts of the world's population, there is an urgent need to improve early diagnostics. In this review we discuss possibilities and limitations connected to using mass spectrometry based proteomics in the search for novel biomarkers, with focus on multiple sclerosis as a typical representative for the large group of non-curable degenerative and disabling disease with the lack of specific tests for early diagnosis. Careful control of the pre-analytical phase including sampling, storage and fractionation of samples, in addition to a thoroughly considered patient selection, is important in order to avoid false biomarkers to appear in the resulting mass spectra. Furthermore, advanced computational tools are needed in order to discover potential biomarkers from the enormous data amounts generated by the mass spectrometers. The development of such computer tools is a research field currently in the start phase and could prove to be a bottle neck in the biomarker discovery the next years. Therefore, a rather detailed review of the most used computational and pre-analytical methods is given in this review. Mass spectrometry based biomarker discovery is undoubtedly still in its early infancy. However, in light of the potential of this technology to provide deep coverage of the body fluid proteomes, it will certainly consolidate its role in developing molecular medicine into clinical practice.
Biomarkers↗
Raw data to knowledge warehouse in proteomic-based drug discovery: a scientific data management issue.
Explore the source record for details and available documents.
Computational Biology↗
Unraveling the complex proteome for biomarker discovery in gastrointestinal and liver diseases.
Explore the source record for details and available documents.
Biomarkers↗
Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry.
We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.
Algorithms↗
SELDI-TOF mass spectrometry for cancer biomarker discovery and serum proteomic diagnostics.
Proteomics is more than the identification of proteins that are altered in expression as a consequence of disease. Among the tools critical for the detection, treatment and monitoring of disease are biomarkers. The necessity for new methods to identify and validate biomarkers is underscored by the increased survival of patients diagnosed at early stages of cancer. Serum proteomic pattern diagnostics is a new technique in which proteomic signatures are used as a diagnostic classifier. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry has shown promise as a modality for biomarker discovery of early-stage cancers. Mass spectrometry-derived protein signatures have been modeled and are now moving into validation. Further sequencing of these key features can lead to new insights into disease etiology and intervention.
Biomarkers, Tumor↗
Human protein reference database as a discovery resource for proteomics.
The rapid pace at which genomic and proteomic data is being generated necessitates the development of tools and resources for managing data that allow integration of information from disparate sources. The Human Protein Reference Database (http://www.hprd.org) is a web-based resource based on open source technologies for protein information about several aspects of human proteins including protein-protein interactions, post-translational modifications, enzyme-substrate relationships and disease associations. This information was derived manually by a critical reading of the published literature by expert biologists and through bioinformatics analyses of the protein sequence. This database will assist in biomedical discoveries by serving as a resource of genomic and proteomic information and providing an integrated view of sequence, structure, function and protein networks in health and disease.
Computational Biology↗
Urinary proteomics and biomarker discovery for glomerular diseases.
Explore the source record for details and available documents.
Biomarkers↗
Proteomic approaches to biomarker discovery in prostate and bladder cancers.
Proteomic technologies, including high resolution two-dimensional electrophoresis (2-DE), antibody/protein arrays, and advances in mass spectrometry (MS), are providing the tools needed to discover and identify disease associated biomarkers. Although application of these technologies to search for potential diagnostic/prognostic biomarkers associated with prostate and bladder cancer have been somewhat limited to date, proteins either overexpressed or underexpressed have been detected in both these urological cancers. Recent advances in mass spectrometry, especially platforms that permit rapid "fingerprint" profiling of multiple biomarkers, and tandem mass spectrometers for protein identification, will most assuredly enhance the discovery, identification, and characterization of potential cancer associated biomarkers. Furthermore, application of laser capture microdissection microscopes has provided a rapid and reproducible approach to procure pure populations of cells. This technology coupled to 2-DE and MS has significantly aided the elucidation of the differential expression profiles between disease, benign and normal prostate and bladder cell populations. Finally, development and application of learning algorithms and bioinformatics to the data generated by these proteomic technologies will be essential in determining the clinical potential of a protein biomarker. The purpose of this review is to provide the reader with an overview of the application of these technologies in the search and identification of potential diagnostic/prognostic biomarkers for prostate and bladder cancers.
Biomarkers, Tumor↗
Accelerating discoveries in the proteome and genome with MALDI TOF MS.
Recent developments in mass spectrometry (MS) provide scientists with an established analytical tool that addresses the demands for rapid, accurate and cost effective analyses of biomolecules. These advances clearly accelerate the rate and success of protein identification, genetic sequencing, determining biological variances and drug discovery. This review is intended to illustrate how matrix-assisted laser desorption/ionisation (MALDI) time-of-flight (TOF) MS is typically used to generate information about biomolecules under investigation. Additionally, examples will be used to describe the steps involved in preparing samples for MALDI TOF and obtaining answers through data management.
Animals↗
The use of proteomics in the discovery of serum biomarkers from patients with severe acute respiratory syndrome.
Severe acute respiratory syndrome (SARS) is a new infectious disease with a global impact. Understanding its pathogenesis and developing specific diagnostic methods for its early diagnosis are crucial for the effective management and control of this disease. By using proteomic technology, truncated forms of alpha(1)-antitrypsin (TF-alpha(1)-AT) were found to increase significantly and consistently in sera of SARS patients compared to control subjects. The result showed a sensitivity of 100% for SARS patients and a specificity of 92.8% for controls. Furthermore, the levels of these proteins significantly correlated with certain clinico-pathological parameters. The dramatic increase in TF-alpha(1)-AT may be the result of degradation of alpha(1)-AT. As alpha(1)-AT plays an important role in the protection of lung function, its degradation may be an important factor in the pathogenesis of SARS. These findings indicate that increased TF-alpha(1)-AT may be therapeutically relevant, and may also be a useful biological marker for the diagnosis of SARS.
Biomarkers↗
Functional proteomics for the discovery of carbohydrate-related enzyme activities.
Carbohydrate metabolism is central to the growth and development of organisms. Thousands of genes that are believed to code for proteins that build and remodel saccharides have been uncovered by genome sequencing projects, thereby necessitating higher-throughput methods to delineate the chemical and biological functions of all these proteins. Recent methods discussed in this review have begun to address this problem by the design of new activity-based probes and mass-differentiated carbohydrate libraries. In addition, a comparative view of carbohydrate-related enzymes from all three branches of life has led to the discovery of unusual enzymes to simplify the synthesis of carbohydrates such as the sugar nucleotides required by glycosyltransferases.
Carbohydrate Metabolism↗
Shotgun proteomics and biomarker discovery.
Coupling large-scale sequencing projects with the amino acid sequence information that can be gleaned from tandem mass spectrometry (MS/MS) has made it much easier to analyze complex mixtures of proteins. The limits of this "shotgun" approach, in which the protein mixture is proteolytically digested before separation, can be further expanded by separating the resulting mixture of peptides prior to MS/MS analysis. Both single dimensional high pressure liquid chromatography (LC) and multidimensional LC (LC/LC) can be directly interfaced with the mass spectrometer to allow for automated collection of tremendous quantities of data. While there is no single technique that addresses all proteomic challenges, the shotgun approaches, especially LC/LC-MS/MS-based techniques such as MudPIT (multidimensional protein identification technology), show advantages over gel-based techniques in speed, sensitivity, scope of analysis, and dynamic range. Advances in the ability to quantitate differences between samples and to detect for an array of post-translational modifications allow for the discovery of classes of protein biomarkers that were previously unassailable.
Biomarkers↗
Exploiting proteomics in the discovery of drugs that target mitochondrial oxidative damage.
To understand how oxidative stress contributes to aging and age-related diseases and to better evaluate the therapeutic effect of antioxidant drugs, it would be highly desirable to have a comprehensive and detailed readout of the types of oxidative damage that occur to proteins at a global or proteome level. In this Perspective, I examine how proteomics, defined here as the science of examining all proteins in an organelle, cell, or tissue in the context of biological phenomena, can be used to provide molecular details of mitochondrial protein oxidative damage. Specifically, I discuss approaches that combine knowledge of the mitochondrial proteome with newer mass spectrometry-based techniques that are capable of identifying proteins and sites of oxidative modification in a high-throughput manner.
Animals↗
From Peaks to Power: Systematic Evaluation of Chromatographic Sampling Reveals Determinants of Quantification and Biological Discovery in DIA Proteomics.
Modern DIA proteomics increasingly emphasizes throughput and depth for large-cohort studies, but methods are often optimized using proxy metrics that can mask losses in quantifiable signal and statistical power. Here, we evaluate how data points per peak and other chromatographic features jointly contribute to quantification and downstream biological discovery. Using a matrix-matched calibration curve dataset, we checked how the number of data points per peak (DPPP) affects the limits of detection and quantification (LOD/LOQ). Reduced DPPP minimally affected LOD but substantially degraded LOQ. Feature modeling and nonparametric association analyses identified precursor peak area as the strongest feature-level predictor of LOQ, whereas DPPP showed weaker and context-dependent effects. Simulations of chromatographic peak integration recapitulated these trends, showing that increased sampling primarily improves integration precision, while quantitative accuracy is strongly governed by peak height and peak shape. Finally, when comparing 20 cancer vs 20 control plasma samples processed with Seer Proteograph, the decrease in DPPP led to a loss of statistical significance for proteins with low-abundance precursors. These findings argue that DIA optimization should prioritize LOQ and statistical power metrics─not identifications alone─by balancing sampling density with chromatographic peak height and quality to maximize useful biological signal.
Proteomics↗
Clinical proteomics: from biomarker discovery and cell signaling profiles to individualized personal therapy.
The discovery of new highly sensitive and specific biomarkers for early disease detection and risk stratification coupled with the development of personalized "designer" therapies holds the key to future treatment of complex diseases such as cancer. Mounting evidence confirms that the low molecular weight (LMW) range of the circulatory proteome contains a rich source of information that may be able to detect early stage disease and stratify risk. Current mass spectrometry (MS) platforms can generate a rapid and high resolution portrait of the LMW proteome. Emerging novel nanotechnology strategies to amplify and harvest these LMW biomarkers in vivo or ex vivo will greatly enhance our ability to discover and characterize molecules for early disease detection, subclassification and prognostic capability of current proteomics modalities. Ultimately genetic mutations giving rise to disease are played out and manifested on a protein level, involving derangements in protein function and information flow within diseased cells and the interconnected tissue microenvironment. Newly developed highly sensitive, specific and linearly dynamic reverse phase protein microarray systems are now able to generate circuit maps of information flow through phosphoprotein networks of pure populations of microdissected tumor cells obtained from patient biopsies. We postulate that this type of enabling technology will provide the foundation for the development of individualized combinatorial therapies of molecular inhibitors to target tumor-specific deranged pathways regulating key biologic processes including proliferation, differentiation, apoptosis, immunity and metastasis. Hence future therapies will be tailored to the specific deranged molecular circuitry of an individual patient's disease. The successful transition of these groundbreaking proteomic technologies from research tools to integrated clinical diagnostic platforms will require ongoing continued development, and optimization with rigorous standardization development and quality control procedures.
Biomarkers↗
The comparative analysis of serum proteomes for the discovery of biomarkers for acute myeloid leukemia.
OBJECTIVE: Acute myeloid leukemia (AML) develops as the consequence of a series of genetic changes in a hematopoietic precursor cell. However, the definitive diagnostic protein biomarkers for AML are still unclear. In our study to identify the biomarkers for an initial diagnosis, detection of relapse, and monitoring the minimal residual disease in AML by a less invasive method, serum proteins reflecting alterations in their proteomes were analyzed. MATERIALS AND METHODS: We compared the two-dimensional electrophoresis patterns of human sera of 12 patients with AML with those of 12 normal subjects. The differentially expressed spots were identified by matrix-assisted laser desorption/ionization time-of-flight and electrospray ionization quadupole time-of-flight mass spectrometries. RESULTS: Eight proteins that expressed differentially in the AML group were found. The expression levels of alpha-2-HS-glycoprotein, complement-associated protein SP-40, 40, RBP4 gene product, lipoprotein C-III, and an unknown protein were downregulated in serum of AML patients, whereas the other three proteins, including immunoglobulin heavy-chain variant, proteosome 26S ATPase subunit 1, and haptoglobin-1 were upregulated. CONCLUSION: These results suggest that these proteins can be used as less invasive diagnostic and monitoring biomarkers of AML if further studies are done.
Adult↗