Dataset Information

Energetics-based protein profiling on a proteomic scale: identification of proteins resistant to proteolysis.

ABSTRACT: Native states of proteins are flexible, populating more than just the unique native conformation. The energetics and dynamics resulting from this conformational ensemble are inherently linked to protein function and regulation. Proteolytic susceptibility is one feature determined by this conformational energy landscape. As an attempt to investigate energetics of proteins on a proteomic scale, we challenged the Escherichia coli proteome with extensive proteolysis and determined which proteins, if any, have optimized their energy landscape for resistance to proteolysis. To our surprise, multiple soluble proteins survived the challenge. Maltose binding protein, a survivor from thermolysin digestion, was characterized by in vitro biophysical studies to identify the physical origin of proteolytic resistance. This experimental characterization shows that kinetic stability is responsible for the unusual resistance in maltose binding protein. The biochemical functions of the identified survivors suggest that many of these proteins may have evolved extreme proteolytic resistance because of their critical roles under stressed conditions. Our results suggest that under functional selection proteins can evolve extreme proteolysis resistance by modulating their conformational energy landscapes without the need to invent new folds, and that proteins can be profiled on a proteomic scale according to their energetic properties by using proteolysis as a structural probe.

SUBMITTER: Park C

PROVIDER: S-EPMC2857998 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Energetics-based protein profiling on a proteomic scale: identification of proteins resistant to proteolysis.

Park Chiwook C Zhou Sharleen S Gilmore Jacqueline J Marqusee Susan S

Journal of molecular biology 20070307 5

Native states of proteins are flexible, populating more than just the unique native conformation. The energetics and dynamics resulting from this conformational ensemble are inherently linked to protein function and regulation. Proteolytic susceptibility is one feature determined by this conformational energy landscape. As an attempt to investigate energetics of proteins on a proteomic scale, we challenged the Escherichia coli proteome with extensive proteolysis and determined which proteins, if ...[more]

PMID: 17400245

Similar Datasets

Project description:BACKGROUND: Attempts to reduce morbidity and mortality in breast cancer is based on efforts to identify novel biomarkers to support prognosis and therapeutic choices. The present study has focussed on S100 proteins as a potentially promising group of markers in cancer development and progression. One reason of interest in this family of proteins is because the majority of the S100 genes are clustered on a region of human chromosome 1q21 that is prone to genomic rearrangements. Moreover, there is increasing evidence that S100 proteins are often up-regulated in many cancers, including breast, and this is frequently associated with tumour progression. METHODS: Samples of breast cancer tissues were obtained during surgical intervention, according to the bioethical recommendations, and cryo-preserved until used. Tissue extracts were submitted to proteomic preparations for 2D-IPG. Protein identification was performed by N-terminal sequencing and/or peptide mass finger printing. RESULTS: The majority of the detected S100 proteins were absent, or present at very low levels, in the non-tumoral tissues adjacent to the primary tumor. This finding strengthens the role of S100 proteins as putative biomarkers. The proteomic screening of 100 cryo-preserved breast cancer tissues showed that some proteins were ubiquitously expressed in almost all patients while others appeared more sporadic. Most, if not all, of the detected S100 members appeared reciprocally correlated. Finally, from the perspective of biomarkers establishment, a promising finding was the observation that patients which developed distant metastases after a three year follow-up showed a general tendency of higher S100 protein expression, compared to the disease-free group. CONCLUSIONS: This article reports for the first time the comparative proteomic screening of several S100 protein members among a large group of breast cancer patients. The results obtained strongly support the hypothesis that a significant deregulation of multiple S100 protein members is associated with breast cancer progression, and suggest that these proteins might act as potential prognostic factors for patient stratification. We propose that this may offer a significant contribution to the knowledge and clinical applications of the S100 protein family to breast cancer.

Project description:BACKGROUND:Emerging proteomic technologies using novel affinity-based reagents allow for efficient multiplexing with high-sample throughput. To identify early biomarkers of myocardial injury, we recently applied an aptamer-based proteomic profiling platform that measures 1129 proteins to samples from patients undergoing septal alcohol ablation for hypertrophic cardiomyopathy, a human model of planned myocardial injury. Here, we examined the scalability of this approach using a markedly expanded platform to study a far broader range of human proteins in the context of myocardial injury. METHODS:We applied a highly multiplexed, expanded proteomic technique that uses single-stranded DNA aptamers to assay 4783 human proteins (4137 distinct human gene targets) to derivation and validation cohorts of planned myocardial injury, individuals with spontaneous myocardial infarction, and at-risk controls. RESULTS:We found 376 target proteins that significantly changed in the blood after planned myocardial injury in a derivation cohort (n=20; P<1.05E-05, 1-way repeated measures analysis of variance, Bonferroni threshold). Two hundred forty-seven of these proteins were validated in an independent planned myocardial injury cohort (n=15; P<1.33E-04, 1-way repeated measures analysis of variance); >90% were directionally consistent and reached nominal significance in the validation cohort. Among the validated proteins that were increased within 1 hour after planned myocardial injury, 29 were also elevated in patients with spontaneous myocardial infarction (n=63; P<6.17E-04). Many of the novel markers identified in our study are intracellular proteins not previously identified in the peripheral circulation or have functional roles relevant to myocardial injury. For example, the cardiac LIM protein, cysteine- and glycine-rich protein 3, is thought to mediate cardiac mechanotransduction and stress responses, whereas the mitochondrial ATP synthase F0 subunit component is a vasoactive peptide on its release from cells. Last, we performed aptamer-affinity enrichment coupled with mass spectrometry to technically verify aptamer specificity for a subset of the new biomarkers. CONCLUSIONS:Our results demonstrate the feasibility of large-scale aptamer multiplexing at a level that has not previously been reported and with sample throughput that greatly exceeds other existing proteomic methods. The expanded aptamer-based proteomic platform provides a unique opportunity for biomarker and pathway discovery after myocardial injury.

Project description:Plasma biomarkers that reflect molecular states of the cardiovascular system are central for clinical decision making. Routinely used plasma biomarkers include troponins, natriuretic peptides, and lipoprotein particles, yet interrogate only a modest subset of pathways relevant to cardiovascular disease. Systematic profiling of a larger portion of circulating plasma proteins (the plasma proteome) will provide opportunities for unbiased discovery of novel markers to improve diagnostic or predictive accuracy. In addition, proteomic profiling may inform pathophysiological understanding and point to novel therapeutic targets. Obstacles for comprehensive proteomic profiling include the immense size and structural heterogeneity of the proteome, and the broad range of abundance levels, as well. Proteome-wide, untargeted profiling can be performed in tissues and cells with tandem mass spectrometry. However, applications to plasma are limited by the need for complex preanalytical sample preparation stages limiting sample throughput. Multiplexing of targeted methods based on capture and detection of specific proteins are therefore receiving increasing attention in plasma proteomics. Immunoaffinity assays are the workhorse for measuring individual proteins but have been limited for proteomic applications by long development times, cross-reactivity preventing multiplexing, specificity issues, and incomplete sensitivity to detect proteins in the lower range of the abundance spectrum (below picograms per milliliter). Emerging technologies to address these issues include nucleotide-labeled immunoassays and aptamer reagents that can be automated for efficient multiplexing of thousands of proteins at high sample throughput, coupling of affinity capture methods to mass spectrometry for improved specificity, and ultrasensitive detection systems to measure low-abundance proteins. In addition, proteomics can now be integrated with modern genomics tools to comprehensively relate proteomic profiles to genetic variants, which may both influence binding of affinity reagents and serve to validate the target specificity of affinity assays. The application of deep quantitative proteomic profiling to large cohorts has thus become increasingly feasible with emerging affinity methods. The aims of this article are to provide the broad readership of Circulation with a timely overview of emerging methods for affinity proteomics and recent progress in cardiovascular medicine based on such methods.

Dataset Information

Energetics-based protein profiling on a proteomic scale: identification of proteins resistant to proteolysis.

Publications

Energetics-based protein profiling on a proteomic scale: identification of proteins resistant to proteolysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets