165,663 research outputs found

    A model of large-scale proteome evolution

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    The next step in the understanding of the genome organization, after the determination of complete sequences, involves proteomics. The proteome includes the whole set of protein-protein interactions, and two recent independent studies have shown that its topology displays a number of surprising features shared by other complex networks, both natural and artificial. In order to understand the origins of this topology and its evolutionary implications, we present a simple model of proteome evolution that is able to reproduce many of the observed statistical regularities reported from the analysis of the yeast proteome. Our results suggest that the observed patterns can be explained by a process of gene duplication and diversification that would evolve proteome networks under a selection pressure, favoring robustness against failure of its individual components

    Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

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    Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics

    The proteostasis network and its decline in ageing

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    Ageing is a major risk factor for the development of many diseases, prominently including neurodegenerative disorders such as Alzheimer disease and Parkinson disease. A hallmark of many age-related diseases is the dysfunction in protein homeostasis (proteostasis), leading to the accumulation of protein aggregates. In healthy cells, a complex proteostasis network, comprising molecular chaperones and proteolytic machineries and their regulators, operates to ensure the maintenance of proteostasis. These factors coordinate protein synthesis with polypeptide folding, the conservation of protein conformation and protein degradation. However, sustaining proteome balance is a challenging task in the face of various external and endogenous stresses that accumulate during ageing. These stresses lead to the decline of proteostasis network capacity and proteome integrity. The resulting accumulation of misfolded and aggregated proteins affects, in particular, postmitotic cell types such as neurons, manifesting in disease. Recent analyses of proteome-wide changes that occur during ageing inform strategies to improve proteostasis. The possibilities of pharmacological augmentation of the capacity of proteostasis networks hold great promise for delaying the onset of age-related pathologies associated with proteome deterioration and for extending healthspan

    Weight matrix based identification of terpene synthases conserved motifs in Arabidopsis thaliana proteome

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    Terpenes comprise the most diverse collection of natural products. Out of more than 30,000 individual terpenoids identified, at least half are synthesized by plants. A relatively small, but quantitatively significant, number of terpenoids are involved in primary plant metabolism. However, the vast majorities are classified as secondary metabolites; compounds not required for plant growth and development but presumed to have an ecological function in communication or defense and are widely used in industrial applications. Terpene hydrocarbon scaffolds are generated by the action of the mechanistically intriguing family of mono-, sesqui-, and diterpene synthases collectively termed as terpene synthases, that catalyze multistep reactions with diphosphorylated substrates of 10 (geranyl diphosphate), 15 (farnesyl diphosphate) or 20 (geranylgeranyl diphosphate) carbons. In the studied work, we performed a computational study on proteome wide identification of terpene synthase motifs in Arabidopsis thaliana proteome on the basis of weight matrix approach. We have developed an optimal weight matrix for the identification of terpene synthase motifs in the plant’s proteome. Weight matrix was constructed by aligning orthologous sequences of known terpene synthases originated from diverse plant species viz., Abies grandis, Nicotiana tobaccum etc. Sequences of detected domains & motifs were retrieved through SwissProtKB/NCBI on the basis of specific conservation IDs of Prosite, Pfam, Interpro, Prodom, COG, TIGR databases, while position specific scoring matrices were made through MEME, MotifSampler, PossuMsearch tools. Weight matrix based search of conserved motifs in the proteome of A. thaliana was done through ESA, Lahead and Simple algorithm based search tools of PossuMsearch biosuite in Linux system. Prediction was first validated by using positive control data set and optimized the method to reach prediction accuracy upto >90%. After tool performance evaluation, prediction was made on whole proteome at specific threshold/score value. Significant results were found in A. thaliana with motif similarity ranges from 80% to 100%. This proteome wide search model paves the path to identify more terpene synthases genes in A. thaliana, as well as in other plant systems

    Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy

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    Background: Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration >= 5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82). Methodology/Principal Findings: Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients. Conclusions: These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin

    Severe childhood malaria syndromes defined by plasma proteome profiles

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    BACKGROUND Cerebral malaria (CM) and severe malarial anemia (SMA) are the most serious life-threatening clinical syndromes of Plasmodium falciparum infection in childhood. Therefore it is important to understand the pathology underlying the development of CM and SMA, as opposed to uncomplicated malaria (UM). Different host responses to infection are likely to be reflected in plasma proteome-patterns that associate with clinical status and therefore provide indicators of the pathogenesis of these syndromes. METHODS AND FINDINGS Plasma and comprehensive clinical data for discovery and validation cohorts were obtained as part of a prospective case-control study of severe childhood malaria at the main tertiary hospital of the city of Ibadan, an urban and densely populated holoendemic malaria area in Nigeria. A total of 946 children participated in this study. Plasma was subjected to high-throughput proteomic profiling. Statistical pattern-recognition methods were used to find proteome-patterns that defined disease groups. Plasma proteome-patterns accurately distinguished children with CM and with SMA from those with UM, and from healthy or severely ill malaria-negative children. CONCLUSIONS We report that an accurate definition of the major childhood malaria syndromes can be achieved using plasma proteome-patterns. Our proteomic data can be exploited to understand the pathogenesis of the different childhood severe malaria syndromes

    Proteomic Approach for Extracting Cytoplasmic Proteins from Streptococcus sanguinis using Mass Spectrometry

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    Streptococcus sanguinis is a commensal and early colonizer of oral cavity as well as an opportunistic pathogen of infectious endocarditis. Extracting the soluble proteome of this bacterium provides deep insights about the physiological dynamic changes under different growth and stress conditions, thus defining “proteomic signatures” as targets for therapeutic intervention. In this protocol, we describe an experimentally verified approach to extract maximal cytoplasmic proteins from Streptococcus sanguinis SK36 strain. A combination of procedures was adopted that broke the thick cell wall barrier and minimized denaturation of the intracellular proteome, using optimized buffers and a sonication step. Extracted proteome was quantitated using Pierce BCA Protein Quantitation assay and protein bands were macroscopically assessed by Coomassie Blue staining. Finally, a high resolution detection of the extracted proteins was conducted through Synapt G2Si mass spectrometer, followed by label-free relative quantification via Progenesis QI. In conclusion, this pipeline for proteomic extraction and analysis of soluble proteins provides a fundamental tool in deciphering the biological complexity of Streptococcus sanguinis

    Pharmacoproteomic characterisation of human colon and rectal cancer

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    Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials. © 2017 The Authors. Published under the terms of the CC BY 4.0 licens