Manual for starch gel electrophoresis: A method for the detection of genetic variation

The procedure to conduct horizontal starch gel electrophoresis on enzymes is described in detail. Areas covered are (I) collection and storage of specimens, (2) preparation of tissues, (3) preparation of a starch gel, (4) application of enzyme extracts to a gel, (5) setting up a gel for electrophoresis, (6) slicing a gel, and (7) staining a gel. Recipes are also included for 47 enzyme stains and 3 selected gel buffers. (PDF file contains 26 pages.

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Chemical cross-linking/mass spectrometry targeting acidic residues in proteins and protein complexes

The study of proteins and protein complexes using chemical cross-linking followed by the MS identification of the cross-linked peptides has found increasingly widespread use in recent years. Thus far, such analyses have used almost exclusively homobifunctional, amine-reactive cross-linking reagents. Here we report the development and application of an orthogonal cross-linking chemistry specific for carboxyl groups. Chemical cross-linking of acidic residues is achieved using homobifunctional dihydrazides as cross-linking reagents and a coupling chemistry at neutral pH that is compatible with the structural integrity of most protein complexes. In addition to cross-links formed through insertion of the dihydrazides with different spacer lengths, zero-length cross-link products are also obtained, thereby providing additional structural information. We demonstrate the application of the reaction and the MS identification of the resulting cross-linked peptides for the chaperonin TRiC/CCT and the 26S proteasome. The results indicate that the targeting of acidic residues for cross-linking provides distance restraints that are complementary and orthogonal to those obtained from lysine cross-linking, thereby expanding the yield of structural information that can be obtained from cross-linking studies and used in hybrid modeling approaches

Proteome coverage prediction with infinite Markov models

Motivation: Liquid chromatography tandem mass spectrometry (LC-MS/MS) is the predominant method to comprehensively characterize complex protein mixtures such as samples from prefractionated or complete proteomes. In order to maximize proteome coverage for the studied sample, i.e. identify as many traceable proteins as possible, LC-MS/MS experiments are typically repeated extensively and the results combined. Proteome coverage prediction is the task of estimating the number of peptide discoveries of future LC-MS/MS experiments. Proteome coverage prediction is important to enhance the design of efficient proteomics studies. To date, there does not exist any method to reliably estimate the increase of proteome coverage at an early stage

Whole cell proteome regulation by microRNAs captured in a pulsed SILAC mass spectrometry approach

Since gene expression is controlled on many different levels in a cell, capturing a comprehensive snapshot of all regulatory processes is a difficult task. One possibility to monitor effective changes within a cell is to directly quantify changes in protein synthesis, which reflects the accumulative impact of regulatory mechanisms on gene expression. Pulsed stable isotope labeling by amino acids in cell culture (pSILAC) has been shown to be a viable method to investigate de novo protein synthesis on a proteome-wide scale (Schwanhausser et al., Proteomics 9:205-209, 2009; Selbach et al., Nature 455:58-63, 2008). One application of pSILAC is to study the regulation of protein expression by microRNAs. Here, we describe how pSILAC in conjunction with shotgun mass spectrometry can assess differences in the protein profile between cells transfected with a microRNA and non-transfected cells

Global alignment of protein-protein interaction networks by graph matching methods

Aligning protein-protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to investigate evolutionary conserved pathways or protein complexes across species, and to help in the identification of functional orthologs through the detection of conserved interactions. It is however a difficult combinatorial problem, for which only heuristic methods have been proposed so far. We reformulate the PPI alignment as a graph matching problem, and investigate how state-of-the-art graph matching algorithms can be used for that purpose. We differentiate between two alignment problems, depending on whether strict constraints on protein matches are given, based on sequence similarity, or whether the goal is instead to find an optimal compromise between sequence similarity and interaction conservation in the alignment. We propose new methods for both cases, and assess their performance on the alignment of the yeast and fly PPI networks. The new methods consistently outperform state-of-the-art algorithms, retrieving in particular 78% more conserved interactions than IsoRank for a given level of sequence similarity. Availability:http://cbio.ensmp.fr/proj/graphm\_ppi/, additional data and codes are available upon request. Contact: [email protected]: Preprint versio

Identification of CDK2 substrates in human cell lysates

Engineered kinases and thiophosphate enrichment were used to identify many candidate CDK2 substrates in human cell lysates

A uniform proteomics MS/MS analysis platform utilizing open XML file formats

The analysis of tandem mass (MS/MS) data to identify and quantify proteins is hampered by the heterogeneity of file formats at the raw spectral data, peptide identification, and protein identification levels. Different mass spectrometers output their raw spectral data in a variety of proprietary formats, and alternative methods that assign peptides to MS/MS spectra and infer protein identifications from those peptide assignments each write their results in different formats. Here we describe an MS/MS analysis platform, the Trans-Proteomic Pipeline, which makes use of open XML file formats for storage of data at the raw spectral data, peptide, and protein levels. This platform enables uniform analysis and exchange of MS/MS data generated from a variety of different instruments, and assigned peptides using a variety of different database search programs. We demonstrate this by applying the pipeline to data sets generated by ThermoFinnigan LCQ, ABI 4700 MALDI-TOF/TOF, and Waters Q-TOF instruments, and searched in turn using SEQUEST, Mascot, and COMET

Deep-Learning-Based Dose Predictor for Glioblastoma-Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process

Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model’s robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process