masstodon: A Tool for Assigning Peaks and Modeling Electron Transfer Reactions in Top-Down Mass Spectrometry

Top-down mass spectrometry methods are becoming continuously more popular in the effort to describe the proteome. They rely on the fragmentation of intact protein ions inside the mass spectrometer. Among the existing fragmentation methods, electron transfer dissociation is known for its precision and wide coverage of different cleavage sites. However, several side reactions can occur under electron transfer dissociation (ETD) conditions, including nondissociative electron transfer and proton transfer reaction. Evaluating their extent can provide more insight into reaction kinetics as well as instrument operation. Furthermore, preferential formation of certain reaction products can reveal important structural information. To the best of our knowledge, there are currently no tools capable of tracing and analyzing the products of these reactions in a systematic way. In this Article, we present in detail masstodon: a computer program for assigning peaks and interpreting mass spectra. Besides being a general purpose tool, masstodon also offers the possibility to trace the products of reactions occurring under ETD conditions and provides insights into the parameters driving them. It is available free of charge under the GNU AGPL V3 public license

On consensus biomarker selection

<p>Abstract</p> <p>Background</p> <p>Recent development of mass spectrometry technology enabled the analysis of complex peptide mixtures. A lot of effort is currently devoted to the identification of biomarkers in human body fluids like serum or plasma, based on which new diagnostic tests for different diseases could be constructed. Various biomarker selection procedures have been exploited in recent studies. It has been noted that they often lead to different biomarker lists and as a consequence, the patient classification may also vary.</p> <p>Results</p> <p>Here we propose a new approach to the biomarker selection problem: to apply several competing feature ranking procedures and compute a consensus list of features based on their outcomes. We validate our methods on two proteomic datasets for the diagnosis of ovarian and prostate cancer.</p> <p>Conclusion</p> <p>The proposed methodology can improve the classification results and at the same time provide a unified biomarker list for further biological examinations and interpretation.</p

Unexpected instabilities explain batch-to-batch variability in cell-free protein expression systems

Cell-free methods of protein synthesis offer rapid access to expressed proteins. Though the amounts produced are generally only at a small scale, these are sufficient to perform protein-protein interaction assays and tests of enzymatic activity. As such they are valuable tools for the biochemistry and bioengineering community. However the most complex, eukaryotic cell-free systems are difficult to manufacture in house and can be prohibitively expensive to obtain from commercial sources. The Leishmania tarentolae system offers a relatively cheap alternative which is capable of producing difficult to express proteins, but which is simpler to produce in large scale. However, this system suffers from batch-to-batch variability, which has been accepted as a consequence of the complexity of the extracts. Here we show an unexpected origin for the variability observed and demonstrate that small variations in a single parameter can dramatically affect expression, such that minor pipetting errors can have major effects on yields. L. tarentolae cell-free lysate activity is shown to be more stable to changes in Mg concentration at a lower ratio of feed solution to lysate in the reaction than typically used, and a higher Mg optimum. These changes essentially eliminate batch-to-batch variability of L. tarentolae lysate activity and permit their full potential to be realized

MIND: A Double-Linear Model To Accurately Determine Monoisotopic Precursor Mass in High-Resolution Top-Down Proteomics

Top-down proteomics approaches are becoming ever more popular, due to the advantages offered by knowledge of the intact protein mass in correctly identifying the various proteoforms that potentially arise due to point mutation, alternative splicing, post-translational modifications, etc. Usually, the average mass is used in this context; however, it is known that this can fluctuate significantly due to both natural and technical causes. Ideally, one would prefer to use the monoisotopic precursor mass, but this falls below the detection limit for all but the smallest proteins. Methods that predict the monoisotopic mass based on the average mass are potentially affected by imprecisions associated with the average mass. To address this issue, we have developed a framework based on simple, linear models that allows prediction of the monoisotopic mass based on the exact mass of the most-abundant (aggregated) isotope peak, which is a robust measure of mass, insensitive to the aforementioned natural and technical causes. This linear model was tested experimentally, as well as in silico, and typically predicts monoisotopic masses with an accuracy of only a few parts per million. A confidence measure is associated with the predicted monoisotopic mass to handle the off-by-one-Da prediction error. Furthermore, we introduce a correction function to extract the “true” (i.e., theoretically) most-abundant isotope peak from a spectrum, even if the observed isotope distribution is distorted by noise or poor ion statistics. The method is available online as an R shiny app: https://valkenborg-lab.shinyapps.io/mind

USING VIRTUAL OR AUGMENTED REALITY for the TIME-BASED STUDY of COMPLEX UNDERWATER ARCHAEOLOGICAL EXCAVATIONS

International audienceCultural Heritage (CH) resources are partial, heterogeneous, discontinuous, and subject to ongoing updates and revisions. The use of semantic web technologies associated with 3D graphical tools is proposed to improve access, exploration, exploitation and enrichment of these CH data in a standardized and more structured form. This article presents the monitoring work developed for more than ten years on the excavation of the Xlendi site. Around an exceptional shipwreck, the oldest from the Archaic period in the Western Mediterranean, we have set up a unique excavation at a depth of 110m assisted by a rigorous and continuous photogrammetry campaign. All the collected results are modelled by an ontology and visualized with virtual and augmented reality tools that allow a bidirectional link between the proposed graphical representations and the non-graphical archaeological data. It is also important to highlight the development of an innovative 3D mobile app that lets users study and understand the site as well as experience sensations close to those of a diver visiting the site

Inferring serum proteolytic activity from LC-MS/MS data

<p>Abstract</p> <p>Background</p> <p>In this paper we deal with modeling serum proteolysis process from tandem mass spectrometry data. The parameters of peptide degradation process inferred from LC-MS/MS data correspond directly to the activity of specific enzymes present in the serum samples of patients and healthy donors. Our approach integrate the existing knowledge about peptidases' activity stored in MEROPS database with the efficient procedure for estimation the model parameters.</p> <p>Results</p> <p>Taking into account the inherent stochasticity of the process, the proteolytic activity is modeled with the use of Chemical Master Equation (CME). Assuming the stationarity of the Markov process we calculate the expected values of digested peptides in the model. The parameters are fitted to minimize the discrepancy between those expected values and the peptide activities observed in the MS data. Constrained optimization problem is solved by Levenberg-Marquadt algorithm.</p> <p>Conclusions</p> <p>Our results demonstrates the feasibility and potential of high-level analysis for LC-MS proteomic data. The estimated enzyme activities give insights into the molecular pathology of colorectal cancer. Moreover the developed framework is general and can be applied to study proteolytic activity in different systems.</p

Estimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry

Electron transfer dissociation (ETD) is a versatile technique used in mass spectrometry for the high-throughput characterization of proteins. It consists of several concurrent reactions triggered by the transfer of an electron from its anion source to sample cations. Transferring an electron causes peptide backbone cleavage while leaving labile post-translational modifications intact. The obtained fragmentation spectra provide valuable information for sequence and structure analyses. In this study, we propose a formal mathematical model of the ETD fragmentation process in the form of a system of stochastic differential equations describing its joint dynamics. Parameters of the model correspond to the rates of occurring reactions. Their estimates for various experimental settings give insight into the dynamics of the ETD process. We estimate the model parameters from the relative quantities of fragmentation products in a given mass spectrum by solving a nonlinear optimization problem. The cost function penalizes for the differences between the analytically derived average number of reaction products and their experimental counterparts. The presented method proves highly robust to noise in silico. Moreover, the model can explain a considerable amount of experimental results for a wide range of instrumentation settings. The implementation of the presented workflow, code-named ETDetective, is freely available under the two-clause BSD license

Tracking Membrane Protein Association in Model Membranes

Membrane proteins are essential in the exchange processes of cells. In spite of great breakthrough in soluble proteins studies, membrane proteins structures, functions and interactions are still a challenge because of the difficulties related to their hydrophobic properties. Most of the experiments are performed with detergent-solubilized membrane proteins. However widely used micellar systems are far from the biological two-dimensions membrane. The development of new biomimetic membrane systems is fundamental to tackle this issue

MyD88 TIR domain higher-order assembly interactions revealed by microcrystal electron diffraction and serial femtosecond crystallography.

MyD88 and MAL are Toll-like receptor (TLR) adaptors that signal to induce pro-inflammatory cytokine production. We previously observed that the TIR domain of MAL (MALTIR) forms filaments in vitro and induces formation of crystalline higher-order assemblies of the MyD88 TIR domain (MyD88TIR). These crystals are too small for conventional X-ray crystallography, but are ideally suited to structure determination by microcrystal electron diffraction (MicroED) and serial femtosecond crystallography (SFX). Here, we present MicroED and SFX structures of the MyD88TIR assembly, which reveal a two-stranded higher-order assembly arrangement of TIR domains analogous to that seen previously for MALTIR. We demonstrate via mutagenesis that the MyD88TIR assembly interfaces are critical for TLR4 signaling in vivo, and we show that MAL promotes unidirectional assembly of MyD88TIR. Collectively, our studies provide structural and mechanistic insight into TLR signal transduction and allow a direct comparison of the MicroED and SFX techniques