Swath-ms: a new data independent acquisition LC-MS methodology for quantitative complete proteome analysis

Comunicaciones a congreso

SOPHIE velocimetry of Kepler transit candidates XII. KOI-1257 b: a highly eccentric three-month period transiting exoplanet

In this paper we report a new transiting warm giant planet: KOI-1257 b. It was first detected in photometry as a planet-candidate by the ${\it Kepler}$ space telescope and then validated thanks to a radial velocity follow-up with the SOPHIE spectrograph. It orbits its host star with a period of 86.647661 d $\pm$ 3 s and a high eccentricity of 0.772 $\pm$ 0.045. The planet transits the main star of a metal-rich, relatively old binary system with stars of mass of 0.99 $\pm$ 0.05 Msun and 0.70 $\pm$ 0.07 Msun for the primary and secondary, respectively. This binary system is constrained thanks to a self-consistent modelling of the ${\it Kepler}$ transit light curve, the SOPHIE radial velocities, line bisector and full-width half maximum (FWHM) variations, and the spectral energy distribution. However, future observations are needed to confirm it. The PASTIS fully-Bayesian software was used to validate the nature of the planet and to determine which star of the binary system is the transit host. By accounting for the dilution from the binary both in photometry and in radial velocity, we find that the planet has a mass of 1.45 $\pm$ 0.35 Mjup, and a radius of 0.94 $\pm$ 0.12 Rjup, and thus a bulk density of 2.1 $\pm$ 1.2 g.cm$^{-3}$. The planet has an equilibrium temperature of 511 $\pm$ 50 K, making it one of the few known members of the warm-jupiter population. The HARPS-N spectrograph was also used to observe a transit of KOI-1257 b, simultaneously with a joint amateur and professional photometric follow-up, with the aim of constraining the orbital obliquity of the planet. However, the Rossiter-McLaughlin effect was not clearly detected, resulting in poor constraints on the orbital obliquity of the planet.Comment: 39 pages, 17 figures, accepted for publication in Astronomy & Astrophysic

ICC-CLASS: isotopically-coded cleavable crosslinking analysis software suite

<p>Abstract</p> <p>Background</p> <p>Successful application of crosslinking combined with mass spectrometry for studying proteins and protein complexes requires specifically-designed crosslinking reagents, experimental techniques, and data analysis software. Using isotopically-coded ("heavy and light") versions of the crosslinker and cleavable crosslinking reagents is analytically advantageous for mass spectrometric applications and provides a "handle" that can be used to distinguish crosslinked peptides of different types, and to increase the confidence of the identification of the crosslinks.</p> <p>Results</p> <p>Here, we describe a program suite designed for the analysis of mass spectrometric data obtained with isotopically-coded <it>cleavable </it>crosslinkers. The suite contains three programs called: DX, DXDX, and DXMSMS. DX searches the mass spectra for the presence of ion signal doublets resulting from the light and heavy isotopic forms of the isotopically-coded crosslinking reagent used. DXDX searches for possible mass matches between cleaved and uncleaved isotopically-coded crosslinks based on the established chemistry of the cleavage reaction for a given crosslinking reagent. DXMSMS assigns the crosslinks to the known protein sequences, based on the isotopically-coded and un-coded MS/MS fragmentation data of uncleaved and cleaved peptide crosslinks.</p> <p>Conclusion</p> <p>The combination of these three programs, which are tailored to the analytical features of the specific isotopically-coded cleavable crosslinking reagents used, represents a powerful software tool for automated high-accuracy peptide crosslink identification. See: <url>http://www.creativemolecules.com/CM_Software.htm</url></p

Integrating Decision-Making Support in Geocollaboration Tools

info:eu-repo/semantics/publishedVersio

Rpgrip1 is required for rod outer segment development and ciliary protein trafficking in zebrafish

The authors would like to thank the Royal Society of London, the National Eye Research Centre, the Visual Research Trust, Fight for Sight, the W.H. Ross Foundation, the Rosetrees Trust, and the Glasgow Children’s Hospital Charity for supporting this work. This work was also supported by the Deanship of Scientific Research at King Saud University for funding this research (Research Project) grant number ‘RGP – VPP – 219’.Mutations in the RPGR-interacting protein 1 (RPGRIP1) gene cause recessive Leber congenital amaurosis (LCA), juvenile retinitis pigmentosa (RP) and cone-rod dystrophy. RPGRIP1 interacts with other retinal disease-causing proteins and has been proposed to have a role in ciliary protein transport; however, its function remains elusive. Here, we describe a new zebrafish model carrying a nonsense mutation in the rpgrip1 gene. Rpgrip1homozygous mutants do not form rod outer segments and display mislocalization of rhodopsin, suggesting a role for RPGRIP1 in rhodopsin-bearing vesicle trafficking. Furthermore, Rab8, the key regulator of rhodopsin ciliary trafficking, was mislocalized in photoreceptor cells of rpgrip1 mutants. The degeneration of rod cells is early onset, followed by the death of cone cells. These phenotypes are similar to that observed in LCA and juvenile RP patients. Our data indicate RPGRIP1 is necessary for rod outer segment development through regulating ciliary protein trafficking. The rpgrip1 mutant zebrafish may provide a platform for developing therapeutic treatments for RP patients.Publisher PDFPeer reviewe

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound

Human-computer collaboration for skin cancer recognition

The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice

Discovery and Expansion of Gene Modules by Seeking Isolated Groups in a Random Graph Process

BACKGROUND: A central problem in systems biology research is the identification and extension of biological modules-groups of genes or proteins participating in a common cellular process or physical complex. As a result, there is a persistent need for practical, principled methods to infer the modular organization of genes from genome-scale data. RESULTS: We introduce a novel approach for the identification of modules based on the persistence of isolated gene groups within an evolving graph process. First, the underlying genomic data is summarized in the form of ranked gene-gene relationships, thereby accommodating studies that quantify the relevant biological relationship directly or indirectly. Then, the observed gene-gene relationship ranks are viewed as the outcome of a random graph process and candidate modules are given by the identifiable subgraphs that arise during this process. An isolation index is computed for each module, which quantifies the statistical significance of its survival time. CONCLUSIONS: The Miso (module isolation) method predicts gene modules from genomic data and the associated isolation index provides a module-specific measure of confidence. Improving on existing alternative, such as graph clustering and the global pruning of dendrograms, this index offers two intuitively appealing features: (1) the score is module-specific; and (2) different choices of threshold correlate logically with the resulting performance, i.e. a stringent cutoff yields high quality predictions, but low sensitivity. Through the analysis of yeast phenotype data, the Miso method is shown to outperform existing alternatives, in terms of the specificity and sensitivity of its predictions

Asteroids' physical models from combined dense and sparse photometry and scaling of the YORP effect by the observed obliquity distribution

The larger number of models of asteroid shapes and their rotational states derived by the lightcurve inversion give us better insight into both the nature of individual objects and the whole asteroid population. With a larger statistical sample we can study the physical properties of asteroid populations, such as main-belt asteroids or individual asteroid families, in more detail. Shape models can also be used in combination with other types of observational data (IR, adaptive optics images, stellar occultations), e.g., to determine sizes and thermal properties. We use all available photometric data of asteroids to derive their physical models by the lightcurve inversion method and compare the observed pole latitude distributions of all asteroids with known convex shape models with the simulated pole latitude distributions. We used classical dense photometric lightcurves from several sources and sparse-in-time photometry from the U.S. Naval Observatory in Flagstaff, Catalina Sky Survey, and La Palma surveys (IAU codes 689, 703, 950) in the lightcurve inversion method to determine asteroid convex models and their rotational states. We also extended a simple dynamical model for the spin evolution of asteroids used in our previous paper. We present 119 new asteroid models derived from combined dense and sparse-in-time photometry. We discuss the reliability of asteroid shape models derived only from Catalina Sky Survey data (IAU code 703) and present 20 such models. By using different values for a scaling parameter cYORP (corresponds to the magnitude of the YORP momentum) in the dynamical model for the spin evolution and by comparing synthetics and observed pole-latitude distributions, we were able to constrain the typical values of the cYORP parameter as between 0.05 and 0.6.Comment: Accepted for publication in A&A, January 15, 201