OpenMI: the essential concepts and their implications for legacy software

International audienceInformation & Communication Technology (ICT) tools such as computational models are very helpful in designing river basin management plans (rbmp-s). However, in the scientific world there is consensus that a single integrated modelling system to support e.g. the implementation of the Water Framework Directive cannot be developed and that integrated systems need to be very much tailored to the local situation. As a consequence there is an urgent need to increase the flexibility of modelling systems, such that dedicated model systems can be developed from available building blocks. The HarmonIT project aims at precisely that. Its objective is to develop and implement a standard interface for modelling components and other relevant tools: The Open Modelling Interface (OpenMI) standard. The OpenMI standard has been completed and documented. It relies entirely on the "pull" principle, where data are pulled by one model from the previous model in the chain. This paper gives an overview of the OpenMI standard, explains the foremost concepts and the rational behind it

Three “hotspots” important for adenosine A2B receptor activation: a mutational analysis of transmembrane domains 4 and 5 and the second extracellular loop

G protein-coupled receptors (GPCRs) are a major drug target and can be activated by a range of stimuli, from photons to proteins. Despite the progress made in the last decade in molecular and structural biology, their exact activation mechanism is still unknown. Here we describe new insights in specific regions essential in adenosine A2B receptor activation (A2BR), a typical class A GPCR. We applied unbiased random mutagenesis on the middle part of the human adenosine A2BR, consisting of transmembrane domains 4 and 5 (TM4 and TM5) linked by extracellular loop 2 (EL2), and subsequently screened in a medium-throughput manner for gain-of-function and constitutively active mutants. For that purpose, we used a genetically engineered yeast strain (Saccharomyces cerevisiae MMY24) with growth as a read-out parameter. From the random mutagenesis screen, 12 different mutant receptors were identified that form three distinct clusters; at the top of TM4, in a cysteine-rich region in EL2, and at the intracellular side of TM5. All mutant receptors show a vast increase in agonist potency and most also displayed a significant increase in constitutive activity. None of these residues are supposedly involved in ligand binding directly. As a consequence, it appears that disrupting the relatively “silent” configuration of the wild-type receptor in each of the three clusters readily causes spontaneous receptor activity

Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel

International audienceMotivation: Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.Results: We modelled the 50% growth inhibition bioassay end-point (GI50) of 17 142 compounds screened against 59 cancer cell lines from the NCI60 panel (941 831 data-points, matrix 93.08% complete) by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data (Tukey’s Honestly Significant Difference, P <0.05). We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines

Кон'юнктурний аналіз розвитку ринку рекреаційних послуг АР Крим

Метою дослідження є кон’юнктурний аналіз розвитку ринку рекреаційних послуг АР Крим та порівняльна оцінка функціонування конкурентоспроможних рекреаційних районів

Rapid Mapping of Landslides in the Western Ghats (India) Triggered by 2018 Extreme Monsoon Rainfall Using a Deep Learning Approach

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in theWestern Ghats of India.We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

BACKGROUND: In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. RESULTS: camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). CONCLUSIONS: Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package