Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

U-Pb zircon (ID-TIMS and SHRIMP) evidence for the early ordovician intrusion of metagranites in the late Proterozoic Canaveilles Group of the Pyrenees and the Montagne Noire (France)

Abstract: Depending on the quality of the zircon grains available for analysis, two methods may be used to date igneous rock emplacement, namely U-Pb TIMS with isotope dilution or in situ U-Pb SIMS (SHRIMP). Both methods have been used to determine, in a precise and accurate manner, the emplacement age of the granitic protolith of the various orthogneisses in the Pyrenean Axial Zone. More specifically, four representative samples of G1, G2 and a "transition gneiss" yielded reliable datings with an average age of 473 ± 4 Ma for each sample. The surrounding sediments of the Canaveilles Group were constrained by zircon grains from interlayered metarhyodacite and dated at 581 ± 10 Ma using the SHRIMP method, clearly giving this group a late Proterozoic (Vendian) age. Finally, the Somail orthogneiss of the Montagne Noire, equivalent to that of the Canigou, yielded an age of 471 ± 4 Ma with the in situ U-Pb method, which is identical to the dating of the Pyrenean samples. In addition, most of the studied orthogneisses recorded a wide range of significant concordant inherited ages spanning from early Archaean (3.5 Ga) to Pan-African/Cadomian (600-800 Ma). Bearing in mind the calc-alkaline affinity of the studied rocks, this work demonstrates the huge contrast between the active Gondwana margin in the north ("South European terrane") and the remarkably homogeneous continental plate that existed from Arabia to Morocco during the Ordovician.

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction

Abstract: Predicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets. We propose a new method to quantify the similarity between binding pockets, and explore its relevance for ligand prediction. We represent each pocket by a cloud of atoms, and assess the similarity between two pockets by aligning their atoms in the 3D space and comparing the resulting configurations with a convolution kernel. Pocket alignment and comparison is possible even when the corresponding proteins share no sequence or overall structure similarities. In order to predict ligands for a given target pocket, we compare it to an ensemble of pockets with known ligands to identify the most similar pockets. We discuss two criteria to evaluate the performance of a binding pocket similarity measure in the context of ligand prediction, namely, area under ROC curve (AUC scores) and classification based scores. We show that the latter is better suited to evaluate the methods with respect to ligand prediction, and demonstrate the relevance of our new binding site similarity compared to existing similarity measures. This study demonstrates the relevance of the proposed method to identify ligands binding to known binding pockets. We also provide a new benchmark for future work in this field. The new method and the benchmark are available at http://cbio.ensmp.fr/paris/ .

Solubility Parameters of Nonelectrolyte Organic Compounds: Determination Using Quantitative Structure–Property Relationship Strategy

Abstract: The solubility parameter is considered to be a significant parameter for the chemical industry. In this study, the quantitative structure–property relationship (QSPR) method is applied to develop three models for determination of the solubility parameters of pure nonelectrolyte organic compounds at 298.15 K and atmospheric pressure. To propose comprehensive, reliable, and predictive models, about 1400 data belonging to experimental solubility parameter values of various nonelectrolyte organic compounds are studied. The genetic function approximation (GFA) mathematical approach is applied for selection of proper model parameters (molecular descriptors) and to develop a linear QSPR model. To study the nonlinear relations between the selected molecular descriptors and the solubility parameter, two approaches are pursued: the three-layer feed forward artificial neural networks (3FFANN) and the least square support vector machine (LSSVM). Furthermore, the Levenberg–Marquardt (LM) and genetic algorithm (GA) opt...