227 research outputs found

    Steenwerck – Pont Lothé

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    Date de l'opération : 1989 (SU) Inventeur(s) : Vanhee C Dans le cadre des travaux liés au tracé du TGV [ (Fig. n°1 : Localisation des zones fouillées), site G] et consécutivement à la découverte de quelques vestiges médiévaux (fossés et matériel céramique), de part et d’autre du chemin vicinal, des sondages complémentaires à ceux déjà réalisés sur l’ensemble de l’emprise à cet endroit ont été entrepris en 1990 sans résultats notables. La majeure partie des structures mises au jour se révèlent..

    Steenwerck – Pont Lothé

    Get PDF
    Date de l'opération : 1989 (SU) Inventeur(s) : Vanhee C Dans le cadre des travaux liés au tracé du TGV [ (Fig. n°1 : Localisation des zones fouillées), site G] et consécutivement à la découverte de quelques vestiges médiévaux (fossés et matériel céramique), de part et d’autre du chemin vicinal, des sondages complémentaires à ceux déjà réalisés sur l’ensemble de l’emprise à cet endroit ont été entrepris en 1990 sans résultats notables. La majeure partie des structures mises au jour se révèlent..

    Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors

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    Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions

    The Artificial Society Analytics Platform

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    Author's accepted manuscriptSocial simulation routinely involves the construction of artificial societies and agents within such societies. Currently there is insufficient discussion of best practices regarding the construction process. This chapter introduces the artificial society analytics platform (ASAP) as a way to spark discussion of best practices. ASAP is designed to be an extensible architecture capable of functioning as the core of many different types of inquiries into social dynamics. Here we describe ASAP, focusing on design decisions in several key areas, thereby exposing our assumptions and reasoning to critical scrutiny, hoping for discussion that can advance debate over best practices in artificial society construction. The five design decisions are related to agent characteristics, neighborhood interactions, evaluating agent credibility, agent marriage, and heritability of personality.acceptedVersio

    Learning a peptide-protein binding affinity predictor with kernel ridge regression

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    We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of accurately predicting the binding affinity of any peptide to any protein. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. On all benchmarks, our method significantly (p-value < 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. The method should be of value to a large segment of the research community with the potential to accelerate peptide-based drug and vaccine development.Comment: 22 pages, 4 figures, 5 table
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