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    NOVEL SELF-ASSEMBLING PEPTIDES AND THEIR USE IN THE FORMATION OF HYDROGELS
    13.
    发明公开
    NOVEL SELF-ASSEMBLING PEPTIDES AND THEIR USE IN THE FORMATION OF HYDROGELS 有权
    SELBST-BILDENDE PEPTIDE UND IHRE VERWENDUNG IN DER HERSTELLUNG VON HYDROGELEN

    公开(公告)号：EP2560689A1

    公开(公告)日：2013-02-27

    申请号：EP11723283.5

    申请日：2011-04-19

    申请人： Universita' Degli Studi Di Milano - Bicocca

    发明人： VESCOVI, Angelo Luigi ,  GELAIN, Fabrizio

    IPC分类号： A61K47/48 ,  C07K14/78 ,  C12N5/00 ,  C07K14/00 ,  B82Y5/00

    CPC分类号： B82Y5/00 ,  B82Y15/00 ,  C07K7/06 ,  C07K7/08 ,  C12N5/0068 ,  C12N2533/50

    摘要： There is described a group of novel self-assembling peptides (SAPs), comprising biotinylated and unbiotinylated sequences, hybrid peptide-peptoid sequences, branched sequences for a total of 48 tested motifs, showing a heterogeneous ensemble of spontaneously self-assembled structures at the nano- and microscale, ranging from short tabular fibers to twisted ribbons, nanotubes and hierarchical self-assembled micrometer-long sheets. Specifically, the SAPs according to the present invention which initially spontaneous assemble, surprisingly form stable solid scaffolds upon exposure to neutral pH buffer. Further these SAPs allow adhesion, proliferation and differentiaton of murine and human neural stem cells and have self-healing propensity. They also did not exert toxic effects in the central nervous system, can stop bleeding and foster nervous regeneration. Therefore, the SAPs according to the present invention are improved biomaterials, a highly valid and useful alternative which may replace the known SAPs, thus overcoming the disadvantages related thereto.

    摘要翻译： 描述了一组新的自组装肽（SAP），其包含生物素化和未生物素化的序列，杂交肽 - 肽类序列，总共48个测试的基序的支链序列，显示在纳米的自发自组装结构的异质组合 - 微尺度，从短片状纤维到扭曲带，纳米管和分级自组装千分尺长片。 具体地，根据本发明的初始自发组装的SAP在暴露于​​中性pH缓冲液时令人惊奇地形成稳定的固体支架。 此外，这些SAP允许鼠和人类神经干细胞的粘附，增殖和分化，并具有自我修复倾向。 他们也没有在中枢神经系统发挥毒性作用，可以止血，促进神经再生。 因此，根据本发明的SAP是改进的生物材料，其是可以替代已知的SAP的高度有效和有用的替代方案，从而克服与之相关的缺点。

    Method of construction and selection of virtual libraries in combinatorial chemistry
    14.
    发明公开
    Method of construction and selection of virtual libraries in combinatorial chemistry 审中-公开
    一种用于在组合化学制备和虚拟存储库的选择过程

    公开(公告)号：EP1628234A1

    公开(公告)日：2006-02-22

    申请号：EP04425416.7

    申请日：2004-06-07

    申请人： Universita' Degli Studi di Milano-Bicocca ,  Consorzio Milano Ricerche

    发明人： Archetti Francesco ,  Zimolo Edoardo Luigi ,  Roggia Luigi ,  Fantucci Piercarlo ,  Gaiji Noura

    IPC分类号： G06F19/00

    CPC分类号： C40B50/02 ,  G06F19/16

    摘要： A method of construction and selection of virtual libraries in combinatorial chemistry is described, comprising the steps of: preparing a three-dimensional structure of a target macromolecule (PROT); determining one or more receptor sites of the macromolecular target (PROT); creating a first virtual library (300) of compounds starting with at least one input library (100;200); calculating a plurality of molecular descriptors for each molecule of the first virtual library (300), generating a second virtual library (400) containing each molecule of the first library (100; 200; 50) and the values of the molecular descriptors; selecting a representative subset (500) of the second virtual library (400); calculating for each molecule belonging to the representative subset a value of a quantity ( E dock ) associated with the formation of a bond between the target macromolecule (PROT) and each molecule belonging to the representative subset (500); and obtaining by way of simulation through "Machine Learning" methods for each molecule of a plurality of molecules of the second virtual library (400) and not belonging to the representative subset (500) a value of the quantity ( E dock ) associated with the formation of a bond between the macromolecular target (PROT) and each molecule of a plurality of molecules not belonging to the representative subset (500).

    摘要翻译： 描述构造和组合化学文库虚拟选择的方法，包括以下步骤：制备一个靶大分子（PROT）的三维结构; 大分子靶的确定性开采的一个或多个受体结合位点（PROT）; 创建化合物的第一虚拟库（300）开始的至少一个输入库（100; 200）; 计算分子描述符的多个用于第一虚拟库（300）的每个分子，生成包含所述第一文库的每个分子中的第二虚拟库（400）（100; 200; 50）和分子描述符的值; 选择所述第二虚拟库（400）的代表子集（500）; 计算对于属于代表子集的量（E坞）的值与目标大分子（PROT）和属于代表子集（500）的每个分子之间的键的形成相关联的每个分子; 和通过对属于代表子集（500）与所述相关联的量（E坞）的值的第二虚拟库（400），而不是分子的多个每分子“机器学习”方法，通过模拟的方式获得 形成大分子靶（PROT）和不属于该代表子集（500）的分子的多个每个分子之间的键的。