Kalva Sukesh, Vadivelan S, Sanam Ramadevi, Jagarlapudi Sarma Arp, Saleena Lilly M
Bioinformation. 2012;8(7):301-8. doi: 10.6026/97320630008301. Epub 2012 Apr 13.
In this study, chemical feature based pharmacophore models of MMP-1, MMP-8 and MMP-13 inhibitors have been developed with the aid of HypoGen module within Catalyst program package. In MMP-1 and MMP-13, all the compounds in the training set mapped HBA and RA, while in MMP-8, the training set mapped HBA and HY. These features revealed responsibility for the high molecular bioactivity, and this is further used as a three dimensional query to screen the knowledge based designed molecules. These pharmacophore models for collagenases picked up some potent and novel inhibitors. Subsequently, docking studies were performed for the potent molecules and novel hits were suggested for further studies based on the docking score and active site interactions in MMP-1, MMP-8 and MMP-13.
在本研究中，借助Catalyst程序包中的HypoGen模块，开发了基于化学特征的基质金属蛋白酶-1（MMP-1）、基质金属蛋白酶-8（MMP-8）和基质金属蛋白酶-13（MMP-13）抑制剂的药效团模型。在MMP-1和MMP-13中，训练集中的所有化合物都映射了氢键受体（HBA）和疏水区域（RA），而在MMP-8中，训练集映射了氢键受体（HBA）和疏水基团（HY）。这些特征揭示了高分子生物活性的原因，并且进一步将其用作三维查询以筛选基于知识设计的分子。这些胶原酶的药效团模型筛选出了一些强效且新颖的抑制剂。随后，对这些强效分子进行了对接研究，并根据MMP-1、MMP-8和MMP-13中的对接分数和活性位点相互作用，提出了新的命中化合物以供进一步研究。
