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首页> 外文期刊>Journal of Biomolecular Structure and Dynamics >3D descriptors calculation and conformational search to investigate potential bioactive conformations, with application in 3D-QSAR and virtual screening in drug design
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3D descriptors calculation and conformational search to investigate potential bioactive conformations, with application in 3D-QSAR and virtual screening in drug design

机译:3D描述符计算和构象搜索以调查潜在的生物活性构象,在3D-QSAR和虚拟筛选中的应用中的应用

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The knowledge of the bioactive conformation for an active hit is relevant because of the easier interpretation and the general quality of the recognition models of protein and ligand. With the aim of investigating potential bioactive conformations without previous structural knowledge of the molecular target, we present herewith a protocol' that could be used which includes generation of low-energy conformations, calculations of tridimensional descriptors and investigation of structural similarity via principal component analysis. The protocol was used in the search for potential bioactive conformations. An initial selection of targets was made from a set of protein-ligand complexes with structure deposited in the Protein Data Bank, which was systematically filtered by lead-like rules, resulting in 45 ligands of 8 important therapeutic targets. After extensive optimization of the protocol and parameters of both OMEGA and Pentacle softwares, the best results were obtained for series of compounds such as the beta-trypsin and urokinase inhibitors, which are more structurally related among each other, inside the respective therapeutic class. Future improvements of the protocol, including a suitable choice and combination of robust 3D descriptors, could yield more reliable and less restrictive results, with general and diverse applications in drug design, in particular for improving the 3D-QSAR methodologies as well as virtual screening experiments for a more reliable selection of new lead compounds for different molecular targets.
机译:由于蛋白质和配体的识别模型的识别模型的易于解释和一般质量,所以对主动命中的生物活性构象的了解是相关的。目的是在没有先前的分子靶标识的情况下研究潜在的生物活性构象,我们在这里展示了可以使用的协议,其包括产生低能量构象的产生,通过主要成分分析来计算阶段描述符的计算和结构相似性的调查。该协议用于搜索潜在的生物活性构象。靶向靶的初始选择是由一组蛋白质 - 配体复合物制成,其中具有沉积在蛋白质数据库中的结构,通过铅状规则系统地过滤,导致8种重要的治疗靶标的45个配体。在广泛优化ω和五孔软件的方案和参数之后,获得了一系列化合物,例如β-胰蛋白酶和尿激酶抑制剂的一系列化合物,其在相应的治疗阶层内部更具结构性相关。协议的未来改进,包括适当的选择和鲁棒3D描述符的组合,可以产生更可靠和更少的限制性结果,以及在药物设计中的一般和多样化的应用,特别是用于改善3D-QSAR方法以及虚拟筛选实验对于更可靠地选择不同分子靶标的新铅化合物。

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