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Development and Application of Computational Drug Design Methods Against Microbial Pathogen Enzymes.

机译:抗微生物病原体酶的计算药物设计方法的开发和应用。

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摘要

The overall objective of this research is to develop new computational protocols that address some of the current challenges in computational drug design, and to use them in the identification of novel small molecules that inhibit enzymes essential for viral or bacterial replication and survival. In this dissertation, we target a cysteine protease from the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and a condensing enzyme from the B. anthracis fatty acid biosynthesis pathway. The SARS-CoV 3-Chymotrypsin-like protease (3CLpro), vital for SARS-CoV replication, and the condensing enzyme, beta-Ketoacyl-acyl carrier protein synthase III (FabH), essential for the B. anthracis fatty acid biosynthesis pathway, serve as selective targets for novel antiviral and antibacterial drug design, respectively.;We have developed a structure-based virtual screening protocol to identify novel small molecules active against SARS CoV 3CLpro. Through the use of a tiered docking approach and consensus scoring function we have obtained considerable enrichment of true positives compared to the individual scoring functions. SARS-CoV 3CLpro shows large movements of active site loops as well as side chain flexibility of the binding pocket residues. In our screening protocol, we have incorporated a combination of docking and pharmacophore based approaches to overcome some of the challenges of protein flexibility. Nearly half a million compounds from the ZINC database were screened using the developed protocol, leading to the identification of several compounds with micromolar activity. The structure activity relationship using the active and inactive compounds was also developed to provide guidance for compound optimization.;Recently, additional experimental inhibitory data against 3CLpro was obtained from experimental screening of an in-house compound collection in our laboratory, and the public release on PubChem of results from an HTS campaign through the NIH Molecular Libraries Program. We used this data to re-validate our structure-based screening protocol. Additionally, the availability of sufficient ligand data allowed us to use a ligand-based approach to find novel scaffolds active against 3CLpro. The structural determinants for FabH-inhibitor binding are poorly understood. We first characterized the FabH binding pocket using computational solvent mapping and identified hot spot residues making dominant contributions to the binding affinity. The results show good agreement with the protein-inhibitor interactions observed in the co-crystal structures. These insights can serve as a guideline for designing and optimizing lead compounds. We also developed a reliable and computationally efficient protocol for virtual screening against FabH. A comprehensive pharmacophore based screening protocol was developed taking advantage of all the structure and inhibitor information available in the literature. Three different approaches were used for pharmacophore query generation for screening the ZINC library. Three compounds were selected and their binding to the baFabH active site was confirmed using fluorescence quenching experiments. The structural and energetic analysis of the putative ligands suggest that they bind to the key hot spots identified in this work.
机译:这项研究的总体目标是开发新的计算协议,以解决计算药物设计中的一些当前挑战,并将其用于鉴定抑制病毒或细菌复制和存活所必需的酶的新型小分子。本文针对严重急性呼吸系统综合症冠状病毒(SARS-CoV)中的半胱氨酸蛋白酶和炭疽芽孢杆菌(B. anthracis 脂肪酸的生物合成途径,分别作为新型抗病毒和抗菌药物设计的选择性靶标。;我们已经开发了一种基于结构的虚拟筛选方案,以鉴定对SARS CoV 3CLpro有活性的新型小分子。通过使用对接分层方法和共识评分功能,与单个评分功能相比,我们获得了相当多的真实肯定。 SARS-CoV 3CLpro显示出活性位点环的大量移动以及结合口袋残基的侧链柔性。在我们的筛选方案中,我们结合了基于对接和药效团的方法,以克服蛋白质灵活性方面的一些挑战。使用开发的方案从ZINC数据库中筛选了近一百万种化合物,从而鉴定出了几种具有微摩尔活性的化合物。还开发了使用活性和非活性化合物的结构活性关系,为化合物的优化提供了指导。;最近,通过对我们实验室内部化合物的实验筛选,并公开发布了针对3CLpro的其他实验抑制数据。通过NIH分子图书馆计划进行的HTS活动的结果的PubChem。我们使用这些数据重新验证了基于结构的筛选方案。此外,足够的配体数据的可用性使我们能够使用基于配体的方法来发现对3CLpro具有活性的新型支架。 FabH抑制剂结合的结构决定因素了解甚少。我们首先使用计算溶剂作图法表征了FabH结合口袋,并确定了对结合亲和力起主要作用的热点残基。结果显示与在共晶体结构中观察到的蛋白质-抑制剂相互作用有很好的一致性。这些见解可以作为设计和优化铅化合物的指南。我们还针对FabH虚拟筛选开发了可靠且计算效率高的协议。利用文献中所有可用的结构和抑制剂信息,开发了一种基于药效团的全面筛选方案。三种不同方法用于药效团查询生成,以筛选ZINC库。选择了三种化合物,并使用荧光猝灭实验证实了它们与baFabH活性位点的结合。推定配体的结构和能量分析表明它们与这项工作中确定的关键热点结合。

著录项

  • 作者

    Mittal, Anuradha.;

  • 作者单位

    University of Illinois at Chicago.;

  • 授予单位 University of Illinois at Chicago.;
  • 学科 Biology Microbiology.;Biology Bioinformatics.;Health Sciences Pharmacy.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 193 p.
  • 总页数 193
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 遥感技术;
  • 关键词

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