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首页> 外文学位 >Computational simulations of protein-ligand molecular recognition via enhanced samplings, free energy calculations and applications to structure-based drug design.
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Computational simulations of protein-ligand molecular recognition via enhanced samplings, free energy calculations and applications to structure-based drug design.

机译：蛋白质-配体分子识别的计算模拟，包括增强采样，自由能计算以及在基于结构的药物设计中的应用。

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摘要

The objective of this dissertation is to understand the underlying intermolecular interactions between disease-causing proteins and anti-disease small-molecule drugs (also known as ligands) through computational methodologies. The insight gained into protein-ligand molecular recognition in terms of structural and energetic complementarities can be rationally utilized for efficient drug discovery, i.e., structure-based drug design (SBDD), to treat diseases caused by specific proteins, including cancers caused by dimerization of survivin (Chapter 2) and of STAT3 (Chapters 4 and 5), and a degenerative, cataract-inducing eye disease caused by misfolding of gammaS-crystallin (Chapter 3).;Protein structures are not static in the body; thus, a single structure cannot fully represent a protein's characteristics. Proteins in aqueous environments have ensemble structures that range from small-scale variations, such as ligand-induced conformational changes (Chapter 2), to large-scale variations, such as protein unfolding and denaturation (Chapter 3). A non-Boltzmann ensemble distribution ('Generalized Boltzmann distribution') can be realized through enhanced sampling methods which help to overcome the pre-existing free energy barriers along the potential energy surface, allowing a wide range of protein structure conformations to be sampled. The extraction of an essential reaction coordinate that constitutes a protein reaction pathway, followed by the construction of a Potential of Mean Force (PMF: the free energy landscape as a function of a selected reaction coordinate), can be utilized for conformational mapping to elucidate the protein's transition structure and thus understand its mechanism.;For candidate ligands to become effective drugs, they should bind to specific protein binding sites with sufficient affinity while controlling the biological reactions of interest. Therefore, estimating the binding energy of a ligand is a crucial metric in assessing a drug's potency. Furthermore, the predicted binding energy should be sufficiently accurate to discriminate the subtle differences associated with its characteristic specificity among similar candidate ligands, as medicinal chemistry usually deals with libraries that contain a large number of chemical compounds. The characterization of the intermolecular forces required for protein-ligand recognition via classical force fields (Chapter 4) and quantum mechanical force fields (Chapter 5) by extracting amino acid residue-by-residue contributions to the ligand binding energy has enriched the quantitative information that can be used for SBDD.
机译：本文的目的是通过计算方法了解致病蛋白与抗疾病小分子药物（也称为配体）之间的潜在分子间相互作用。从结构和能量互补性方面对蛋白质配体分子识别的认识可以合理地用于有效的药物发现，即基于结构的药物设计（SBDD），以治疗由特定蛋白质引起的疾病，包括由二聚体引起的癌症。 Survivin（第2章）和STAT3（第4和5章），以及由γS-crystallin错折叠引起的退化性白内障诱导眼病（第3章）。因此，单一结构不能完全代表蛋白质的特性。水性环境中的蛋白质具有从小规模变化（如配体诱导的构象变化（第2章））到大规模变化（如蛋白质解折叠和变性）（第3章）的集合结构。可以通过增强的采样方法实现非玻尔兹曼系谱分布（“广义玻尔兹曼分布”），这有助于克服沿势能面预先存在的自由能垒，从而可以对各种蛋白质结构构象进行采样。提取构成蛋白质反应途径的基本反应坐标，然后构建平均力势（PMF：自由能态势作为所选反应坐标的函数），可用于构象作图以阐明为了使候选配体成为有效的药物，它们应以足够的亲和力结合特定的蛋白质结合位点，同时控制感兴趣的生物反应。因此，估计配体的结合能是评估药物效力的关键指标。此外，预测的结合能应足够准确，以区别相似候选配体之间与其特征特异性相关的细微差别，因为药物化学通常处理包含大量化合物的文库。通过提取氨基酸对残基的配体结合能的贡献，通过经典力场（第4章）和量子机械力场（第5章）对蛋白质-配体识别所需的分子间力进行表征，从而丰富了定量信息，可以用于SBDD。

著录项

  • 作者

    Park, In-Hee.;

  • 作者单位

    The Ohio State University.;

  • 授予单位 The Ohio State University.;
  • 学科 Chemistry Physical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 204 p.
  • 总页数 204
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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