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A coarse-grained view of protein-protein recognition.

机译:蛋白质识别的粗粒度视图。

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摘要

One of the most important characteristics of proteins is their ability to specifically interact with other proteins and with other types of molecules to build supramolecular assemblies in order to perform different kinds of functions. Protein-protein interaction has been a subject of study in several sciences including biochemistry, structural biology, and computational biophysics/biology. One limitation that delays our understanding of molecular recognition is the lack of high-resolution three-dimensional structures of the protein-protein complexes. Because of this, the methods for computational prediction have gained popularity and importance but in many cases, the predicted complex is not accurate. Predicting the native three-dimensional conformation of protein-protein complexes still remains a big challenge.;Most biological processes in the cell involve a huge number of atoms and happen at time-scales that are frequently beyond the current limits of classical atomistic simulations. The ELNEDIN approach is a new and powerful coarse-grained representation with the ability to overcome size and time limits without deforming the overall shape of a protein. Previous studies have shown that the quality of the ELNEDIN scaffold influences the ability of the modeled proteins to experience structural transitions and to associate and form a stable complex.;The main objective of this thesis is to test if the ELNEDIN approach is able to discriminate native interfaces from non-native. The Barnase/Barstar complex, the RNase/Barnase complex and the Ubiquitin/Ubiquitin ligase complex were chosen to test this hypothesis. Each individual protein model was simulated using the ELNEDIN approach and the potential of mean force for the dissociation of the complex was calculated. Our results show: 1) It is possible to obtain accurate energy-profiles using the ELNEDIN approach. 2) The shape of the free energy landscape around a protein receptor has a funnel-like shape where the bottom of the funnel is the global minimum and it starts to increase smoothly. 3) The solvent plays an important role in the shape of the free energy profile. 4) The ELNEDIN approach is able to recognize the native conformation for hydrophilic interfaces. To further support the last result, an additional complex was chosen: the Nuclease A/Nuclease inhibitor A complex.
机译:蛋白质最重要的特征之一是它们与其他蛋白质以及与其他类型的分子特异性相互作用以构建超分子组装体以执行不同种类功能的能力。蛋白质-蛋白质相互作用一直是包括生物化学,结构生物学和计算生物物理学/生物学在内的几门科学的研究主题。延迟我们对分子识别的理解的一个限制是缺乏蛋白质-蛋白质复合物的高分辨率三维结构。因此,用于计算预测的方法已获得普及和重要性,但是在许多情况下,预测的复杂度并不准确。预测蛋白质-蛋白质复合物的天然三维构象仍然是一个很大的挑战。;细胞中的大多数生物过程涉及大量原子,并且发生的时间尺度通常超出了经典原子模拟的当前范围。 ELNEDIN方法是一种新的强大的粗粒度表示法,能够克服大小和时间限制,而不会变形蛋白质的整体形状。以前的研究表明,ELNEDIN支架的质量影响建模的蛋白质经历结构转变以及缔合和形成稳定复合物的能力。本论文的主要目的是测试ELNEDIN方法是否能够区分天然蛋白质。来自非本地的接口。选择Barnase / Barstar复合物,RNase / Barnase复合物和Ubiquitin / Ubiquitin连接酶复合物来检验该假设。使用ELNEDIN方法模拟每个单独的蛋白质模型,并计算了复合物解离的平均力。我们的结果表明:1)使用ELNEDIN方法可以获得准确的能量分布。 2)蛋白质受体周围的自由能态势的形状呈漏斗状,其中漏斗的底部为全局最小值,并且开始平滑增加。 3)溶剂在自由能曲线的形状中起着重要作用。 4)ELNEDIN方法能够识别亲水性界面的天然构象。为了进一步支持最后的结果,选择了另一种复合物:核酸酶A /核酸酶抑制剂A复合物。

著录项

  • 作者单位

    City University of New York.;

  • 授予单位 City University of New York.;
  • 学科 Chemistry Biochemistry.;Biology Bioinformatics.;Biophysics General.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 172 p.
  • 总页数 172
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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