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Ubr1 and Ubr2 constitute a novel protein quality control pathway in the cytosol.

机译:Ubr1和Ubr2构成了细胞质中新型的蛋白质质量控​​制途径。

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摘要

The problem of inferring the function of a protein in the context of the complex network of interactions is one of the most crucial challenges faced by Computational Biology today. Knowing the binding partners of proteins is an essential step to untangle the web of functional relationships that control cellular processes, and the identification and the characterization of a protein binding site represent an important step to achieve this goal. Some of the most widely used techniques that have been developed by the bioinformatics community over the years are discussed here, together with their limitations and applicability range. This thesis introduces a framework to perform binding site identification on protein structures by means of an energy-based approach based on the concept of Molecular Interaction Fields (MIFs). The approach has been validated on a large set of bound and unbound protein structures, and a specific application of binding site identification in the context of reverse virtual screening is discussed. The advantage of using chemically specific probes to compute the MIFs is illustrated by applying the binding site identification procedure to phosphorylated ligands. Furthermore, an improved version of the energy-based binding site identification approach that incorporates evolutionary information is presented, emphasizing its advantage in situations where the energy-based signal is weak. As an attempt to move beyond the problem of binding site identification, a methodology that can be applied to infer the bound conformation of a protein starting from an unbound form is introduced. Taken together, the results presented in this work indicate that the energy-based approach with multiple probes MIFs provides a versatile framework to carry out binding site identification and hint to the possibility of identifying the bound form of structures that undergo large conformational changes. Furthermore, the problem of predicting the type of ligand that a binding site can accommodate lies among the future challenges that could benefit from the methodology described here.
机译:在复杂的相互作用网络中推断蛋白质功能的问题是当今计算生物学面临的最关键的挑战之一。了解蛋白质的结合伴侣是解开控制细胞过程的功能关系网的必不可少的步骤,蛋白质结合位点的鉴定和表征是实现此目标的重要步骤。本文讨论了生物信息学界多年来开发的一些最广泛使用的技术,以及它们的局限性和适用范围。本文提出了一种基于分子相互作用场(MIF)概念的基于能量的方法,对蛋白质结构进行结合位点识别的框架。该方法已在一大批结合和未结合的蛋白质结构上得到验证,并讨论了在反向虚拟筛选中结合位点识别的具体应用。通过将结合位点鉴定程序应用于磷酸化的配体,可以说明使用化学特异性探针计算MIF的优势。此外,提出了一种结合了进化信息的基于能量的结合位点识别方法的改进版本,强调了其在基于能量的信号微弱的情况下的优势。为了超越结合位点识别的问题的尝试,引入了可用于从未结合形式开始推断蛋白质的结合构象的方法。两者合计,这项工作中提出的结果表明,具有多个探针MIF的基于能量的方法提供了进行结合位点鉴定的通用框架,并暗示了鉴定经历大构象变化的结构的结合形式的可能性。此外,预测结合位点可以容纳的配体类型的问题还存在于未来的挑战中,这些挑战可以从此处描述的方法中受益。

著录项

  • 作者

    Nillegoda, N.M. Nadinath B.;

  • 作者单位

    Mount Sinai School of Medicine of New York University.;

  • 授予单位 Mount Sinai School of Medicine of New York University.;
  • 学科 Biology Molecular.;Biology Cell.;Biology Genetics.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 176 p.
  • 总页数 176
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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