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De novo protein structure modeling and energy function design.

机译：从头蛋白质结构建模和能量功能设计。

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摘要

The two major challenges in protein structure prediction problems are (1) the lack of an accurate energy function and (2) the lack of an efficient search algorithm. A protein energy function accurately describing the interaction between residues is able to supervise the optimization of a protein conformation, as well as select native or native-like structures from numerous possible conformations. An efficient search algorithm must be able to reduce a conformational space to a reasonable size without missing the native conformation. My PhD research studies focused on these two directions.;A protein energy function---the distance and orientation dependent energy function of amino acid key blocks (DOKB), containing a distance term, an orientation term, and a highly packed term---was proposed to evaluate the stability of proteins. In this energy function, key blocks of each amino acids were used to represent each residue; a novel reference state was used to normalize block distributions. The dependent relationship between the orientation term and the distance term was revealed, representing the preference of different orientations at different distances between key blocks. Compared with four widely used energy functions using six general benchmark decoy sets, the DOKB appeared to perform very well in recognizing native conformations. Additionally, the highly packed term in the DOKB played its important role in stabilizing protein structures containing highly packed residues. The cluster potential adjusted the reference state of highly packed areas and significantly improved the recognition of the native conformations in the ig_structal data set. The DOKB is not only an alternative protein energy function for protein structure prediction, but it also provides a different view of the interaction between residues.;The top-k search algorithm was optimized to be used for proteins containing both alpha-helices and beta-sheets. Secondary structure elements (SSEs) are visible in cryo-electron microscopy (cryo-EM) density maps. Combined with the SSEs predicted in a protein sequence, it is feasible to determine the topologies referring to the order and direction of the SSEs in the cryo-EM density map with respect to the SSEs in the protein sequence. Our group member Dr. Al Nasr proposed the top-k search algorithm, searching the top-k possible topologies for a target protein. It was the most effective algorithm so far. However, this algorithm only works well for pure a-helix proteins due to the complexity of the topologies of beta-sheets. Based on the known protein structures in the Protein Data Bank (PDB), we noticed that some topologies in beta-sheets had a high preference; on the contrary, some topologies never appeared. The preference of different topologies of beta-sheets was introduced into the optimized top-k search algorithm to adjust the edge weight between nodes. Compared with the previous results, this optimization significantly improved the performance of the top-k algorithm in the proteins containing both alpha-helices and beta-sheets.
机译：蛋白质结构预测问题中的两个主要挑战是（1）缺乏准确的能量函数和（2）缺乏有效的搜索算法。准确描述残基之间相互作用的蛋白质能量功能能够监督蛋白质构象的优化，以及从众多可能的构象中选择天然或类似天然的结构。有效的搜索算法必须能够将构象空间减小到合理的大小，而不会丢失本机构象。我的博士研究专注于这两个方向：蛋白质能量函数-氨基酸键区（DOKB）的距离和方向依赖性能量函数，其中包含距离项，方向项和高度堆积的项-提出了评估蛋白质稳定性的方法。在这种能量函数中，每个氨基酸的关键嵌段被用来代表每个残基。一种新颖的参考状态用于规范块分布。揭示了方位项和距离项之间的依存关系，代表了关键块之间不同距离处的不同方位的偏好。与使用六个通用基准诱饵集的四个广泛使用的能量函数相比，DOKB在识别天然构象方面表现非常出色。此外，DOKB中的高度堆积术语在稳定包含高度堆积残基的蛋白质结构中起着重要作用。簇势调整了高度堆积区域的参考状态，并显着提高了ig_structal数据集中天然构象的识别能力。 DOKB不仅是蛋白质结构预测的替代蛋​​白质能量函数，而且还提供了残基之间相互作用的另一种观点。top-k搜索算法经过优化，可用于同时包含α-螺旋和β-螺旋的蛋白质。床单。二级结构元素（SSE）在低温电子显微镜（cryo-EM）密度图中可见。结合蛋白质序列中预测的SSE，确定相对于蛋白质序列中的SSE而言，参考冷冻EM密度图中SSE的顺序和方向确定拓扑是可行的。我们的小组成员Al Nasr博士提出了top-k搜索算法，用于搜索目标蛋白的top-k可能拓扑。这是迄今为止最有效的算法。然而，由于β-折叠的拓扑结构的复杂性，该算法仅对纯α-螺旋蛋白有效。基于蛋白质数据库（PDB）中已知的蛋白质结构，我们注意到β-sheets中的某些拓扑具有较高的优先级。相反，某些拓扑从未出现过。在优化的top-k搜索算法中引入了不同的beta-sheet拓扑偏好，以调整节点之间的边缘权重。与以前的结果相比，此优化显着改善了同时包含α-螺旋和β-折叠的蛋白质中top-k算法的性能。

著录项

  • 作者

    Chen, Lin.;

  • 作者单位

    Old Dominion University.;

  • 授予单位 Old Dominion University.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 139 p.
  • 总页数 139
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 古生物学;
  • 关键词

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