Computational Methods for Wrapping and Threading Remote Protein Homologs

描述(由申请人提供)：“暮光区”是由Burkhard Rost创造的一个术语，指的是与已知结构的蛋白质的序列相似性足够低的远程蛋白质同系物，使得同源性的计算检测变得非常具有挑战性。我们建议构建新的线索方法，将蛋白质结构预测和序列/结构比对进一步扩展到“暮光区”。我们从两个方面解决这个问题：首先，我们打算将我们设计的成功攻击该问题的两个困难特殊情况的“包装”方法扩展到其他SCOP超家族。这包括将能量函数的包装部分中的成对依赖关系投射到马尔可夫随机场的一般框架中，同时仍然允许它们包装多个比对的序列以缩小搜索空间；然后使用基于骨架依赖的旋转体文库的更复杂的能量函数来进行侧链包装。其次，我们的β-螺旋和三叶折叠程序使用人类干预来构建核心结构模板，我们在核心结构模板上包装序列，以预测它们是否可以折叠到这些结构中。为了为PDB构建一个具有合理折叠库覆盖率的通用线程程序，我们需要解决从一组蛋白质自动构建结构模板的挑战，例如，这些蛋白质都属于同一SCOP超家族。大多数当前的线程程序训练得太接近一个特定结构的主干，无法捕获远程同源基因。我们提出了一种新颖的多结构比对方法，它增加了几何灵活性来捕捉更远的同源物之间的相似性，从而可以从中抽象出更一般的核心模板。更好的计算蛋白质结构预测在加快医学发现方面的应用是众所周知的。我们的第一个β-螺旋预测程序BetaWrap已经发现了β-螺旋折叠和微生物病原体毒力之间的一种以前未知的关系。BetaWrap程序的一个引人注目的预测是，许多人类病原体的表面粘附素、毒素和其他识别/穿透蛋白都预测到了β-螺旋折叠。我们关于一种主要的花粉过敏原形成β-螺旋形状的预测最近刚刚在实验上得到证实。公共卫生相关性：计算蛋白质结构预测的进展可以帮助指导蛋白质功能的预测，从而加快医学发现。这项建议特别针对改善对β结构基序的预测，其中包括许多对细菌发病重要的蛋白质家族，代表从百日咳毒素(β螺旋)到肉毒毒素(β三叶草)。我们的第一个β-螺旋预测程序BetaWrap已经发现了β-螺旋折叠和微生物病原体毒力之间的一种以前未知的关系。BetaWrap程序的一个引人注目的预测是，许多人类病原体的表面粘附素、毒素和其他识别/穿透蛋白都预测到了β-螺旋折叠。我们关于一种主要的花粉过敏原形成β-螺旋形状的预测最近刚刚在实验上得到证实。

DESCRIPTION (provided by applicant): The "twillight zone" is a term coined by Burkhard Rost to refer to remote protein homologs whose sequence similarity to proteins of known structure is sufficiently low that computational detection of the homology becomes quite challenging. We propose to construct new threading methods that extend protein structure prediction and sequence/structure alignments further into the "twilight zone". We attack the problem on two fronts: first, we intend to extend the "wrapping" methods we designed to successfully attack two hard special cases of this problem to other SCOP superfamilies. This involves casting the pairwise dependencies in the wrapping portion of the energy function into the general framework of Markov Random Fields, while still allowing them to wrap multiple aligned sequences to narrow the search space; then using a more sophisticated energy function based on a backbone-dependent rotamer library for sidechain packing. Second, our programs for the beta-helix and trefoil folds used human intervention to construct the core structural templates on which we are wrapping the sequences to predict whether they could fold into these structures or not. In order to construct a general threading program with reasonable fold library coverage for the PDB, we need to solve the challenge of automating the construction of a structural template from a set of proteins that, for example, all belong to the same SCOP superfamily. Most current threading programs train too closely to a backbone of one particular structure to be able to capture remote homologs. We propose a novel multiple structure alignment that adds geometric flexibility to capture similarities between more distant homologs, from which more general core templates can be abstracted. The applications of better computational protein structure prediction to speed up medical discovery are well-known. BetaWrap, our first beta-helix prediction program, already uncovered a previously-unknown relationship between the beta-helix fold and the virulence of microbial pathogens. A striking prediction of the BetaWrap program is that the beta-helix fold is predicted for many surface adhesins, toxins, and other recognition/penetration proteins of human pathogens. Our prediction that a major pollen allergen forms the beta-helix shape has just recently been confirmed experimentally. PUBLIC HEALTH RELEVANCE: Advances in computational protein structure prediction can help guide prediction of protein function, and thus speed medical discovery. This proposal is especially targeted at improving prediction of beta-structural motifs, which include many protein families that are important for bacterial pathogenesis, with representatives from whooping cough toxin (beta-helices) to the botulism toxin (beta-trefoils). BetaWrap, our first beta-helix prediction program, already uncovered a previously-unknown relationship between the beta-helix fold and the virulence of microbial pathogens. A striking prediction of the BetaWrap program is that the beta-helix fold is predicted for many surface adhesins, toxins, and other recognition/penetration proteins of human pathogens. Our prediction that a major pollen allergen forms the beta-helix shape has just recently been confirmed experimentally.