Computational Methods for Wrapping and Threading Remote Protein Homologs

包裹和穿线远程蛋白质同系物的计算方法

• 批准号: 7624601
• 负责人: LENORE Jennifer COWEN
• 金额: $ 26.28万
• 依托单位: TUFTS UNIVERSITY MEDFORD
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7624601
• 关键词: Allergens; Amino Acid Sequence; Amino Acids; Bacterial Adhesins; Benchmarking; Botulism; Coin; Computing Methodologies; Dependency; Detection; Distant; Elements; Employee Strikes; Family; Generations; Hand; Homologous Gene; Homologous Protein; Human; Internet; Intervention; Lead; Libraries; Maps; Medical; Methods; Metric; Modeling; Pathogenesis; Pectate lyase; Penetration; Peptide Sequence Determination; Pertussis; Pollen; Protein Family; Proteins; Reporting; Rest; Rosa; Running; Sequence Alignment; Sequence Homology; Set protein; Shapes; Source Code; Speed; Structure; Surface; Testing; Toxin; Training Programs; Trefoil; Validation; Vertebral column; Virulence; Work; abstracting; base; beta pleated sheet; comparative; design; flexibility; improved; markov model; member; microbial; novel; novel strategies; pathogen; programs; protein function; protein structure; protein structure prediction; public health relevance; research study; three dimensional structure; web site

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.

描述（由申请人提供）：“轻链区”是Burkhard Rost创造的术语，指与已知结构的蛋白质序列相似性足够低的远距离蛋白质同源物，以致同源物的计算检测变得相当具有挑战性。我们建议构建新的线程方法，扩展蛋白质结构预测和序列/结构比对进一步进入“黄昏区”。我们攻击的问题在两个方面：第一，我们打算扩展的“包装”的方法，我们设计成功地攻击两个硬特殊情况下，这个问题的其他SCOP超家族。这涉及到将能量函数的包装部分中的成对依赖关系转换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对齐的序列以缩小搜索空间;然后使用基于主链依赖旋转异构体库的更复杂的能量函数进行侧链包装。第二，我们的β-螺旋和三叶折叠程序使用人工干预来构建核心结构模板，我们将序列包裹在模板上，以预测它们是否可以折叠成这些结构。为了构建具有PDB的合理折叠文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，所有蛋白质都属于相同的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，以至于不能捕获远程同源物。我们提出了一种新的多结构比对，增加了几何灵活性，以捕捉更遥远的同源物之间的相似性，从中可以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速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-螺旋折叠与微生物病原体毒力之间的一种以前未知的关系。BetaWrap程序的一个惊人预测是，预测了许多表面粘附素、毒素和人类病原体的其他识别/穿透蛋白的β-螺旋折叠。我们的预测，一个主要的花粉过敏原形成β-螺旋形状最近刚刚得到实验证实。公共卫生关系：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，从而加速医学发现。该提议特别针对改善β-结构基序的预测，其中包括许多对细菌发病机制重要的蛋白质家族，其代表从百日咳毒素（β-螺旋）到肉毒杆菌毒素（β-三叶草）。我们的第一个β-螺旋预测程序BetaWrap已经发现了β-螺旋折叠与微生物病原体毒力之间的一种以前未知的关系。BetaWrap程序的一个惊人预测是，预测了许多表面粘附素、毒素和人类病原体的其他识别/穿透蛋白的β-螺旋折叠。我们的预测，一个主要的花粉过敏原形成β-螺旋形状最近刚刚得到实验证实。