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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

基本信息

批准号：
8076912
负责人：
LENORE Jennifer COWEN
金额：
$ 25.71万
依托单位：
TUFTS UNIVERSITY MEDFORD
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-06-01 至 2013-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8076912
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 Health 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 research study three dimensional structure web site

项目摘要

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）：“暮光地带”是由Burkhard Rost创造的一个术语，指与已知结构的蛋白质序列相似性足够低，以至于同源性的计算检测变得相当具有挑战性的远程蛋白质同源物。我们建议构建新的线程方法，将蛋白质结构预测和序列/结构比对进一步扩展到“模糊区域”。我们从两个方面来解决这个问题：首先，我们打算将我们设计的“包装”方法扩展到其他SCOP超族，以成功地解决这个问题的两个硬特殊情况。这包括将能量函数包装部分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；然后使用基于依赖于主干的转子库的更复杂的能量函数进行侧链填充。其次，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，我们在其上包裹序列，以预测它们是否可以折叠成这些结构。为了为PDB构建一个具有合理折叠库覆盖的通用线程程序，我们需要解决从一组蛋白质中自动构建结构模板的挑战，例如，所有这些蛋白质都属于同一个SCOP超家族。大多数当前的线程程序训练过于接近一个特定结构的主干，无法捕获远程同源。我们提出了一种新的多结构对齐方法，增加了几何灵活性，以捕获更远的同源物之间的相似性，从中可以抽象出更通用的核心模板。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。BetaWrap是我们的第一个β -螺旋预测程序，已经揭示了β -螺旋折叠与微生物病原体毒力之间以前未知的关系。BetaWrap程序的一个惊人的预测是，β -螺旋折叠被预测为许多表面粘附素、毒素和其他人类病原体的识别/渗透蛋白。我们的预测是一种主要的花粉过敏原形成了β -螺旋形状，这一预测最近得到了实验证实。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。该建议特别针对改善β结构基基的预测，其中包括许多对细菌发病机制很重要的蛋白质家族，从百日咳毒素（β -螺旋）到肉毒毒素（β -三叶）的代表。BetaWrap是我们的第一个β -螺旋预测程序，已经揭示了β -螺旋折叠与微生物病原体毒力之间以前未知的关系。BetaWrap程序的一个惊人的预测是，β -螺旋折叠被预测为许多表面粘附素、毒素和其他人类病原体的识别/渗透蛋白。我们的预测是一种主要的花粉过敏原形成了β -螺旋形状，这一预测最近得到了实验证实。