Detecting Homology in the "Twilight Zone" of Sequence Similarity

检测序列相似性“暮光区”的同源性

• 批准号: 8243153
• 负责人: RANDEN LEE PATTERSON
• 金额: $ 13.27万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-10 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8243153
• 关键词: Detecting; Homology; Twilight; Zone; Sequence

DESCRIPTION (provided by applicant): The `protein problem' has remained unsolved despite decades of research [1, 2]. In principle, one expects that the primary amino acid sequence of a protein determines its structure, function, and evolutionary (SF&E) characteristics. Yet, there still is no reliable method for predicting the native state structure of a protein and its function given only its sequence. In addition, inferring the evolutionary relationships among highly divergent protein sequences is a daunting task. In general, when pairwise sequence alignments between protein sequences fall below 25% identity, statistical measurements do not provide support robust enough to identify clear phylogenetic relationships despite intensive research in this area [1, 3, 4]. The recent explosion in the availability of knowledge bases and computational techniques for the analysis of complex data has created an unprecedented opportunity for teasing out invaluable information from protein sequences. Starting with a basic premise that protein sequence encodes information about SF&E, we developed a unified framework for inferring SF&E from sequence information using a knowledge-based approach in which we measure the similarity between a query sequence and a set of biologically relevant profiles in an unbiased manner. Results from this Gestalt Domain Detection Algorithm-Basic Local Alignment Tool (GDDA-BLAST) provide phylogenetic profiles that have the capacity to model SF&E relationships of various proteins. Indeed, GDDA-BLAST is capable of deriving deep phylogenetic relationships for highly divergent proteins in a quantifiable manner [5, 6]. Preliminary results from our computational case study of the highly divergent family of retroelements accord with those previously reported, and demonstrate that GDDA-BLAST measurements can be treated as "fingerprints" that can be used to derive distance estimates and hence phylogenetic relationships without prior information, multiple sequence alignment, or manual editing. We propose that sequence information present within the "twilight zone" of sequence similarity can provide key insight into SF&E relationships among distantly related and/or rapidly evolving proteins. This proposal aims to push our limits of detecting homology within the "twilight zone" of sequence similarity by evaluating and optimizing GDDA-BLAST performance on benchmark and experimental data sets. Armed with these refined GDDA- BLAST measurements we propose to conduct a comprehensive, ab initio, phylogenetic study of retroelements and RNA dependent RNA polymerases from the positive-strand family of RNA viruses (+ssRNA). Simultaneously we will derive high-resolution maps of domain boundaries and empirically validate functional annotations and predictions of key residues for those activities. This work aims to perform translational research from the computer to the laboratory bench top. We expect that the tools and resources generated from this grant will be accessible and user-friendly to the bench scientist, thereby speeding the discovery process of other clinically relevant research endeavors. PUBLIC HEALTH RELEVANCE: The long-term implication of this proposal is the development of a unified framework for high-resolution and simultaneous measurements of structure, function, and evolution. Should this be possible: (i) functional and evolutionary measurements could quantitatively inform structural modeling to derive accurate atomic resolution protein structures, (ii) structural and functional measurements could inform evolutionary histories to derive accurate evolutionary rates, deep-branch relationships, and homologous spaces within each protein, and (iii) structural and evolutionary measures would inform as to the location of functionalities contained within any protein and the regulatory elements which control these functions. Armed with this information, the speeds at which diseases could be understood and pharmacophores/therapies developed to combat them would likely increase dramatically.

描述（由申请人提供）：尽管经过数十年的研究，“蛋白质问题”仍未得到解决[1, 2]。原则上，人们预计蛋白质的一级氨基酸序列决定其结构、功能和进化 (SF&E) 特征。然而，仍然没有可靠的方法来预测蛋白质的天然状态结构及其仅给定其序列的功能。此外，推断高度差异的蛋白质序列之间的进化关系是一项艰巨的任务。一般来说，当蛋白质序列之间的成对序列比对低于 25% 同一性时，尽管在该领域进行了深入研究，但统计测量仍无法提供足够强大的支持来识别清晰的系统发育关系 [1,3,4]。最近，用于分析复杂数据的知识库和计算技术的可用性激增，为从蛋白质序列中梳理出宝贵的信息创造了前所未有的机会。从蛋白质序列编码有关 SF&E 的信息这一基本前提出发，我们开发了一个统一的框架，使用基于知识的方法从序列信息推断 SF&E，在该框架中，我们以无偏倚的方式测量查询序列和一组生物学相关图谱之间的相似性。方式。该格式塔域检测算法-基本局部比对工具 (GDDA-BLAST) 的结果提供了能够对各种蛋白质的 SF&E 关系进行建模的系统发育图谱。事实上，GDDA-BLAST 能够以可量化的方式推导高度差异蛋白质的深层系统发育关系 [5, 6]。我们对高度分歧的逆转录因子家族的计算案例研究的初步结果与之前报道的结果一致，并证明 GDDA-BLAST 测量可以被视为“指纹”，可用于推导距离估计，从而在没有先验信息的情况下推导出系统发育关系，多序列比对，或手动编辑。我们认为，序列相似性“暮光区”中存在的序列信息可以为了解远缘相关和/或快速进化的蛋白质之间的 SF&E 关系提供关键见解。该提案旨在通过评估和优化基准和实验数据集上的 GDDA-BLAST 性能，突破序列相似性“暮光区”内同源性检测的极限。有了这些精确的 GDDA-BLAST 测量，我们建议对来自 RNA 病毒正链家族 (+ssRNA) 的逆转录元件和 RNA 依赖性 RNA 聚合酶进行全面的、从头开始的系统发育研究。同时，我们将得出域边界的高分辨率图，并凭经验验证这些活动的关键残基的功能注释和预测。这项工作旨在进行从计算机到实验室台面的转化研究。我们期望该资助产生的工具和资源对于实验室科学家来说是易于使用且用户友好的，从而加快其他临床相关研究工作的发现过程。公共卫生相关性：该提案的长期影响是开发一个统一的框架，用于高分辨率和同步测量结构、功能和进化。如果这可能的话：（i）功能和进化测量可以定量地为结构建模提供信息，以导出准确的原子分辨率蛋白质结构，（ii）结构和功能测量可以为进化历史提供信息，以导出准确的进化速率、深分支关系和同源空间(iii)结构和进化测量将告知任何蛋白质中包含的功能的位置以及控制这些功能的调节元件。有了这些信息，了解疾病和开发对抗疾病的药效基团/疗法的速度可能会显着提高。