Detecting Homology in the "Twilight Zone" of Sequence Similarity

描述（由申请人提供）：尽管几十年的研究，“蛋白质问题”仍未得到解决[1,2]。原则上，人们期望蛋白质的初级氨基酸序列决定其结构、功能和进化（SF&E）特征。然而，目前还没有可靠的方法来预测蛋白质的天然状态结构和它的功能，只给出它的序列。此外，推断高度分化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%的同一性时，尽管在这一领域进行了深入的研究，但统计测量并不能提供足够强大的支持来确定明确的系统发育关系[1,3,4]。最近，用于分析复杂数据的知识库和计算技术的爆炸式增长，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SF&E信息的基本前提开始，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，其中我们以无偏的方式测量查询序列和一组生物学相关概况之间的相似性。格式塔结构域检测算法-基本局部比对工具（gda - blast）的结果提供了能够模拟各种蛋白质的SF&E关系的系统发育概况。事实上，gda - blast能够以可量化的方式推导出高度分化的蛋白质的深层系统发育关系[5,6]。我们对高度分化的逆转录因子家族的计算案例研究的初步结果与先前报道的结果一致，并证明gda - blast测量可以被视为“指纹”，可用于推导距离估计，从而无需事先信息，多个序列比对或手动编辑系统发育关系。我们提出，存在于序列相似性“模糊区域”内的序列信息可以为了解远亲和/或快速进化的蛋白质之间的SF&E关系提供关键见解。本提案旨在通过评估和优化gda - blast在基准和实验数据集上的性能，突破我们在序列相似性“模糊区域”内检测同源性的极限。有了这些改进的GDDA- BLAST测量，我们建议对RNA病毒正链家族（+ssRNA）的逆转录因子和RNA依赖的RNA聚合酶进行全面的从头开始的系统发育研究。同时，我们将获得领域边界的高分辨率地图，并经验验证这些活动的功能注释和关键残基的预测。这项工作旨在进行从计算机到实验室工作台的转化研究。我们期望从这项资助中产生的工具和资源对实验科学家来说是可访问的和用户友好的，从而加快其他临床相关研究努力的发现过程。公共卫生相关性：本提案的长期意义是发展一个高分辨率和同时测量结构、功能和进化的统一框架。如果这是可能的：(i)功能和进化测量可以定量地为结构建模提供信息，以获得精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史提供信息，以获得精确的进化速率、深分支关系和每个蛋白质内的同源空间；（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，了解疾病的速度和开发对抗疾病的药效团/疗法的速度可能会大大提高。

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.