COMPUTER ANALYSIS OF LOW-COMPLEXITY AMINO ACID AND NUCLEOTIDE SEQUENCES

低复杂性氨基酸和核苷酸序列的计算机分析

• 批准号: 5203621
• 负责人: J WOOTTON
• 金额: --
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/5203621
• 关键词: automated data processing; chemical information system; computer assisted sequence analysis; computer program /software; computer system design /evaluation; method development; nucleic acid sequence; protein sequence; protein structure function; statistics /biometry; automated data processing; chemical information system; computer assisted sequence analysis; computer program /software; computer system design /evaluation; method development; nucleic acid sequence; protein sequence; protein structure function; statistics /biometry

The goal of this project is to define, classify and analyze, using computational analysis, segments of protein and nucleotide sequences showing compositional bias or improbably low compositional complexity. In protein sequences, these include the abundant residue clusters of predominantly one or a few amino acid types, which commonly contain homopolymeric tracts or mosaics of these, aperiodic patterns and sections of low-period repeats. Other common examples include long non-globular domains. The abundance of biased segments in both amino acid and nucleotide sequence databases has been determined, and their properties are being related to evidence of biological functions. A. Methods: Different formal definitions of local compositional complexity were used to make unbiased identification of low-complexity segments, at different levels of stringency. Algorithms were refined to (a) select segments for further study, (b) filter out non-informative segments prior to database searches, and (c) discover and analyze regions in which compositional bias is present in periodically-spaced rather than contiguous residues. New methods for automated classification and neighboring of low- complexity sequences have been developed. B. Abundance and biological properties: Approximately 25% of the residues in protein databases are in compositionally biased segments (including some known long non- globular regions) and approximately 55% of proteins contain one or more such segments. Interspersed low-complexity sequences are particularly abundant in many eukaryotic proteins crucial in morphogenesis and embryonic development, RNA processing, transcriptional regulation, signal transduction and aspects of cellular and extracellular structural integrity. The limited structural information available for low- complexity regions of proteins indicates that they are generally non- globular and polymorphic kr mobile. Significance of project: The project is highlighting the high abundance and biological importance of low-complexity protein segments. Knowledge of their molecular structure and dynamics is beginning to emerge in a few cases, but these are a minority. This is a priority area for future research. The methods recently developed to analyze nucleotide sequences are revealing many new and intricate compositional features. These methods are valuable in eliminating many artefacts in sequence database searches and alignment analysis.

该项目的目的是使用 计算分析，蛋白质和核苷酸序列的段 显示组成偏差或不可能的低组成复杂性。 在蛋白质序列中，这些包括丰富的残留簇 主要是一种或几种氨基酸类型，通常包含 这些均聚焦区或镶嵌物，多个剖面图和部分 低周期重复。 其他常见的例子包括长期非全球 域。 氨基酸和 已经确定了核苷酸序列数据库及其性能 与生物学功能的证据有关。 A.方法： 使用了局部组成复杂性的不同形式定义 在不同 严格水平。 将算法完善为（a）选择的部分 进一步研究，（b）在数据库之前过滤掉非信息段 搜索，以及（c）发现和分析组成的区域 偏置存在于定期间隔而不是连续残基中。 自动分类和相邻的新方法 已经开发了复杂性序列。 B.丰度和生物学 特性：蛋白质数据库中约25％的残基是 在构图有偏见的段（包括一些已知的长期非 - 球状区域）和大约55％的蛋白质包含一个或多个 这样的细分市场。 散布的低复杂性序列特别是 在许多真核蛋白中丰富的形态发生至关重要 胚胎发育，RNA处理，转录调控，信号 细胞和细胞外结构的转导和方面 正直。 有限的结构信息可用于低 - 蛋白质的复杂性区域表明它们通常不是 球形和多态性KR移动。 项目的意义： 项目强调了高丰度和生物学的重要性 低复杂性蛋白段。 了解其分子结构 在少数情况下，动态开始出现了，但是这些都是 少数民族。 这是未来研究的优先领域。 方法 最近开发的用于分析核苷酸序列正在揭示许多新的 和复杂的组成特征。 这些方法在 消除序列数据库搜索和对齐方式中的许多人工制品 分析。