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: 7594457
• 负责人: JOHN C. WOOTTON
• 金额: $ 22.95万
• 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/7594457
• 关键词: Accounting; Address; Algorithms; Amino Acid Repetitive Sequences; Amino Acid Sequence; Amino Acids; Base Sequence; Biological Process; Chemicals; Communicable Diseases; Computer Analysis; Computing Methodologies; Crystallography; Data; Databases; Embryonic Development; Evolution; Genome; Genomics; Goals; Investigation; Knowledge; Malaria Vaccines; Methods; Molecular Structure; Morphologic artifacts; Mutation; Nucleotides; Organism; Pathology; Pattern; Property; Protein Region; Proteins; RNA Processing; Resolution; Sequence Alignment; Sequence Analysis; Signal Transduction; Structure; Transcriptional Regulation; extracellular; insight; pressure; quantum; vaccine development

The goal of this project is to define and analyze segments of protein and nucleotide sequences showing compositional bias and to understand their structural, functional and evolutionary significance, and their pathology. These sequences include local low complexity regions or domains, including conformationally mobile or intrinsically unstructured regions of proteins, tandemly-repeated sequences, and also more generally distributed amino acid content bias. The latter can reflect directional mutation pressures at the genomic level and constraints specific to protein or domain function. Low complexity regions comprise a large proportion of the genome-encoded amino acids, and may contain homopolymeric tracts or mosaics of a few amino acids, or repeated patterns, frequently subtle, including those typical of many non-globular domains and dynamic segments of proteins. New mathematical definitions and algorithms are being developed to define and identify regions of compositional bias, and to discover and analyze properties of these regions relevant to their structures, interactions, biological functions, and evolution. Strong background bias is shown by proteins encoded by very AT-rich or GC-rich genomes, which include those of several important infectious disease organisms: these raise problems for sequence alignment algorithms which are being addressed. Local regions of low complexity and tandemly repeated amino acid sequences occur in many proteins involved in cellular differentiation and embryonic development, RNA processing, transcriptional regulation, signal transduction and aspects of cellular and extracellular structural integrity. Experimental data indicate that low complexity segments of proteins are generally non-globular, intrinsically unstructured, or conformationally mobile: however, knowledge of the molecular structures and dynamics of these domains is still very limited. They are generally relatively intractable to investigation by crystallography and NMR, and they account for less than 1% of the residues in current structural databases. Hence, mathematically rigorous sequence analysis and ab initio quantum chemical methods, together with some relevant high-resolution structural data, are methods of choice for gaining insights into these regions of proteins and for raising questions to be investigated expermentally. We are currently investigating repeated domains that are under trial as components of malaria vaccines. These methods are also valuable, for both nucleotide and amino acid sequences, in detecting and eliminating some artifacts in sequence database searches and alignment analysis.

这个项目的目标是定义和分析显示出组成偏见的蛋白质和核苷酸序列片段，并了解它们的结构、功能和进化意义以及它们的病理学。这些序列包括局部低复杂性区域或结构域，包括蛋白质的构象移动或内在非结构区域，重复序列，以及更广泛分布的氨基酸含量偏向。后者可以反映基因组水平的定向突变压力和特定于蛋白质或结构域功能的约束。低复杂性区域包括很大比例的基因组编码的氨基酸，并可能包含几个氨基酸的均聚区或镶嵌体，或重复模式，通常微妙，包括那些典型的许多非球状结构域和蛋白质的动态片段。正在开发新的数学定义和算法，以定义和识别成分偏差区域，并发现和分析与其结构、相互作用、生物功能和进化相关的这些区域的属性。由富含AT或GC的基因组编码的蛋白质显示出强烈的背景偏差，其中包括几种重要的传染病生物体的基因组：这给正在解决的序列比对算法带来了问题。低复杂性和简单重复氨基酸序列的局部区域存在于许多蛋白质中，涉及细胞分化和胚胎发育、RNA加工、转录调控、信号转导以及细胞和细胞外结构完整性等方面。实验数据表明，低复杂性的蛋白质片段通常是非球形的、本质上无结构的或构象可移动的：然而，对这些结构域的分子结构和动力学的了解仍然非常有限。它们通常相对较难通过结晶学和核磁共振进行研究，在当前结构数据库中所占残基的比例不到1%。因此，数学上严格的序列分析和从头算量子化学方法，以及一些相关的高分辨率结构数据，是深入了解这些蛋白质区域并提出有待实验研究的问题的首选方法。我们目前正在调查作为疟疾疫苗成分进行试验的重复结构域。对于核苷酸和氨基酸序列，这些方法在检测和消除序列数据库搜索和比对分析中的某些伪影方面也是有价值的。