Computer Analysis Of Low-complexity Amino Acid And Nucle

低复杂性氨基酸和核酸的计算机分析

• 批准号: 7316230
• 负责人: JOHN C. 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/7316230
• 关键词: Computer; Analysis; Low; complexity; Amino

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. New mathematical definitions and algorithms are being developed to 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. 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.

该项目的目的是定义和分析蛋白质和核苷酸序列的片段，显示出组成偏差，并了解其结构，功能和进化意义及其病理。这些序列包括局部低复杂性区域或域，包括蛋白质的构象移动或本质上的非结构化区域，串联重复的序列以及更普遍分布的氨基酸含量偏见。后者可以反映基因组水平上的定向突变压力，并针对蛋白质或结构域功能的约束。低复杂性区域包括很大一部分基因组编码的氨基酸，并且可能包含几种氨基酸或重复模式的同质膜或马赛克，通常是微妙的，包括许多非球形域的典型域。正在开发新的数学定义和算法来识别组成偏差的区域，并发现和分析与它们的结构，相互作用，生物学功能和进化相关的这些区域的性质。强烈的背景偏见是由非常丰富或富含GC的基因组编码的蛋白质显示的，其中包括几种重要的感染性疾病生物的蛋白质：这些引起了正在解决的序列比对算法的问题。低复杂性和串联重复的氨基酸序列的局部区域发生在许多参与细胞分化和胚胎发育，RNA处理，转录调控，信号转导以及细胞外结构完整性方面的蛋白质中。实验数据表明，蛋白质的低复杂性片段通常是非全球，本质上非结构化或构象移动的：但是，对这些结构域的分子结构和动力学的了解仍然非常有限。它们通常与晶体学和NMR的研究相对棘手，并且在当前结构数据库中不到1％的残基。因此，在数学上进行了严格的序列分析和从头量量子化学方法以及一些相关的高分辨率结构数据，是获得对这些蛋白质区域的见解的选择，并在实验上提出问题。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很有价值。