Analysis-Low-complexity Amino Acid-Nucleotide Sequences

低复杂性氨基酸-核苷酸序列分析

• 批准号: 7148025
• 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/7148025
• 关键词: artificial intelligence; bioinformatics; chemical models; chemical structure function; computational biology; computer assisted sequence analysis; computer system design /evaluation; conformation; mathematical model; molecular dynamics; nucleic acid repetitive sequence; nucleic acid sequence; protein sequence; statistics /biometry; structural biology

The goal of this project is to define and analyze, using computational methods, 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 have been 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.

该项目的目标是使用计算方法定义和分析显示成分偏差的蛋白质和核苷酸序列片段，并了解它们的结构、功能和进化意义及其病理学。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本质上非结构化区域、串联重复序列以及更普遍分布的氨基酸含量偏差。后者可以反映基因组水平的定向突变压力以及特定于蛋白质或结构域功能的约束。低复杂性区域包含大部分基因组编码的氨基酸，并且可能包含一些氨基酸的同聚束或嵌合体，或重复的模式，通常是微妙的，包括许多非球状结构域的典型模式。新的数学定义和算法已经被开发出来，以识别成分偏差的区域，并发现和分析这些区域与其结构、相互作用、生物功能和进化相关的特性。由富含 AT 或 GC 的基因组编码的蛋白质表现出强烈的背景偏差，其中包括几种重要的传染病生物体的基因组：这些给序列比对算法带来了问题，目前正在解决这些问题。低复杂性和串联重复氨基酸序列的局部区域存在于许多涉及细胞分化和胚胎发育、RNA加工、转录调节、信号转导以及细胞和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球状的、本质上非结构化的或构象可移动的：然而，对这些域的分子结构和动力学的了解仍然非常有限。它们通常相对难以通过晶体学和 NMR 进行研究，并且在当前结构数据库中仅占残基的不到 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要进行实验研究的问题的首选方法。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一些伪影方面也很有价值。