Novel Use of Genome Information to Understand Mutations

利用基因组信息来理解突变的新方法

• 批准号: 10303852
• 负责人: ROBERT L JERNIGAN
• 金额: $ 48.06万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10303852
• 关键词: Adverse effects; Affect; Amino Acid Sequence; Amino Acid Substitution; Amino Acids; Artificial Intelligence; Back; Behavior; Binding; Binding Proteins; Binding Sites; Cell physiology; Characteristics; Clinical; Complex; Computer software; Data; Data Analyses; Databases; Detection; Disease; Drug Design; Drug Targeting; Environment; Family; Foundations; Future; Genetic Variation; Genome; Gold; Impairment; Individual; Knowledge; Learning; Literature; Machine Learning; Methods; Missense Mutation; Molecular; Mutation; Nature; Normal Range; Organism; Outcome; Output; Patients; Pharmaceutical Preparations; Phenotype; Positioning Attribute; Probability; Property; Protein Dynamics; Proteins; Quantitative Evaluations; Reporting; Scientific Advances and Accomplishments; Sequence Alignment; Severities; Site; Solid; Specificity; Structure; Testing; Training; Translating; Ursidae Family; Variant; base; cost; data mining; design; disease phenotype; drug development; experience; genome sequencing; human disease; improved; individual patient; individualized medicine; innovation; large datasets; machine learning algorithm; mutant; novel; protein function; protein structure; protein structure prediction; random forest; screening; tool; user friendly software

There are significant advantages from translating genome sequences into proteins, where there is a large body of accumulated knowledge regarding their relationships among sequence, structure and function. Advances in genome sequencing are producing a deluge of data that can be used to train and test prediction methods to identify the characteristics of various mutants by building atop the large functional protein data. Clinicians need to know the functional behavior of mutants - whether they are neutral or deleterious - whether they affect protein structure – whether they affect protein dynamics - whether they affect protein binding specificity. Protein structures have local environments for each amino acid in the sequence, and usually amino acids at each position are compatible with their local environment. This leads to strongly correlated amino acids as manifested in the multiple sequence alignments. This project will combine protein sequence and structure data together with amino acid properties and their correlations to characterize each site in the protein structure to investigate the hypothesis that outliers in the distributions over the important amino acid properties for each position will negatively impact functionality, i.e. they will be deleterious mutants. The project will drill down deeply to learn what is the nature of the impaired mechanism. Two diverse approaches will be taken in the two aims: Aim 1 will investigate the amino acid property distributions to identify the properties that best characterize each position in the sequence and structure, and determine how the outliers negatively impact the functional structures, dynamics and binding characteristics. Preliminary results show that the deleterious mutants usually have a significantly broader range of single amino acid properties for the deleterious mutants. Data from these analyses will be fed into Aim 2 where two type of machine learning approaches – Extreme Learning Machines and Random Forests will be jointly applied. Preliminary results show that incorporating just one amino acid property yields significant gains over existing methods. One of the major strengths of this project is that results from the two Aims will be exchanged frequently to achieve improved predictions for both approaches. The project builds on the long experience of the PIs in datamining from protein structures and sequences, as well as previous machine learning applications. Important potential outcomes include a more reliable, more informed understanding of how mutants affect function. In addition, the project aims to predict connections of mutants to specific diseases. The results of the project will be important for drug development, because the specific part of the protein where function is impaired will be identified, to allow drug developers to narrow their focus onto more limited parts of a protein that is targeted for drug design. The predictors established by this project will also have the potential to screen for large numbers of previously unknown mutations that could be used to identify specific regions of a protein structure susceptible to further disease-related mutations.

将基因组序列翻译成蛋白质具有显着的优势，因为蛋白质的体量很大 关于序列、结构和功能之间关系的积累的知识。 基因组测序正在产生大量数据，可用于训练和测试预测方法 临床医生通过构建大量功能蛋白数据来识别各种突变体的特征。 需要知道突变体的功能行为 - 它们是中性的还是有害的 - 它们是否影响 蛋白质结构 – 是否影响蛋白质动力学 – 是否影响蛋白质结合特异性。 蛋白质结构对于序列中的每个氨基酸都有局部环境，通常是位于 每个位置都与其当地环境相容，这导致氨基酸的强相关性。 体现在多重序列比对上。 结合氨基酸特性及其相关性来表征蛋白质结构中的每个位点 研究以下假设：每个重要氨基酸特性的分布中存在异常值 位置将对功能产生负面影响，即它们将是有害的突变体，该项目将深入研究。 深入了解受损机制的本质是什么，这两种方法将采取两种不同的方法。 目标：目标 1 将研究氨基酸特性分布，以确定最能表征的特性 序列和结构中的每个位置，并确定异常值如何对功能产生负面影响 结构、动力学和结合特征的初步结果表明，有害突变体通常是有害的。 来自这些有害突变体的数据具有更广泛的显着单氨基酸特性。 分析将被纳入目标 2，其中两种机器学习方法 – 极限学习机 和随机森林将联合应用，初步结果表明仅合并一种氨基酸。 该项目的主要优势之一是结果。 这两个目标的结果将经常交换，以改进这两种方法的预测。 该项目建立在 PI 在蛋白质结构和序列数据挖掘方面的长期经验的基础上 与之前的机器学习应用一样，重要的潜在成果包括更可靠、更可靠。 此外，该项目旨在预测突变体如何影响功能。 该项目的结果对于药物开发非常重要，因为 将识别出功能受损的蛋白质的特定部分，从而使药物开发人员能够缩小研究范围 重点关注药物设计所针对的蛋白质的更有限部分。 该项目还将有潜力筛选大量以前未知的突变，这些突变可能会被 用于识别蛋白质结构中易受进一步疾病相关突变影响的特定区域。