Novel Use of Genome Information to Understand Mutations

利用基因组信息来理解突变的新方法

基本信息

批准号：
10661834
负责人：
ROBERT L JERNIGAN
金额：
$ 46.5万
依托单位：
IOWA STATE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10661834
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 Classification Clinical Complex Computer software Data Data Analyses Databases Detection Disease Drug Design Drug Targeting Environment Family Foundations Future Genetic Variation Genome Impairment Individual Knowledge Learning Literature Machine Learning Methods Missense Mutation Molecular Mutation Nature Normal Range Organism Outcome Output Patients Pharmaceutical Preparations Phenotype Positioning Attribute Predisposition Probability Property Protein Dynamics Proteins Quantitative Evaluations Reporting Scientific Advances and Accomplishments Sequence Alignment Severities Site Solid Specificity Structure Testing Training Translating Variant cost data mining design disease phenotype drug development experience feature selection genome sequencing human disease improved individual patient individualized medicine innovation large datasets machine learning algorithm mutant novel protein function protein structure random forest screening tool user friendly software

项目摘要

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.

从将基因组序列转换为蛋白质，有很大的优势，那里有一个大物体关于它们在序列，结构和功能之间关系的积累知识。进步基因组测序正在产生大量数据，这些数据可用于训练和测试预测方法通过在大型功能蛋白数据上构建各种突变体的特征。临床医生需要知道突变体的功能行为 - 无论它们是中性还是有害的 - 它们是否影响蛋白质结构 - 是否影响蛋白质动力学 - 它们是否影响蛋白质结合特异性。蛋白质结构在序列中具有每个氨基酸的局部环境，通常在每个位置都与他们的本地环境兼容。这导致强烈相关的氨基酸作为在多个序列比对中表现出来。该项目将结合蛋白质序列和结构数据以及氨基酸特性及其相关性，以表征蛋白质结构中的每个位点与调查以下假设：每个都在分布中的分布在每个重要氨基酸特性上的异常值位置将对功能产生负面影响，即它们将被删除突变体。该项目将钻探两种潜水员的方法将在两者中采取目的：AIM 1将研究氨基酸性质分布，以识别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。初步结果表明，通常已删除的突变体对于有害突变体的单个氨基酸性质的范围明显较宽。这些数据分析将被馈入目标2，其中两种机器学习方法 - 极限学习机器并将共同应用随机森林。初步结果表明，仅掺入一个氨基酸财产对现有方法产生了可观的收益。该项目的主要优势之一是结果从这两个目标中将经常交换，以实现两种方法的改进预测。这项目基于PI的长期经验，从蛋白质结构和序列中的数据进行研究中作为以前的机器学习应用程序。重要的潜在结果包括一个更可靠，更多的结果了解突变体如何影响功能的了解。此外，该项目旨在预测特定疾病的突变体。该项目的结果对于药物开发将很重要，因为将确定功能受损的蛋白质的特定部分，以允许药物开发人员缩小其范围专注于针对药物设计的蛋白质的更多有限部分。由此建立的预测因素项目还有可能筛选大量以前未知的突变用于鉴定蛋白质结构的特定区域，易受与疾病相关的突变的影响。