Novel Use of Genome Information to Understand Mutations

将基因组序列翻译成蛋白质有很大的优势， 关于它们的序列、结构和功能之间的关系的积累知识。进展 基因组测序正在产生大量的数据，这些数据可用于训练和测试预测方法， 通过建立在大的功能蛋白质数据之上来识别各种突变体的特征。临床医生 我们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-它们是否影响蛋白质动力学-它们是否影响蛋白质结合特异性。 蛋白质结构对于序列中的每个氨基酸都有局部环境，并且通常氨基酸在 每个位置都与当地环境相适应。这导致强烈相关的氨基酸， 在多重序列比对中表现出来。本项目将联合收割机结合蛋白质序列和结构数据 与氨基酸性质及其相关性一起表征蛋白质结构中的每个位点， 调查假设，在分布中的离群值超过重要的氨基酸性质，为每个 位置将负面影响功能性，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。在这两个领域将采取两种不同的方法 目的：目的1将研究氨基酸性质分布，以确定最能表征氨基酸性质的性质。 序列和结构中的每个位置，并确定异常值如何对函数产生负面影响 结构、动力学和结合特性。初步结果表明，有害突变体通常 对于有害突变体具有显著更宽范围的单个氨基酸特性。数据从这些 分析将被输入Aim 2，其中有两种类型的机器学习方法-极限学习机器 和随机森林将联合应用。初步结果显示，仅加入一种氨基酸 性能产生了显着的收益超过现有的方法。该项目的主要优势之一是， 这两个目标将经常交换，以实现两种方法的改进预测。的 这个项目也建立在PI在蛋白质结构和序列数据挖掘方面的长期经验之上 和以前的机器学习应用一样。重要的潜在成果包括更可靠、更 了解突变体如何影响功能。此外，该项目旨在预测 针对特定疾病的突变体。该项目的结果将对药物开发很重要，因为 蛋白质中功能受损的特定部分将被鉴定出来，以允许药物开发人员缩小其 专注于药物设计的目标蛋白质的更有限的部分。由此建立的预测器 该项目还将有可能筛选大量以前未知的突变， 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。

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.