Computational diagnosis of non-synonymous variations using structural dynamics

使用结构动力学对非同义变异进行计算诊断

• 批准号: 8682241
• 负责人: Zeynep Nevin Gerek Ince
• 金额: $ 23.4万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8682241
• 关键词: Algorithms; Amino Acid Substitution; Amino Acids; Area; Benchmarking; Biochemical; Biochemical Reaction; Biological; Biological Process; Biology; Characteristics; Clinical; Complex; Computer Simulation; Computer software; Computing Methodologies; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Electron Transport; Equilibrium; Evolution; Exhibits; Genes; Genetic Variation; Genome; Genomics; Health; Hereditary Disease; Human; Human Genetics; Hybrids; Individual; Internet; Investigation; Knowledge; Lead; Learning; Ligands; Measures; Medicine; Methods; Modeling; Molecular Genetics; Motion; Movement; Mutation; Nucleotides; Performance; Phenotype; Positioning Attribute; Process; Property; Protein Dynamics; Proteins; Proteome; Research; Resources; Rotation; Sequence Alignment; Sequence Analysis; Side; Site; Solvents; Statistical Models; Structural Protein; Structure; Techniques; Technology; Training and Education; Translating; Variant; base; computerized tools; cost; design; diagnostic accuracy; disease diagnosis; disorder risk; exome; functional genomics; genetic evolution; improved; meetings; novel; predictive modeling; programs; protein function; protein structure; structural biology; success; tool

DESCRIPTION (provided by applicant): Advances in sequencing technologies provide rapidly increasing amounts of data on human genetic variation. However, distinguishing between neutral variants (with little or no effect on phenotype) from variants conferring disease risk remains a major challenge for both monogenic (Mendelian) and complex diseases. The current state-of-the-art methods for diagnosing amino acid variants primarily employ evolutionary information obtained from multispecies sequence analysis in a variety of ways. While these methods have been used extensively, they often fail to correctly diagnose damaging variants at evolutionarily variable positions and neutral variants at highly conserved positions. Our initial investigations suggests that the protein structural dynamics, which is crucial for proper biochemical activity, has the potential to improve prediction of function-altering variants at less conserved positions and neutral variants at highly conserved positions. Therefore, we propose to explore and build novel in silico prediction tools that exclusively use parameters capturing protein structure and dynamics. We propose to investigate the use of various structure dynamics features that capture the multi- dimensional effects of perturbations on a residue when the protein structure is displaced out of equilibrium. We will also independently assess the contributions of different structural dynamics features in a systematic, quantitative way for their diagnostic power and compare the accuracy of our models with state-of-the-art methods. Furthermore, we will explore the use of multiple methods together to identify most reliable diagnoses. Success of this project will catalyze research at the interface of protein structural biology, molecular genetics, evolution and medicine, as it will advance the mechanistic understanding of protein function disruption in functional and genomic investigations.

描述（由申请人提供）：测序技术的进步提供了快速增加的人类遗传变异数据量。然而，区分中性变异（对表型影响很小或没有影响）与赋予疾病风险的变异仍然是单基因（孟德尔）和复杂疾病的主要挑战。当前用于诊断氨基酸变异的最先进方法主要利用以多种方式从多物种序列分析中获得的进化信息。虽然这些方法已被广泛使用，但它们常常无法正确诊断进化可变位置的破坏性变异和高度保守位置的中性变异。我们的初步研究表明，蛋白质结构动力学对于适当的生化活性至关重要，有可能以更少的成本改进功能改变变异的预测。 保守位置和高度保守位置的中性变体。因此，我们建议探索和构建新颖的计算机预测工具，专门使用捕获蛋白质结构和动力学的参数。我们建议研究使用各种结构动力学特征来捕获当蛋白质结构失去平衡时残基上的扰动的多维效应。我们还将以系统、定量的方式独立评估不同结构动力学特征对其的贡献 诊断能力并将我们的模型的准确性与最先进的方法进行比较。此外，我们将探索结合使用多种方法来确定最可靠的诊断。该项目的成功将促进蛋白质结构生物学、分子遗传学、进化和医学交叉领域的研究，因为它将促进功能和基因组研究中对蛋白质功能破坏的机制的理解。