Computational diagnosis of non-synonymous variations using structural dynamics

描述（由申请人提供）：测序技术的进步为人类遗传变异提供了快速增长的数据量。然而，区分中性变异（对表型影响很小或没有影响）和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。目前最先进的诊断氨基酸变异的方法主要采用从多种方式的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，但它们往往不能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适当的生化活性至关重要，它有可能以更低的成本提高对功能改变变异的预测

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.