BD Spokes: PLANNING: WEST: Collaborative: Increasing collaborations in proteogenomics applications of genetic data

许多众所周知的疾病可能是由影响重要蛋白质特征（如酶活性位点）的遗传变异（如点突变）引起的。科学界已经编目了数百万种遗传变异（在基因组数据库中）和数千种蛋白质结构（在蛋白质数据库中）。然而，这两种类型的信息并没有联系起来，或者很容易以一种容易探索变体及其结构位置之间关系的方式联系起来。包括遗传变异、蛋白质序列和3D结构在内的综合研究很少，或者只是单独关注少数蛋白质。在这个规划项目中，我们将促进和促进来自这些社区的专家之间的互动，共同目标是开发全面整合这些数据的方法。该项目将为精准医学工作奠定基础，并将对许多物种的研究产生重大影响，对这些物种来说，探索品系或品种之间的遗传变异非常重要。此外，该项目将直接影响教育：我们目前每年教授几门蛋白质组学信息学和系统生物学课程，我们将创建和使用可再分发的教学模块，以帮助学生学习将这些概念应用到他们的研究中。超越线性？基因变异的时候。目前，基因组学和质谱蛋白质组学领域的科学家们有一种强烈的倾向，即以线性方式思考基因组功能。然而，变异体（和翻译后修饰）的功能意义受到它们在蛋白质结构上的三维位置的强烈影响。由于缺乏现成的工具，这种从线性位置到三维位置的飞跃很少发生。我们的基础设施将支持各种规模的分析，从将单个变体映射到单个蛋白质，到将数百万个变体映射到所有可用的蛋白质序列和结构。这将反过来使空间模式的发现和解释的功能的变异频率，受影响的氨基酸，倾向于后修饰，和亚结构内的位置。

Many well-known diseases can be caused by genetic variants (such as point mutations) that affect important protein features such as enzyme active sites. The scientific community has catalogued millions of genetic variants (in genomic databases) and thousands of protein structures (in the Protein Data Bank). However, these two types of information are not linked, or easily linkable, in a manner that makes it easy to explore the relationships between variants and their structural locations. Integrative research, including genetic variation and protein sequence and 3D structure, has been rare or just focusing on a few proteins individually. In this planning project we will promote and facilitate interactions between experts from these communities with the shared goal of developing methods for integrating these data comprehensively. The project will lay groundwork for precision medicine efforts, and will have a significant impact on research on many species for which exploration of the genetic variation among strains or breeds is important. Furthermore, this project will directly impact education: we currently teach several courses per year in proteomics informatics and systems biology, and we will create and use redistributable teaching modules to help students learn to apply these concepts to their research.Our proposed methodology will enable researchers to ?think beyond linear? when interpreting genetic variation. There is currently a strong tendency for scientists in the genomic and mass-spectrometry proteomics communities to think about genome function in linear terms. However, the functional implications of variants (and post-translational modifications) are strongly influenced by their 3-dimensional location on a protein structure. Due to the lack of readily available tools, this leap from a linear position to a 3-dimensional location is rarely made. Our infrastructure will enable analysis at all scales, from mapping individual variants to a single protein, to mapping millions of variants to all available protein sequences and structures. This will in turn enable the discovery and interpretation of spatial patterns as a function of variant frequencies, affected amino acids, tendency to be post-translationally modified, and location within substructures.