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的遗传变异(如点突变)引起的，如酶活性部位。科学界已经对数百万种遗传变异(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)进行了编目。然而，这两种类型的信息不是联系在一起的，或者很容易联系在一起，这种方式使得探索变体和它们的结构位置之间的关系变得容易。包括遗传变异、蛋白质序列和3D结构在内的综合性研究一直很少，或者只关注几个单独的蛋白质。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，共同的目标是制定全面整合这些数据的方法。该项目将为精确医学努力奠定基础，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是很重要的。此外，这个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的课程，我们将创建和使用可再分配的教学模块来帮助学生学习将这些概念应用到他们的研究中。我们提出的方法将使研究人员在解释基因变异时能够“超越线性”思考。目前，基因组和质谱学蛋白质组学领域的科学家有一种强烈的趋势，即用线性术语来思考基因组的功能。然而，变体(和翻译后修饰)的功能含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实现。我们的基础设施将支持所有规模的分析，从将单个变体映射到单一蛋白质，到将数百万个变体映射到所有可用的蛋白质序列和结构。这反过来将使空间模式的发现和解释成为不同频率、受影响的氨基酸、翻译后修饰的倾向以及亚结构中位置的函数。

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.