Comparative genomics of protein structure and function

网络与疾病中蛋白质结构-功能的比较基因组学 临床外显子组测序越来越频繁，但对患者护理的影响仍然有限 和个性化医疗。阻止这些数据转化为 治疗计划是，在每个病人的有害突变是很少的， 融入了无害的大背景中。结果，致病基因 很难被发现此外，即使找到了可归因基因，它们的功能也通常是 未知我们现在通过合并两条调查路线来解决这些问题， 将生物信息的进化和传播建模为平滑过程。在 蛋白质结构，平滑改善了对进化重要位点的预测， 功能和基质。在网络中，它导致了功能预测，而不需要 结构知识。而且，关于基因型-表型关系，它导致了 编码突变对适应度的有害作用的方程。目标1现在建议， 完善这个公式来评估编码突变的危害，以发现疾病- 导致基因和途径。重点将放在癌症上， 来自癌症基因组图谱的外显子组数据。与此同时，Aim 2将开发新的 通用的基于网络的技术来破译感兴趣的蛋白质的作用。目标3将 通过头部测试，实验验证致癌基因的选择预测， 颈癌和肺癌细胞系是否表现为肿瘤抑制剂的预期， 致癌基因这项工作应产生创新的，正式的分析基因型表型 与直接应用于癌症基因组学的关系;它将结合分子进化和 群体遗传学，同时能够对基因组变异进行临床解释， 发现决定发病率的癌症基因， 死亡率，并最终导致新的治疗方法。

Comparative Genomics of Protein Structure-Function in Networks and Disease Clinical exome sequencing is increasingly frequent but still of limited impact on patient care and personalized medicine. Key difficulties keeping these data from being translated into therapeutic plans are that in each patient harmful mutations are few and seemlessly blended into the vast background of harmless ones. As a result, disease-causing genes are difficult to spot. Also, even when imputable genes are found their functions are typically unknown. We now tackle these problems by merging two lines of investigation that both model the evolution and propagation of biological information as a smooth process. In protein structures, smoothing has improved predictions of evolutionary important sites, functions and substrates. In networks, it has led to function predictions without need of structural knowledge. And, with respect to the genotype-phenotype relationship, it has led to an equation for the harmful action of coding mutations on fitness. Aim 1 now proposes to refine this equation to score the harm of coding mutations in order to uncover disease- causing genes and pathways. The focus will be on cancer given the ready availability of exome data from The Cancer Genome Atlas. In parallel, Aim 2 will develop new and general network-based techniques to decipher the role of proteins of interest. Aim 3 will experimentally validate select predictions of cancer-causing genes by testing in head and neck and lung cancer cell lines whether they behave as expected of tumor suppressors and oncogenes. This work should yield innovative, formal analyses of the genotype to phenotype relationship with direct applications to cancer genomics; it will unite molecular evolution and population genetics, whilst enabling the clinical interpretation of genome variations and the discovery of cancer genes that determine morbidity and mortality and eventually lead to novel therapeutics.