Human functional genomics of post-translationally modifying clinical coding variants: FGx-PTMv

罕见疾病使人虚弱，患有这些疾病的儿童中有三分之一在5岁生日之前死亡。由英国基因组公司和英国国民健康保险制度提供的100,000个基因组计划产生了丰富的基因组变异目录。通过将基因组变异与患有疾病症状和症状的患者的疾病联系起来，该项目成功地确定了许多罕见疾病的遗传基础。这使得罕见疾病组中的一些患者可以得到临床诊断，但大多数仍然没有得到诊断。一般而言，基因组变异分为两类：位于基因蛋白质编码区的变异(外显子变异)和位于蛋白质之外的变异(内含子变异)。外显子变异体影响组成蛋白质的氨基酸序列。在某些情况下，这些变体具有可靠的可预测效果，例如产生无功能的蛋白质缩短版本。在其他情况下，变异可能导致正常氨基酸被替代。这些所谓的错义变体可以微妙地影响蛋白质的形态和功能，预测错义变体对蛋白质的影响更具挑战性。例如，蛋白质的翻译后修饰(PTM)可以调节它们的功能，有时这些错义变体会影响这些PTM，改变通常修饰的氨基酸。假设，PTM变异体(PTMv)对蛋白质功能的影响应该更可预测。为了了解给定的错义变体如何影响蛋白质功能，有必要在实验室中通过实验确定该变体的影响。这样做的一个结果是，对于在10万个基因组计划的罕见疾病相关基因中发现的大多数错义变异，尽管我们可以准确地确定相关性，但我们不能确定因果关系。因此，这些错义变体不被用于为患有这些罕见疾病的患者的临床诊断提供信息。我们将为10万基因组计划中罕见疾病相关基因中存在的1000个错义变体提供亟需的功能信息，并通过这样做建立一个可扩展的管道，用于对更多的临床解释。为了实现这一目标，我们建立了一个跨学科的研究团队，该团队交织了计算基因组学、生物医学信息学、数学、数字化学、生物信息学、过程自动化、功能蛋白质组学、生物化学和细胞生物学等学科。我们的模块化功能基因组变异解释平台建立在成熟的新方法的基础上，并已大规模应用。PTMv将从10万基因组计划的罕见疾病基因小组中提取。它们将被计算建模，并根据它们对蛋白质功能的预测贡献，使用原子键能量倾向分析进行优先排序。我们将建立和部署一个新的端到端变异工程生物信息学工具集，以及高通量过程自动化，以同时测试数百个这样的PTMv在活细胞中的功能贡献。这些功能信息将稳定地整合到欧洲生物信息研究所的PROTVAR资源中，用于国内和国际学术研究影响，并反馈到基因英国公司的研究环境中，以支持罕见疾病患者的临床诊断并改进临床实践指南。

Rare diseases are debilitating, with one third of children suffering from these diseases dying before their fifth birthday. The 100,000 genomes project delivered by Genomics England and NHS England produced a rich catalogue of genomic variation. By associating genome variants and disease for patients suffering from disease symptoms and syndromes, the project succeeded in determining the genetic basis for many rare diseases. This allowed for clinical diagnoses to be provided for some patients within the rare disease group, but most remain undiagnosed.In general terms, genome variants are separated into two classes: those that sit within the protein-coding regions of genes (exonic variants), and those that sit outside of the protein (intronic variants). Exonic variants affect the amino acid sequence that comprises the protein. In some cases, these variants have a reliably predictable effect, such as the production of non-functional shortened versions of the protein. In other cases, a variant can result in the normal amino acid being replaced by an alternative. These so-called missense variants can subtly affect protein form and function, and it is more challenging to predict the effect a missense variant will have upon a protein. For example, the post-translational modification (PTM) of proteins can regulate their function, and sometimes these missense variants affect these PTMs, changing the amino acid that the modification is normally attached onto. Hypothetically, PTM variants (PTMv) should have a more predictable effect on protein function. To understand how a given missense variant affects protein function it is necessary to experimentally determine the impact of the variant in a laboratory. One result of this is that for most of the missense variants identified in rare disease-associated genes from the 100,000 genomes project, while we can accurately determine correlation, we cannot be certain of causation. Consequently, these missense variants are not used to inform the clinical diagnoses for patients suffering from these rare diseases.We will provide much needed functional information for one thousand missense variants present in rare disease-associated genes from the 100,000 genomes project, and in so doing establish a scalable pipeline for the clinical interpretation of many more. To accomplish this, we have established a cross-disciplinary investigative team that interweaves the disciplines of computational genomics, biomedical informatics, mathematics, digital chemistry, bioinformatics, process automation, functional proteomics, biochemistry, and cell biology. Our modular functional genomics variant interpretation platform is built on well-established and new methods, applied at scale. PTMv will be extracted from the rare disease gene panel of the 100,000 genomes project. They will be computationally modelled and prioritised according to their predicted contribution to protein function using an atomistic bond energy propensity analysis. We will build and deploy a new end-to-end variant engineering bioinformatic toolset alongside high-throughput process automation to test the functional contribution of hundreds of these PTMv in live cells at the same time. This functional information will be stably integrated into the European Bioinformatic Research Institute's Protvar resource for national and international academic research impact, and be fed back into Genomics England's research environment to support clinical diagnoses for rare disease patients and improve clinical practice guidelines.