Predicting the functional impact of alternative splicing on protein-protein interactions using an integrated approach

前体mRNA的选择性剪接（AS）提供了一种重要的遗传控制手段， 参与大多数基因的表达。AS产生多种异构体，其可表现出不同的稳定性， 分子结合能力和表型效应，从而大大扩展了 基因.这些功能在人类疾病中经常失调，导致异常表达的同种型 可以作为细胞通路的驱动器和“重布线器”。鉴于AS在几乎所有方面的内在作用， 在生物学的发展中，迫切需要了解同种型功能的工具和技术。 不幸的是，预测同种型的功能是非常困难的，因为我们对它们的了解非常粗略。 同种型活性的分子决定因素以及实验数据集的缺乏， 异构体分辨率。由于缺乏稳健的方法，这些实验本身就具有挑战性 用于体内同种型分辨率下的检测、作图和表型分型。因此，其中一个 在基因组学和蛋白质组学领域的最大差距是理解功能和进化 人类蛋白质组惊人的复杂性的含义。 为了缩小这一差距，我们必须重新制定现有的办法。在这里，我们建议建立 计算和实验方法在整个开发过程中相互交织， 评价生命周期。计算方法，利用不断改进的机器学习算法， 可以预测高覆盖率下AS的影响。另一方面，实验验证对于 基准的预测，以及阐明迄今未表征的功能异构体的特点。 该项目的目标是开发（i）蛋白质异构体稳定性的预测因子，即“第一线证据”， 异构体功能的先决条件，（ii）一种新的生物信息学方法来研究AS的“重新布线”效应 异构体对蛋白质相互作用，和（iii）一种新的措施，选择性剪接影响因子，预测 基于指标（如相互作用和表达模式的丧失）的同种型的功能作用，以及 将这一概念应用于计算机模拟和体外/体内（基于细胞的测定）测定AS诱导的表型。每个 计算阶段将与高度定制和新颖的实验方法密切配合- 大规模同种型蛋白质基因组学、相互作用组学和功能测定实验-以验证和 对预测器进行基准测试，并进入迭代计算-实验的“良性循环”。

Alternative splicing (AS) of precursor mRNA provides an important means of genetic control and is a crucial step in the expression of most genes. AS gives rise to multiple isoforms that can exhibit differential stabilities, molecular binding capabilities, and phenotypic effects, thereby greatly expanding the functional capacity of genes. These functions are frequently deregulated in human disease, leading to aberrantly expressed isoforms that can act as drivers and “rewirers” of cellular pathways. Given the intrinsic role of AS in nearly every aspect of biology, tools and technologies for the functional understanding of isoforms are desperately needed. Unfortunately, prediction of isoform functions are notoriously difficult, due to our only crude understanding of the molecular determinants of isoform activities as well as a paucity of experimental datasets annotated at isoform resolution. The experiments, in turn, are challenging in their own right, due to a lack of robust methods for the detection, mapping, and phenotyping at isoform-resolution in vivo. As a consequence, one of the biggest gaps in the genomics and proteomics fields is an understanding the functional and evolutionary implications of the astonishing complexity of the human proteome. To make progress towards this gap, we must reformulate existing approaches. Here, we propose to build computational and experimental methodologies that are intertwined across the entire development and evaluation lifecycle. Computational approaches, capitalizing on ever-improving machine learning algorithms, can predict the effect of AS at high coverage. Experimental validation, on the other hand, is critical to benchmark the predictions as well as shed light on heretofore uncharacterized features of isoform functionality. The goals of this project are to develop (i) a predictor of protein isoform stability, a “first line of evidence” and prerequisite of isoform functionality, (ii) a novel bioinformatics approach to study the “rewiring” effects of AS isoforms on protein interactions, and (iii) a novel measure, the alternative splicing impact factor, that predicts the functional role of an isoform based on metrics such as the loss of interaction and expression patterns, and apply this concept to determine AS-induced phenotypes in-silico and in vitro/vivo (cell-based assay). Each computational stage will be closely complemented with a highly customized and novel experimental approach– large-scale isoform proteogenomics, interactomics, and functional assays experiments–to validate and benchmark the predictors, as well as feed into an iterative computation-experimental “virtuous cycle”.