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，是一种至关重要的 参与大多数基因的表达。当产生可表现出不同稳定性的多种异构体时， 分子结合能力和表型效应，从而极大地扩展了细胞的功能容量 基因。这些功能在人类疾病中经常被解除调控，导致异构体异常表达。 它们可以充当细胞通路的驱动者和“改编者”。鉴于AS在几乎所有方面的内在作用 对于生物学来说，迫切需要工具和技术来了解异构体的功能。 不幸的是，同形函数的预测是出了名的困难，因为我们对 异构体活性的分子决定因素以及少数实验数据集在 同形分辨率。反过来，由于缺乏可靠的方法，这些实验本身就具有挑战性。 用于体内异构体分辨率的检测、定位和表型鉴定。因此，其中一个 基因组学和蛋白质组学领域最大的差距是对功能和进化的理解 人类蛋白质组惊人复杂的含义。 为了在这一差距方面取得进展，我们必须重新制定现有的方法。在这里，我们建议建立 整个开发过程中相互交织的计算和实验方法 评估生命周期。计算方法，利用不断改进的机器学习算法， 可以预测AS在高覆盖率下的效果。另一方面，实验验证对于 对这些预测进行基准测试，并阐明迄今尚未确定的异构体功能特征。 这个项目的目标是开发(I)蛋白质异构体稳定性的预测指标，一个“第一线证据”和 异构体功能的前提条件，(Ii)研究AS“重连”效应的生物信息学新方法 异构体对蛋白质相互作用的影响，以及(Iii)一种新的测量方法，即选择性剪接影响因子，它预测 基于诸如相互作用和表达模式丧失等指标的异构体的功能作用，以及 应用这一概念来确定砷在硅胶和体外/活体(基于细胞的分析)中诱导的表型。每个人 计算阶段将与高度定制和新颖的实验方法密切配合- 大规模的异构体蛋白质组学、相互作用和功能分析实验-以验证和 对预测者进行基准测试，并将其送入迭代计算--实验“良性循环”中。

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”.