Department of Cell Biology, Duke University School of Medicine, Durham, North Carolina 27710, USA.
Department of Cell Biology, Duke University School of Medicine, Durham, North Carolina 27710, USA
RNA. 2024 May 16;30(6):644-661. doi: 10.1261/rna.079848.123.
UV-crosslinking has proven to be an invaluable tool for the identification of RNA-protein interactomes. The paucity of methods for distinguishing background from bona fide RNA-protein interactions, however, makes attribution of RNA-binding function on UV-crosslinking alone challenging. To address this need, we previously reported an RNA-binding protein (RBP) confidence scoring metric (RCS), incorporating both signal-to-noise (:) and protein abundance determinations to distinguish high- and low-confidence candidate RBPs. Although RCS has utility, we sought a direct metric for quantification and comparative evaluation of protein-RNA interactions. Here we propose the use of protein-specific UV-crosslinking efficiency (%CL), representing the molar fraction of a protein that is crosslinked to RNA, for functional evaluation of candidate RBPs. Application to the HeLa RNA interactome yielded %CL values for 1097 proteins. Remarkably, %CL values span over five orders of magnitude. For the HeLa RNA interactome, %CL values comprise a range from high efficiency, high specificity interactions, e.g., the Elav protein HuR and the Pumilio homolog Pum2, with %CL values of 45.9 and 24.2, respectively, to very low efficiency and specificity interactions, for example, the metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase, and alpha-enolase, with %CL values of 0.0016, 0.006, and 0.008, respectively. We further extend the utility of %CL through prediction of protein domains and classes with known RNA-binding functions, thus establishing it as a useful metric for RNA interactome analysis. We anticipate that this approach will benefit efforts to establish functional RNA interactomes and support the development of more predictive computational approaches for RBP identification.
紫外交联已被证明是鉴定 RNA-蛋白质互作组的一种非常有价值的工具。然而，区分背景与真实 RNA-蛋白质相互作用的方法很少，因此仅凭紫外交联来确定 RNA 结合功能具有挑战性。为了解决这一需求，我们之前报道了一种 RNA 结合蛋白（RBP）置信评分指标（RCS），该指标结合了信号与噪声（:）和蛋白质丰度的测定，以区分高可信度和低可信度的候选 RBP。尽管 RCS 具有实用性，但我们还是希望找到一种直接的指标，用于定量和比较评估蛋白质-RNA 相互作用。在这里，我们提出使用蛋白质特异性紫外交联效率（%CL），代表与 RNA 交联的蛋白质的摩尔分数，用于候选 RBP 的功能评估。将其应用于 HeLa RNA 互作组，得到了 1097 种蛋白质的%CL 值。值得注意的是，%CL 值跨越了五个数量级。对于 HeLa RNA 互作组，%CL 值的范围从高效、高特异性的相互作用，例如 Elav 蛋白 HuR 和 Pumilio 同源物 Pum2，其%CL 值分别为 45.9 和 24.2，到非常低效和特异性的相互作用，例如代谢酶甘油醛-3-磷酸脱氢酶、果糖-1,6-二磷酸醛缩酶和α-烯醇酶，其%CL 值分别为 0.0016、0.006 和 0.008。我们通过预测具有已知 RNA 结合功能的蛋白质结构域和类别进一步扩展了%CL 的用途，从而将其确立为 RNA 互作组分析的有用指标。我们预计，这种方法将有助于建立功能性 RNA 互作组，并支持开发更具预测性的 RBP 识别计算方法。
