Höppner Sabine, Schröder Bernd, Fluhrer Regina
Biochemistry and Molecular Biology, Faculty of Medicine, Institute of Theoretical Medicine, University of Augsburg, Germany.
Institute for Physiological Chemistry, Technische Universität Dresden, Germany.
FEBS J. 2023 Dec;290(23):5456-5474. doi: 10.1111/febs.16968. Epub 2023 Oct 13.
More than 20 years ago, signal peptide peptidase (SPP) and its homologues, the signal peptide peptidase-like (SPPL) proteases have been identified based on their sequence similarity to presenilins, a related family of intramembrane aspartyl proteases. Other than those for the presenilins, no high-resolution structures for the SPP/SPPL proteases are available. Despite this limitation, over the years bioinformatical and biochemical data have accumulated, which altogether have provided a picture of the overall structure and topology of these proteases, their localization in the cell, the process of substrate recognition, their cleavage mechanism, and their function. Recently, the artificial intelligence-based structure prediction tool AlphaFold has added high-confidence models of the expected fold of SPP/SPPL proteases. In this review, we summarize known structural aspects of the SPP/SPPL family as well as their substrates. Of particular interest are the emerging substrate recognition and catalytic mechanisms that might lead to the prediction and identification of more potential substrates and deeper insight into physiological and pathophysiological roles of proteolysis.
20多年前,信号肽肽酶(SPP)及其同系物,即信号肽肽酶样(SPPL)蛋白酶,已基于它们与早老素(一种相关的跨膜天冬氨酸蛋白酶家族)的序列相似性而被鉴定出来。除了早老素的结构外,目前尚无SPP/SPPL蛋白酶的高分辨率结构。尽管存在这一局限性,但多年来积累了生物信息学和生化数据,这些数据共同描绘了这些蛋白酶的整体结构和拓扑结构、它们在细胞中的定位、底物识别过程、切割机制及其功能。最近,基于人工智能的结构预测工具AlphaFold增加了SPP/SPPL蛋白酶预期折叠的高可信度模型。在这篇综述中,我们总结了SPP/SPPL家族已知的结构方面及其底物。特别令人感兴趣的是新兴的底物识别和催化机制,这些机制可能会导致预测和识别更多潜在底物,并更深入地了解蛋白水解的生理和病理生理作用。