Correa Marrero Miguel, van Dijk Aalt D J, de Ridder Dick
Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
Applied Bioinformatics, Bioscience, Wageningen University & Research, Wageningen, The Netherlands.
Proteins. 2017 Sep;85(9):1593-1601. doi: 10.1002/prot.25323. Epub 2017 Jun 10.
Protein turnover is a key aspect of cellular homeostasis and proteome dynamics. However, there is little consensus on which properties of a protein determine its lifetime in the cell. In this work, we exploit two reliable datasets of experimental protein degradation rates to learn models and uncover determinants of protein degradation, with particular focus on properties that can be derived from the sequence. Our work shows that simple sequence features suffice to obtain predictive models of which the output correlates reasonably well with the experimentally measured values. We also show that intrinsic disorder may have a larger effect than previously reported, and that the effect of PEST regions, long thought to act as specific degradation signals, can be better explained by their disorder. We also find that determinants of protein degradation depend on the cell types or experimental conditions studied. This analysis serves as a first step towards the development of more complex, mature computational models of degradation of proteins and eventually of their full life cycle. Proteins 2017; 85:1593-1601. © 2017 Wiley Periodicals, Inc.
蛋白质周转是细胞稳态和蛋白质组动态变化的一个关键方面。然而，关于蛋白质的哪些特性决定其在细胞中的寿命，目前几乎没有达成共识。在这项工作中，我们利用两个可靠的实验性蛋白质降解率数据集来学习模型并揭示蛋白质降解的决定因素，特别关注可从序列推导出来的特性。我们的工作表明，简单的序列特征足以获得预测模型，其输出与实验测量值有合理的相关性。我们还表明，内在无序可能比之前报道的影响更大，并且长期以来被认为作为特定降解信号的PEST区域的影响，可以通过其无序性得到更好的解释。我们还发现蛋白质降解的决定因素取决于所研究的细胞类型或实验条件。该分析是朝着开发更复杂、成熟的蛋白质降解计算模型以及最终其完整生命周期模型迈出的第一步。《蛋白质》2017年；85:1593 - 1601。© 2017威利期刊公司。
