Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands.
Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, 6525 GA Nijmegen, the Netherlands.
Cell Rep. 2021 Feb 2;34(5):108705. doi: 10.1016/j.celrep.2021.108705.
Membraneless organelles are liquid condensates, which form through liquid-liquid phase separation. Recent advances show that phase separation is essential for cellular homeostasis by regulating basic cellular processes, including transcription and signal transduction. The reported number of proteins with the capacity to mediate protein phase separation (PPS) is continuously growing. While computational tools for predicting PPS have been developed, obtaining a proteome-wide overview of PPS probabilities has remained challenging. Here, we present a phase separation analysis and prediction (PSAP) machine-learning classifier that, based solely on the amino acid content of a training set of known PPS proteins, can determine the phase separation likelihood for each protein in a given proteome. Through comparison with PPS databases, existing predictors, and experimental evidence, we demonstrate the validity and advantages of the PSAP classifier. We anticipate that the PSAP predictor provides a useful tool for future research aimed at identifying phase separating proteins in health and disease.
无膜细胞器是通过液-液相分离形成的液态凝聚物。最近的进展表明，通过调节包括转录和信号转导在内的基本细胞过程，相分离对于细胞内稳态至关重要。具有介导蛋白质相分离（PPS）能力的蛋白质的报道数量一直在不断增加。虽然已经开发了用于预测 PPS 的计算工具，但获得 PPS 概率的全蛋白质组概览仍然具有挑战性。在这里，我们提出了一种相分离分析和预测（PSAP）机器学习分类器，该分类器仅基于一组已知 PPS 蛋白质的氨基酸含量，即可确定给定蛋白质组中每个蛋白质的相分离可能性。通过与 PPS 数据库、现有预测器和实验证据进行比较，我们证明了 PSAP 分类器的有效性和优势。我们预计，PSAP 预测器将为未来旨在识别健康和疾病中相分离蛋白质的研究提供有用的工具。
