Prediction of liquid-liquid phase separating proteins using machine learning.
Prediction of liquid-liquid phase separating proteins using machine learning.
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DOI：
10.1186/s12859-022-04599-w
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发表时间：
2022-02-15
影响因子：
3
通讯作者：
Pei J
中科院分区：
文献类型：
--
作者：
Chu X;Sun T;Li Q;Xu Y;Zhang Z;Lai L;Pei J
细胞中生物分子的液-液相分离（LLPS）是形成无膜细胞器的基础，无膜细胞器是蛋白质、核酸或两者的浓缩物，并且在细胞功能中起关键作用。LLPS的失调与许多疾病有关。近年来，尽管人们对生物分子的LLP进行了深入的研究，但对相分离蛋白（PSP）的存在和分布的认识仍然滞后。因此，发展预测PSP的计算方法对于全面了解LLPS的生物学功能具有重要意义。 基于LLPSDB中收集的PSP，我们开发了一个基于序列的LLPS蛋白预测工具（PSPPredictor），这是一个不依赖于特定蛋白类型的PSP预测通用目的的尝试。该方法在蛋白质嵌入阶段结合了组分信息和序列信息，并采用机器学习算法进行最终预测。该方法实现了94.71%的十倍交叉验证准确率，并优于以前报道的PSP预测工具。为了进一步的应用，我们建立了一个用户友好的PSPPredictor Web服务器（http：//www.pSPdl.cn/PSPPredictor），这是可访问的潜在PSP的预测。 PSPPredictor可以识别应激颗粒的新支架蛋白，并预测人类基因组中的PSPs候选者，以供进一步研究。为了进一步的应用，我们建立了一个用户友好的PSPPredictor网络服务器（http：//www.pspdl.cn/PSPPredictor），它提供了有价值的信息，潜在的PSP识别。在线版本包含补充材料，可通过10.1186/s12859-022-04599-w获得。
The liquid–liquid phase separation (LLPS) of biomolecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular function. Dysregulation of LLPS is implicated in a number of diseases. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS. Based on the PSPs collected in LLPSDB, we developed a sequence-based prediction tool for LLPS proteins (PSPredictor), which is an attempt at general purpose of PSP prediction that does not depend on specific protein types. Our method combines the componential and sequential information during the protein embedding stage, and, adopts the machine learning algorithm for final predicting. The proposed method achieves a tenfold cross-validation accuracy of 94.71%, and outperforms previously reported PSPs prediction tools. For further applications, we built a user-friendly PSPredictor web server (http://www.pkumdl.cn/PSPredictor), which is accessible for prediction of potential PSPs. PSPredictor could identifie novel scaffold proteins for stress granules and predict PSPs candidates in the human genome for further study. For further applications, we built a user-friendly PSPredictor web server (http://www.pkumdl.cn/PSPredictor), which provides valuable information for potential PSPs recognition. The online version contains supplementary material available at 10.1186/s12859-022-04599-w.