计算生物学
核糖核酸
应力颗粒
生物
随机森林
RNA结合蛋白
计算机科学
伪氨基酸组成
氨基酸
生物信息学
翻译(生物学)
人工智能
遗传学
基因
二肽
信使核糖核酸
作者
Zahoor Ahmed,Kiran Shahzadi,Yan-Ting Jin,Rui Li,Biffon Manyura Momanyi,Hasan Zulfiqar,Ning Lin,Hao Lin
出处
期刊:Proteomics
[Wiley]
日期:2024-06-02
卷期号:24 (21-22)
被引量:3
标识
DOI:10.1002/pmic.202400044
摘要
Abstract RNA‐dependent liquid‐liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry‐based methods for identifying these proteins are time‐consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA‐dependent and 606 non‐RNA‐dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K‐spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA‐dependent LLPS proteins. To enhance the accessibility of the model, a user‐centric web server has been established and can be accessed via the link: http://rpp.lin‐group.cn .
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