dSCOPE: a software to detect sequences critical for liquid-liquid phase separation
Shihua LiiD, Kai YuiD, Qingfeng ZhangiD, Zexian LiuiD, Jia LiuiD, Huai‐Qiang JuiD, Zhixiang ZuoiD, Xiaoxing LiiD, Zhenlong WangiD, Han ChengiD
Abstract
Membrane based cells are the fundamental structure and function units of organisms, while evidences were increasing that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation, but these studies lacked an effective tool to identify the sequence segments that critical for LLPS (SCOPEs). In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the SCOPEs. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted SCOPEs enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted SCOPEs enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS related research.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 3
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Prediction of liquid-liquid phase separation proteins using machine learning
Tanlin Sun, Qian Li, … Jianfeng Pei·bioRxiv·Nov 15, 2019
- SSPSPredictor: A Sequence and Structure based Deep Learning Model for Predicting Phase-Separating Proteins
Tinglan Wang, Shaofeng Liao, … Zhuqing Zhang·Journal of Chemical Information and ModelingPeer-reviewed·May 29, 2026
- A hybrid model for multiscale prediction and generation of phase separating protein regions
Amir M. MohammadHosseini, Hossein Teimouri, … Amir Pandi·bioRxiv·Jan 27, 2025
- Accurate Predictions of Phase Separating Proteins at Single Amino Acid Resolution
Michele Monti, Jonathan Fiorentino, … Gian Gaetano Tartaglia·Genome biologyPeer-reviewed·Feb 20, 2025
- LLPSWise - fast and accurate prediction of LLPS constituents
Mengchen Pu, Biao Fu, Yingsheng Zhang·bioRxiv·Jul 25, 2022
- BIAPSS - BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences
Aleksandra Elzbieta Badaczewska-Dawid, Davit A. Potoyan·bioRxiv·Feb 11, 2021