English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Predicting protein condensate formation using machine learning.

MPS-Authors
/cone/persons/resource/persons222397

Wang,  Jie
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/cone/persons/resource/persons219545

Poser,  Ina
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Mierlo, G. v., Jansen, J. R. G., Wang, J., Poser, I., Heeringen, S. J. v., & Vermeulen, M. (2021). Predicting protein condensate formation using machine learning. Cell reports, 34(5): 108705. doi:10.1016/j.celrep.2021.108705.


Cite as: https://hdl.handle.net/21.11116/0000-0008-DAF1-2
Abstract
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.