Predicting protein condensate formation using machine learning - PubMed (original) (raw)

Predicting protein condensate formation using machine learning

Guido van Mierlo et al. Cell Rep. 2021.

Free article

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.

Keywords: phase separation, machine learning, condensate formation.

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

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