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MolPhase, an advanced prediction algorithm for protein phase separation

Qiyu Liang,Nana Peng,Yi Xie,Nivedita Kumar,Weibo Gao,Yansong Miao

Published 2024 in EMBO Journal

ABSTRACT

We introduce MolPhase, an advanced algorithm for predicting protein phase separation (PS) behavior that improves accuracy and reliability by utilizing diverse physicochemical features and extensive experimental datasets. MolPhase applies a user-friendly interface to compare distinct biophysical features side-by-side along protein sequences. By additional comparison with structural predictions, MolPhase enables efficient predictions of new phase-separating proteins and guides hypothesis generation and experimental design. Key contributing factors underlying MolPhase include electrostatic pi-interactions, disorder, and prion-like domains. As an example, MolPhase finds that phytobacterial type III effectors (T3Es) are highly prone to homotypic PS, which was experimentally validated in vitro biochemically and in vivo in plants, mimicking their injection and accumulation in the host during microbial infection. The physicochemical characteristics of T3Es dictate their patterns of association for multivalent interactions, influencing the material properties of phase-separating droplets based on the surrounding microenvironment in vivo or in vitro. Robust integration of MolPhase’s effective prediction and experimental validation exhibit the potential to evaluate and explore how biomolecule PS functions in biological systems. The efficiency of functional protein phase-separation relies heavily on intrinsic and environmental physical-chemical attributes. MolPhase, a machine-learning engine trained on 606 experimentally derived sequences and employing 39 physical features, effectively predicts and analyzes biophysical features associated with phase separation. MolPhase, a machine-learning predictor, improves accuracy and effectively demonstrates the regulatory biophysical properties of phase-separating proteins. In vivo and in vitro analysis of phytobacterial type III effectors reveals a significantly higher propensity for phase separation compared to the phytobacterial proteome. In addition to effector proteins, DNA and RNA regulatory proteins in phytobacteria also exhibit a propensity for phase separation. MolPhase, a machine-learning predictor, improves accuracy and effectively demonstrates the regulatory biophysical properties of phase-separating proteins. In vivo and in vitro analysis of phytobacterial type III effectors reveals a significantly higher propensity for phase separation compared to the phytobacterial proteome. In addition to effector proteins, DNA and RNA regulatory proteins in phytobacteria also exhibit a propensity for phase separation. A user-friendly predictor correlates propensity for phase separation with key biophysical features, and can be applied to study phytobacterial type III effectors.

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