This project leverages the advanced capabilities of AlphaFold2, a state-of-the-art protein structure prediction tool, to predict the propensity of proteins to undergo liquid-liquid phase separation (LLPS). The research focuses on developing computational models to identify single protein phase separation and co-condensation of protein pairs. Utilizing a dataset of experimentally validated proteins, the project aims to enhance our understanding of the biological roles of phase separation and its implications in health and disease. The resulting predictive models promise to significantly aid in identifying proteins involved in biomolecular condensates, offering insights that could inform therapeutic strategies for diseases associated with phase separation. The findings of this research are published at (Zhang et al., 2024). Access the thesis pdf.
References
2024
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AlphaFold2-based prediction of the co-condensation propensity of proteins
Shengyu Zhang, Christine M. Lim, Martina Occhetta, and 1 more author
Proceedings of the National Academy of Sciences, 2024
The phenomenon of protein condensation has been associated with a variety of cellular functions and implicated in a wide range of human diseases. By developing CoDropleT, a predictive model incorporating conformational properties of proteins derived from AlphaFold2, this study introduces a tool for the prediction of proteins that make up protein condensates. This tool could be used for accelerating the finding of proteins involved in protein phase separation and for elucidating the still elusive mechanisms governing protein co-condensation. We also expect CoDropleT to contribute to future explorations of the therapeutic opportunities offered by the modulation of pathological processes generated by the dysregulation of protein condensates. The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.