Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation
Christine Maria Lima, Alicia González Díaza, Mónika Fuxreiterb, Frank W. Punc, Alex A. Zhavoronkovc, Michele Vendruscoloa
- aYusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge CB2 1EW, United Kingdom
- bDepartment of Biomedical Sciences, University of Padova, Padova 35131, Italy
- cInsilico Medicine, Hong Kong Science and Technology Park, Hong Kong, China
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Abstract
The phenomenon of protein phase separation (PPS) underlies a wide range of cellular functions. Correspondingly, the dysregulation of the PPS process has been associated with numerous human diseases. To enable therapeutic interventions based on the regulation of this association, possible targets should be identified. For this purpose, we present an approach that combines the multiomic PandaOmics platform with the FuzDrop method to identify PPS-prone disease-associated proteins. Using this approach, we prioritize candidates with high PandaOmics and FuzDrop scores using a profiling method that accounts for a wide range of parameters relevant for disease mechanism and pharmacological intervention. We validate the differential phase separation behaviors of three predicted Alzheimer's disease targets (MARCKS, CAMKK2, and p62) in two cell models of this disease. Overall, the approach that we present generates a list of possible therapeutic targets for human diseases associated with the dysregulation of the PPS process.
TL;DRThis work presents an approach based on the intersection of two components: an analysis of a variety of parameters commonly used to identify therapeutic targets through the multiomic PandaOmics platform and an assessment of the propensity of proteins to undergo PPS through the FuzDrop method. (Semantic Scholar)