AI-driven techniques reveal new targets for drug discovery associated with protein phase separation

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"The research team ... presented an approach to identify therapeutic targets for human diseases associated with a phenomenon known as protein phase separation, a recently discovered phenomenon widely present in cells that drives a variety of important biological functions.

Protein phase separation at the wrong place or time could disrupt key cellular functions or create aggregates of molecules linked to neurodegenerative diseases. It is believed that poorly formed cellular condensates could contribute to cancers and might help explain the aging process.

researchers ... developed a method for finding new targets for drug discovery in diseases caused by dysregulation of the protein phase separation process. The team found that they could replicate disease characteristics in cells by controlling the behaviour of these targets. ..."

From the significance and abstract:

"Significance

Given the emerging association between human disease and the protein phase separation (PPS) process, it is of great interest to identify possible targets for therapeutic interventions based on PPS regulation. This goal, however, is challenging because our understanding of the molecular origins of PPS-associated diseases is still in its infancy. Here, we present an approach based on the intersection of two components: 1) an analysis of a variety of parameters commonly used to identify therapeutic targets through the multiomic PandaOmics platform and 2) an assessment of the propensity of proteins to undergo PPS through the FuzDrop method. We apply this approach to prioritize human diseases for PPS-based interventions, and illustrate its implementation in the case of Alzheimer’s disease.

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."

AI-driven techniques reveal new targets for drug discovery | University of Cambridge Researchers have developed a method to identify new targets for human disease, including neurodegenerative conditions such as Alzheimer’s disease.

Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation (open access)

Fig. 2 Disease landscape representing opportunities for PPS-based therapeutics for 64 diseases across 10 disease groups.