AI-driven drug discovery company Insilico Medicine and the University of Cambridge have discovered an approach to identify therapeutic targets for human diseases associated with protein phase separation (PPS).
In this study, the researchers combined Insilico’s artificial intelligence (AI)-driven target identification engine PandaOmics with the FuzDrop method for predicting protein separation to identify PPS-prone disease-associated proteins.
Recent research has demonstrated that PPS is widely present in cells and drives a variety of important biological functions. PPS can create clogs or aggregates of molecules linked to neurodegenerative diseases like Alzheimer’s and Parkinson’s, while poorly formed cellular condensates could contribute to cancers and the ageing process.
PandaOmics integrates multiple omics and text-based AI bioinformatics models to assess the potential of proteins as therapeutic targets.
The FuzDrop is a tool introduced by Professor Michele Vendruscolo’s group at the University of Cambridge, which calculates the propensity of a protein to undergo spontaneous phase separation, aiding in the identification of proteins prone to forming liquid-liquid phase-separated condensates.
“It has been challenging so far to understand the role of protein phase separation in cellular functions,” said Professor Vendruscolo, Co-Director, Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge. “Even more difficult has been to clarify the exact nature of its association with human disease. By working with Insilico Medicine, we have provided a roadmap for researchers to navigate this complex terrain.”
Using this approach, the researchers validated the differential phase separation behaviours of three predicted Alzheimer’s disease targets (MARCKS, CAMKK2 and p62) in two cell models of Alzheimer’s disease, which support their potential as therapeutic targets.
By modulating the formation and behaviour of these condensates, it may be possible to develop novel interventions to mitigate the pathological processes associated with Alzheimer’s disease.