Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation.

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.

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

A Kinetic Map of the Influence of Biomimetic Lipid Model Membranes on Aß42 Aggregation.

The aggregation of the amyloid ß (Aß) peptide is one of the molecular hallmarks of Alzheimer's disease (AD). Although Aß deposits have mostly been observed extracellularly, various studies have also reported the presence of intracellular Aß assemblies. Because these intracellular Aß aggregates might play a role in the onset and progression of AD, it is important to investigate their possible origins at different locations of the cell along the secretory pathway of the amyloid precursor protein, from which Aß is derived by proteolytic cleavage. Senile plaques found in AD are largely composed of the 42-residue form of Aß (Aß42). Intracellularly, Aß42 is produced in the endoplasmatic reticulum (ER) and Golgi apparatus. Since lipid bilayers have been shown to promote the aggregation of Aß, in this study, we measure the effects of the lipid membrane composition on the in vitro aggregation kinetics of Aß42. By using large unilamellar vesicles to model cellular membranes at different locations, including the inner and outer leaflets of the plasma membrane, late endosomes, the ER, and the Golgi apparatus, we show that Aß42 aggregation is inhibited by the ER and Golgi model membranes. These results provide a preliminary map of the possible effects of the membrane composition in different cellular locations on Aß aggregation and suggest the presence of an evolutionary optimization of the lipid composition to prevent the intracellular aggregation of Aß.