Sequence-based Prediction of the Cellular Toxicity Associated with Amyloid Aggregation within Protein Condensates

Various neurological dysfunctions are associated with
cytotoxic
amyloid-containing aggregates formed through the irreversible maturation
of protein condensates generated by phase separation. Here, we investigate
the amino acid code for this cytotoxicity using TDP-43 deep-sequencing
data. Within the droplet landscape framework, we analyze the impact
of mutations in the amyloid core, aggregation hot-spot, and droplet-promoting
residues on TDP-43 cytotoxicity. Our analysis suggests that TDP-43
mutations associated with low cytotoxicity moderately decrease the
probability of droplet formation while increasing the probability
of multimodal binding. These mutations promote both ordered and disordered
binding modes, thus facilitating the conversion between the droplet
and amyloid states. Based on this understanding, we develop an extension
of the FuzDrop method for the sequence-based prediction of the cytotoxicity
of aging condensates and test it over 20,000 TDP-43 variants. Our
analysis provides insight into the amino acid code that regulates
the cytotoxicity associated with the maturation of liquid-like condensates
into amyloid-containing aggregates, suggesting that, at least in the
case of TDP-43, mutations that promote aggregation tend to decrease
cytotoxicity, while those that promote droplet formation tend to increase
cytotoxicity.