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

Abstract

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.