SARS-CoV-2 has recently emerged as the seventh coronavirus known to infect humans. 1 Since its discovery, SARS-CoV-2 has resulted in >6.9 million documented human infections worldwide and >400 000 deaths reported to date, according to the World Health Organization (https://www. who.int/emerg encie s/disea ses/novel-coron aviru s-2019/situa tion-reports; accessed on 6/9/20). Given the magnitude of this ongoing pandemic, a molecular-level understanding of SARS-CoV-2 infection and host interaction is of paramount importance for rational drug development. The nucleocapsid ("N") protein of the closely related SARS-CoV 2,3 is essential for the formation of new virions and is the most common antigen of host-produced antibodies during infection by the closely related SARS-CoV virus, 4 and the SARS-CoV-2 N

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Hundreds of human proteins contain prion-like domains, which are a subset of low-complexity domains with high amino acid compositional similarity to yeast prion domains. A recently characterized mutation in the prion-like domain of the human heterogeneous nuclear ribonucleoprotein hnRNPA2B1 increases the aggregation propensity of the protein and causes multisystem proteinopathy. The mutant protein forms cytoplasmic inclusions when expressed in , the mutation accelerates aggregation, and the mutant prion-like domain can substitute for a portion of a yeast prion domain in supporting prion activity. To examine the relationship between amino acid sequence and aggregation propensity, we made a diverse set of point mutations in the hnRNPA2B1 prion-like domain. We found that the effects on prion formation in and aggregation could be predicted entirely based on amino acid composition. However, composition was an imperfect predictor of inclusion formation in ; while most mutations showed similar behaviors in yeast,, and in , a few showed anomalous behavior. Collectively, these results demonstrate the significant progress that has been made in predicting the effects of mutations on intrinsic aggregation propensity while also highlighting the challenges of predicting the effects of mutations in more complex organisms.

Prions are self-propagating infectious protein isoforms. A growing number of prions have been identified in yeast, each resulting from the conversion of soluble proteins into an insoluble amyloid form. These yeast prions have served as a powerful model system for studying the causes and consequences of prion aggregation. Remarkably, a number of human proteins containing prion-like domains, defined as domains with compositional similarity to yeast prion domains, have recently been linked to various human degenerative diseases, including amyotrophic lateral sclerosis (ALS). This suggests that the lessons learned from yeast prions may help in understanding these human diseases. In this review, we examine what has been learned about the amino acid sequence basis for prion aggregation in yeast, and how this information has been used to develop methods to predict aggregation propensity. We then discuss how this information is being applied to understand human disease, and the challenges involved in applying yeast prediction methods to higher organisms.

Proteins with low-complexity domains continue to emerge as key players in both normal and pathological cellular processes. Although low-complexity domains are often grouped into a single class, individual low-complexity domains can differ substantially with respect to amino acid composition. These differences may strongly influence the physical properties, cellular regulation, and molecular functions of low-complexity domains. Therefore, we developed a bioinformatic approach to explore relationships between amino acid composition, protein metabolism, and protein function. We find that local compositional enrichment within protein sequences is associated with differences in translation efficiency, abundance, half-life, protein-protein interaction promiscuity, subcellular localization, and molecular functions of proteins on a proteome-wide scale. However, local enrichment of related amino acids is sometimes associated with opposite effects on protein regulation and function, highlighting the importance of distinguishing between different types of low-complexity domains. Furthermore, many of these effects are discernible at amino acid compositions below those required for classification as low-complexity or statistically-biased by traditional methods and in the absence of homopolymeric amino acid repeats, indicating that thresholds employed by classical methods may not reflect biologically relevant criteria. Application of our analyses to composition-driven processes, such as the formation of membraneless organelles, reveals distinct composition profiles even for closely related organelles. Collectively, these results provide a unique perspective and detailed insights into relationships between amino acid composition, protein metabolism, and protein functions.