Abstract
Brazilian mammarenavirus, or Sabiá virus (SABV), is a New World (NW) arenavirus associated with fulminant hemorrhagic disease in humans and the sole biosafety level 4 microorganism ever isolated in Brazil. Since the isolation of SABV in the 1990s, studies on viral biology have been scarce, with no available countermeasures against SABV infection or disease. Here we provide a comprehensive review of SABV biology, including key aspects of SABV replication, and comparisons with related Old World and NW arenaviruses. SABV is most likely a rodent-borne virus, transmitted to humans, through exposure to urine and feces in peri-urban areas. Using protein structure prediction methods and alignments, we analyzed shared and unique features of SABV proteins (GPC, NP, Z, and L) that could be explored in search of therapeutic strategies, including repurposing intended application against arenaviruses. Highly conserved catalytic activities present in L protein could be targeted for broad-acting antiviral activity among arenaviruses, while protein-protein interactions, such as those between L and the matrix protein Z, have evolved in NW arenaviruses and should be specific to SABV. The nucleoprotein (NP) also shares targetable interaction interfaces with L and Z and exhibits exonuclease activity in the C-terminal domain, which may be involved in multiple aspects of SABV replication. Envelope glycoproteins GP1 and GP2 have been explored in the development of promising cross-reactive neutralizing antibodies and vaccines, some of which could be repurposed for SABV. GP1 remains a challenging target in SABV as evolutive pressures render it the most variable viral protein in terms of both sequence and structure, while antiviral strategies targeting the Z protein remain to be validated. In conclusion, the prediction and analysis of protein structures should revolutionize research on viruses such as SABV by facilitating the rational design of countermeasures while reducing dependence on sophisticated laboratory infrastructure for experimental validation.