Bacterial systems have long been of use in toxicology. In addition to providing general models of enzymes and paradigms for biochemistry and molecular biology, they have been adapted to practical genotoxicity assays. More recently, bacteria also have been used in the production of mammalian enzymes of relevance to toxicology. Escherichia coli has been used to express cytochrome P450, NADPH-cytochrome P450 reductase, flavin-containing monooxygenase, glutathione S-transferase, quinone reductase, sulfotransferase, N-acetyltransferase, UDP-glucuronosyl transferase, and epoxide hydrolase enzymes from humans and experimental animals. The expressed enzymes have been utilized in a variety of settings, including coupling with bacterial genotoxicity assays. Another approach has involved expression of mammalian enzymes directly in bacteria for use in genotoxicity systems. Particularly with Salmonella typhimurium. Applications include both the reversion mutagenesis assay and a system using a chimera with an SOS-response indicator and a reporter.

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

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