Deciphering the evolution of flavin-dependent monooxygenase stereoselectivity using ancestral sequence reconstruction

Abstract

Controlling the selectivity of a reaction is critical for target-oriented synthesis. Accessing complementary selectivity profiles enables divergent synthetic strategies, but is challenging to achieve in biocatalytic reactions given enzymes’ innate preferences of a single selectivity. Thus, it is critical to understand the structural features that control selectivity in biocatalytic reactions to achieve tunable selectivity. Here, we investigate the structural features that control the stereoselectivity in an oxidative dearomatization reaction that is key to making azaphilone natural products. Crystal structures of enantiocomplementary biocatalysts guided the development of multiple hypotheses centered on the structural features that control the stereochemical outcome of the reaction; however, in many cases, direct substitutions of active site residues in natural proteins led to inactive enzymes. Ancestral sequence reconstruction (ASR) and resurrection were employed as an alternative strategy to probe the impact of each residue on the stereochemical outcome of the dearomatization reaction. These studies suggest that two mechanisms are active in controlling the stereochemical outcome of the oxidative dearomatization reaction: one involving multiple active site residues in AzaH and the other dominated by a single Phe to Tyr switch in TropB and AfoD. Moreover, this study suggests that the flavin-dependent monooxygenases (FDMOs) adopt simple and flexible strategies to control stereoselectivity, which has led to stereocomplementary azaphilone natural products produced by fungi. This paradigm of combining ASR and resurrection with mutational and computational studies showcases sets of tools for understanding enzyme mechanisms and provides a solid foundation for future protein engineering efforts.

Biocatalytic Stereoselective Oxidation of 2-Arylindoles

“…33,43,45 This is a surprising result considering that the indole substrates tested are structurally quite different from the resorcinol substrates that TropB, AfoD, and AzaH are known to oxidize. 33,43,45 In the SSN and latent space, these two ancestral sequences are located in the same cluster (SSN) and in close proximity (latent space) to their contemporary counterparts (Figure 2b). Some library building approaches focus on only sampling clusters within the SSN that are known to act on a given scaffold of interest.…”

“…Two ancestral proteins, Anc280 and Anc280a, 33 generated the oxidized products of melatonin (26), 3-methylindole (25), and 2-ethyl-3-methylindole (27). These ancestors are the computationally reconstructed predecessors of TropB, AfoD, and AzaH, which are previously characterized enzymes that natively perform oxidative dearomatization on resorcinol substrates.…”

“…One portion of the library includes 30 ancestral FDMOs reconstructed from a phylogenetic tree of TropB, AfoD, and AzaH and other 274 extant fungal FDMOs . The diversity of the ancestral FDMOs is relatively limited, and the transformations of most of these enzymes are likely related to the oxidative dearomatization of resorcinol substrates. ,− However, we sought to leverage the purported superior thermal stability and potentially higher substrate promiscuity of these ancestral enzymes to observe their activity with different substrates.…”

“…Beyond these enzymes and their reported or proposed natural functions with indoles, we employed a combination of bioinformatics and deep learning strategies to sample the sequence space of FDMOs in PF01494. We used three sampling strategies including ancestral sequences, 33 SSN sampling, 27 and latent space sampling. 32 One portion of the library includes 30 ancestral FDMOs reconstructed from a phylogenetic tree of TropB, AfoD, and AzaH and other 274 extant fungal FDMOs.…”

“…30,31 We incorporated three approaches to sampling sequences for this library: (1) guiding enzyme selection based on a latent space model, 32 (2) selecting sequences from a sequence similarity network (SSN), 27 and (3) including ancestral FDMOs reconstructed computationally (Figure 1c). 33 Specifically, we anticipated that this curated library would enable the discovery of effective biocatalysts for a variety of small-molecule oxidations and herein report the use of this strategy for the chemo-and stereoselective oxidation of 2-arylindole substrates (Figure 1d).…”