Billions of years ago, the Earth’s atmosphere had very little oxygen. It was only after some bacteria and early plants evolved to harness energy from sunlight that oxygen began to fill the Earth’s environment. Oxygen is highly reactive and can interfere with enzymes and other molecules that are essential to life. Organisms living at this point in history therefore had to adapt to survive in this new oxygen-rich world.

An ancient family of enzymes known as ribonucleotide reductases are used by all free-living organisms and many viruses to repair and replicate their DNA. Because of their essential role in managing DNA, these enzymes have been around on Earth for billions of years. Understanding how they evolved could therefore shed light on how nature adapted to increasing oxygen levels and other environmental changes at the molecular level.

One approach to study how proteins evolved is to use computational analysis to construct a phylogenetic tree. This reveals how existing members of a family are related to one another based on the chain of molecules (known as amino acids) that make up each protein. Despite having similar structures and all having the same function, ribonucleotide reductases have remarkably diverse sequences of amino acids. This makes it computationally very demanding to build a phylogenetic tree.

To overcome this, Burnim, Spence, Xu et al. created a phylogenetic tree using structural information from a part of the enzyme that is relatively similar in many modern-day ribonucleotide reductases. The final result took seven continuous months on a supercomputer to generate, and includes over 6,000 members of the enzyme family.

The phylogenetic tree revealed a new distinct group of ribonucleotide reductases that may explain how one adaptation to increasing levels of oxygen emerged in some family members, while another adaptation emerged in others. The approach used in this work also opens up a new way to study how other highly diverse enzymes and other protein families evolved, potentially revealing new insights about our planet’s past.

The wealth of genomic and metagenomic sequence data that has exploded in recent years provides a new opportunity to reexamine enzyme families using large-scale and robust bioinformatic analyses. A particularly important enzyme family that necessitates such analyses are the ribonucleotide reductases (RNRs). RNRs are used by all free-living organisms and many viruses for the conversion of ribonucleotides to 2ʹ-deoxyribonucleotides in the de novo biosynthesis of DNA precursors (Torrents et al., 2002). RNRs are especially fascinating from an evolutionary perspective as they exhibit high diversity in primary sequence and cofactor requirement (Lundin et al., 2015), yet they share a common fold and radical-based catalytic mechanism for nucleotide reduction (Licht et al., 1996).

Based on current biochemical evidence, the full catalytic cycle is thought to involve three steps in all RNRs (Figure 1—figure supplement 1): cofactor-mediated generation of a thiyl radical at a conserved cysteine in the active site, nucleotide reduction, and re-reduction of the active site after turnover (Greene et al., 2020; Holmgren and Sengupta, 2010). Despite being a diverse enzyme family, the core structure of the catalytic subunit (known as α) is a conserved 10-stranded α/β barrel with the thiyl radical on the so-called ‘finger loop’ which connects the two halves of the barrel (Figure 1; Uhlin and Eklund, 1996). RNRs have been biochemically classified into three major groups based on the cofactor used to generate the thiyl radical (Figure 1—figure supplement 1). Class I RNRs use a ferritin subunit (β) to house a stable radical cofactor and are further subclassified by the metal content of the cofactor (Cotruvo and Stubbe, 2011; Ruskoski and Boal, 2021). For every turnover, the α and β subunits must form a complex to engage in long-range radical transfer to the active site (Kang et al., 2020; Seyedsayamdost et al., 2007). In class II RNRs, the thiyl radical is generated by a 5ʹ-deoxyadenosyl radical (5ʹ-dAdo•) produced from an adenosylcobalamin (AdoCbl) cofactor bound within the active site, and thus these enzymes do not require additional subunits (Licht et al., 1996). In class III RNRs, the thiyl radical is generated by a glycyl radical on the C-terminal domain of the α subunit, which itself is generated by a separate activase enzyme that utilizes S-adenosylmethionine (AdoMet) bound to a [4Fe-4S] cluster for radical chemistry (Wei et al., 2014b). Once the thiyl radical is produced in the active site, nucleotide reduction proceeds via a conserved mechanism for all classes (Licht et al., 1996; Figure 1—figure supplement 1). In most RNRs, nucleotide reduction is coupled with the oxidation of a conserved pair of active-site cysteines (Figure 1—figure supplement 1; Booker et al., 1994; Mao et al., 1992; Wei et al., 2014a), which are ultimately reduced by a thioredoxin system to enable the next round of turnover, although some class III RNRs use formate to directly reduce the active site (Wei et al., 2014b).

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Except for a subset of class II RNRs which are monomeric, all extant RNR α subunits are thought to dimerize at the αA and αB helices in their active forms. Class I and II RNRs dimerize in anti-parallel orientation at these helices, such that the dimer interface is capped on both ends with a functional insertion known as loop 1 (Figure 1B–C, dark green), which is the binding site for nucleotides that control the identity of the substrate in the active site (Larsson et al., 2004; Zimanyi et al., 2016). The binding of these so-called specificity-regulating effectors is coupled to substrate binding via another insertion known as loop 2 (Figure 1B–C, red) (Zimanyi et al., 2016). Class I and II RNRs also share another insertion, a long β-hairpin motif, between the αH helix and βH strand. Although less is known about the function of this insertion, it is thought to be important for binding the radical cofactor (the entire ferritin subunit in the case of class I RNRs and the AdoCbl cofactor in the case of class II RNRs) (Kang et al., 2020; Sintchak et al., 2002; Thomas et al., 2019). Class III RNRs, on the other hand, dimerize at the αA and αB helices such that they are oriented parallel to each other across the interface. This orientation is stabilized by a long helix in place of the loop 1 insertion (Logan et al., 1999; Figure 1D, dark green). Additionally, the class III RNRs contain a glycyl radical domain with a zinc finger motif at the C-terminus. In contrast, the C-termini of class I and II RNRs that have been characterized are unstructured and contain cysteines to re-reduce the active-site cysteines via disulfide exchange (Booker et al., 1994; Mao et al., 1992; Thomas et al., 2019). Thus, overall, class I and II RNRs share more structural similarities with each other than they do with class III RNRs.

The ancestor of modern RNRs, and its subsequent evolution into the three known classes, has long been of interest for its hypothesized importance in transitioning life from an RNA/protein world to a DNA world (Lundin et al., 2015; Lundin et al., 2009; Poole et al., 2002; Reichard, 1993; Stubbe, 2000; Stubbe et al., 2001; Torrents, 2014; Torrents et al., 2002). Class I RNRs have been proposed to be the most recently evolved class as they require molecular oxygen, which became abundant as the Earth’s atmosphere transitioned from anoxic to oxic. In contrast, class III RNRs have been proposed to have emerged before this transition as glycyl radicals and [4Fe-4S] clusters are both extremely oxygen-sensitive (Fontecave et al., 1989) and Fe-S chemistry is thought to have a prebiotic origin (Goldman and Kaçar, 2021). The AdoCbl chemistry used by class II RNRs, on the other hand, is neither oxygen-dependent nor especially oxygen-sensitive. Based on the relative oxygen sensitivities and biosynthetic complexities of the different cofactors, class III RNRs have been proposed to be the most ancient of the RNR family (Reichard, 1997; Riera et al., 1997). However, whether class III predates class II has been debated when considering cofactor availability (Stubbe, 2000). Indeed, cobalamin, AdoMet, and Fe-S clusters have all been proposed to be part of the cofactor set used by the last universal common ancestor (Weiss et al., 2016). It has been proposed in an alternative model that dimeric class II and III RNRs evolved in parallel from a shared ancestor using class II-like chemistry and that class I RNRs then evolved from class II (Lundin et al., 2015). However, this model was derived from a structure-based phylogenetic network (Bryant and Moulton, 2004), which estimates a phylogeny based on pairwise structural alignment scores rather than evolutionary modelling on protein sequences with well-established amino acid substitution models and statistical frameworks. Despite much effort to understand the diversity and evolution of RNRs, the reconstruction of a large-scale, unifying phylogenetic inference that includes all classes of RNRs has been hindered by the significant computational demand imposed by the high sequence diversity of the extant members of the family.

In this study, we used the conserved 10-stranded α/β barrel to anchor highly diverged RNR sequences in a structure-based alignment and used maximum-likelihood (ML) inference to reconstruct the largest RNR phylogeny to-date, consisting of 6779 α sequences, that unifies all classes. Our comprehensive phylogeny shows the parallel development of three major clades, corresponding to the three known classes, with a small, phylogenetically distinct clade, which we denote as class Ø (for convenience, pronounced ‘oh’), placed as an ancestral clade to the class I and II RNRs. Using small-angle X-ray scattering (SAXS), cryogenic-electron microscopy (cryo-EM), and AlphaFold2 (Jumper et al., 2021), we show that the class Ø α is the most minimal RNR structurally characterized thus far. These observations were corroborated by evo-velocity analysis, an alignment-independent method that employs protein language models to infer sequence fitness and evolutionary trajectories (Hie et al., 2022). Together, our analyses indicate that class III RNRs diverged early on and evolved independently of class I and II RNRs, which diverged from an ancestor shared with the minimal, class Ø RNRs. Our evolutionary model provides an explanation for the structural similarities of the class I and II RNRs and also supports the idea that adaptations to an oxygenated atmosphere appeared earlier than initially thought.

As RNR sequences have been diverging for billions of years, pairwise sequence identity is frequently low (<15%), and sequence homology is often undetectable between distant members of the enzyme family. It is therefore challenging to produce an accurate sequence alignment of all RNR classes, without which a phylogenetic inference cannot be performed. Thus, although various models for RNR evolution have been proposed by biochemical reasoning (e.g., based on the availabilities and oxygen sensitivities of the cofactors), prior to our work, phylogenetic inference had only been performed on a very small scale. Early studies included an inference of eight class II sequences (Jordan et al., 1997) performed with Clustal W (Thompson et al., 1994) and an inference with a dataset of 96 class I, II, and III sequences (Torrents et al., 2002). In the latter study, Torrents and co-workers used a neighbor-joining (NJ) algorithm to build a tree using a Poisson model, which assumes that all amino acids have equal probability of change (Torrents et al., 2002). The NJ approach can be understood as a way to build a tree by creating nodes between pairs of sequences that are considered close by a certain metric for distance, which in this case is determined by the Poisson sequence evolution model. This approach differs significantly from modern methods of tree inference in which the tree topology is determined by the sequence evolution model itself (Minh et al., 2020) often in replicate. Furthermore, the sequence availability at the time was too sparse to capture the true diversity of the RNR family, and the dataset lacked class Ø sequences, monomeric class II sequences, as well as many subclasses of the class I sequences.

Since then, small-scale inferences have been performed on subsets of the RNR family on either the α or β subunit with datasets ranging between 10s and 100s of sequences in size (Dwivedi et al., 2013; Lundin et al., 2010; Martínez-Carranza et al., 2020; Rose et al., 2019; Rozman-Grinberg et al., 2018; Sakowski et al., 2014). Among these, the work of Lundin and co-workers has been the most comprehensive with respect to consideration of the different RNR classes (Lundin et al., 2010). The growth of sequence information, however, also meant that greater diversity had to be accounted for by phylogenetic methods, and therefore, phylogenetic inference had not been performed on all RNR classes simultaneously since the seminal work of Torrents et al. In its place, a structure-based phylogenetic network was constructed via an NJ algorithm, where the distance between two sequences was estimated by pairwise structural alignment scores (Lundin et al., 2015). Under the assumption that this network estimates a phylogenetic tree, different possible roots were considered, and although none led to monophyly of the RNR classes, it was proposed that the class I RNRs emerged from the dimeric class II RNRs.

In this study, we succeeded in performing a large-scale phylogenetic reconstruction of the entire, highly diverse RNR family for the first time by using structural information to aid the sequence alignment procedure. To test for alternative hypotheses, we performed ML inference using two different amino acid replacement matrices in replicate (10 trees for each model) and repeated the inference with a different sequence redundancy threshold to test for the effect of taxon sampling on tree topology and evolutionary conclusions. Key bifurcations featured high branch supports and reproducibility in replicate tree inferences, allowing us to make robust interpretations, which were further supported by evo-velocity analysis, a recently introduced method that is independent of multiple sequence alignments (Hie et al., 2022). In contrast to previously proposed models (Lundin et al., 2015), our unified RNR phylogeny supports the evolution of three major, monophyletic clades corresponding to the three biochemically known classes as well as a new phylogenetic clade, which we denoted as class Ø. Together, our bioinformatic analyses support an evolutionary trajectory in which the O₂-sensitive class III RNRs are the earliest to diverge, and the class Ø clade shares an ancestor with the LCA of the O₂-dependent class I and O₂-tolerant class II RNRs. It is important to note that all evolutionary models, including ours, are hypotheses, and it is impossible to know the true historical trajectory of mutations. Nonetheless, we can say that the evolutionary conclusions we present in this work were derived from the most advanced approach that is currently possible, and with the strongest statistical support and congruence across multiple independent methods. This work highlights the advances we have seen in bioinformatic methods while also providing a roadmap for the many important questions that remain.

Most notably, our phylogeny revealed the novel class Ø clade, which is exclusively (among characterized organisms) associated with marine microbes, such as cyanophages and photosynthetic bacteria. Bioinformatically, we showed that the class Ø α subunit shares significant sequence homology with class II sequences, while also containing in its gene neighborhood a gene for a ferritin-like protein that resembles the class I β subunit. Structurally, we further showed that the class Ø α subunit defines the minimum set of features shared by class I and II RNRs, including the orientation of the two monomers in the dimer, specificity site at loop 1, and the β-hairpin. It was previously hypothesized that the LCA of RNRs may have possessed class II-like biochemistry based on the Precambrian bioavailabilities of iron and cobalt and the abiotic synthesis of porphyrin rings (Lundin et al., 2015). An alternative hypothesis is supported by our phylogeny, which places the class I mechanism as ancestral to, or having diverged in parallel with, that of class II. Although we cannot classify the mechanism of radical generation in the class Ø RNRs in the absence of biochemical data, our phylogeny suggests that a class II-like enzyme resembling the extant class Ø sequences recruited a ferritin-like protein into the catalytic machinery, before diverging into class I RNRs (which retained a ferritin subunit for catalysis) and class II RNRs (which specialized in cobalamin usage). The usage of a ferritin-like protein in class Ø RNRs is supported by the observation of a stacked YY dyad in the active site of our cryo-EM structure, which suggests that long-range radical transfer is an important component of catalysis in these enzymes. However, the short C-terminus of the class Ø ferritins and the prevalence of the CXC motif in the α C-terminus suggest that there are key differences with the class I and II RNRs. Future biochemical characterization will require identification of the correct metallocofactors for activity and possible reducing partners.

Based on our results, we present the following model for the evolution of the RNR family. The LCA of the RNR catalytic subunit likely shared the most biochemical resemblance to extant class III RNRs. Increasing oxygen availability selected for the divergence of the RNR family into the anaerobic LCA of class III and the oxygen-tolerant LCA of classes Ø–II, from which the minimal class Ø RNRs diverged. Class I and II RNRs sharing an ancestor would suggest that multiple strategies to adapt to the presence of oxygen evolved in parallel, rather than sequentially. Interestingly, we find sequences from the earliest diverging cyanobacterium, Gloeobacter, near the root of the class II clade in our phylogeny. Gloeobacter are notable for lacking thylakoid membranes and have been called ‘the missing link’ between anoxic photosynthesis, which involves Fe-S chemistry, and oxygenic photosynthesis (Nakamura et al., 2003; Saw et al., 2013). Thus, along with the emergence of cyanobacteria (Schirrmeister et al., 2013), it is possible that class II RNRs evolved well before the Great Oxygenation Event (GOE). If so, this would imply that class I RNRs also emerged before the Earth’s atmosphere became permanently oxidizing. In fact, recent work suggests that oxygen availability was sufficient for the birth of O₂-producing and O₂-utilizing enzymes to occur well before the geochemically defined GOE (Jabłońska and Tawfik, 2021). Such an evolutionary model could explain how O₂-dependent class I RNRs can share an ancestor with class II RNRs. In this respect, it is especially intriguing that class Ø RNRs are associated with cyanophages and photosynthetic bacteria.

The cryo-EM map has been deposited in the Electron Microscopy Data Bank under accession code EMD-26712, and the model has been deposited in the Protein Data Bank under accession code 7urg. The phylogeny shown in Figure 2 is available at (https://itol.embl.de/shared/yFvz6aVgum9z). The structure-guided sequence alignment and all twenty inferred phylogenies are available for download as supplementary materials.

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Author details

1. Audrey A Burnim

   Department of Chemistry and Chemical Biology, Cornell University, Ithaca, United States

   Contribution

   Performed bioinformatic analyses, Purified and expressed protein, Performed SAXS analysis, Analyzed results, Curated all project results, Wrote the paper

   Contributed equally with

   Matthew A Spence and Da Xu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9962-1397

2. Matthew A Spence

   Research School of Chemistry, Australian National University, Canberra, Australia

   Contribution

   Validation, Devised bioinformatic workflow, Performed computational experiments, Performed data analysis, Assisted manuscript preparation, Edited the manuscript

   Contributed equally with

   Audrey A Burnim and Da Xu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2284-2498

3. Da Xu

   Department of Chemistry and Chemical Biology, Cornell University, Ithaca, United States

   Contribution

   Performed AlphaFold predictions

   Contributed equally with

   Audrey A Burnim and Matthew A Spence

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3879-039X

4. Colin J Jackson

   1. Research School of Chemistry, Australian National University, Canberra, Australia
   2. Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, Australian National University, Canberra, Australia
   3. Australian Research Council Centre of Excellence in Synthetic Biology, Australian National University, Canberra, Australia

   Contribution

   Acquired funding, Conceived of the project, Designed and supervised research, Analyzed data, Edited the manuscript

   For correspondence

   colin.jackson@anu.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6150-3822

5. Nozomi Ando

   Department of Chemistry and Chemical Biology, Cornell University, Ithaca, United States

   Contribution

   Oversaw the project, Acquired funding, Conceived of the project, Designed and supervised research, Analyzed data, Wrote the paper

   For correspondence

   nozomi.ando@cornell.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7062-1644

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