A highly limited amino acid library from asteroid Bennu yields wide-ranging protein folds

Domain: biology
Published: 2026-04-03
Editorial outcome: minor_revision

Abstract
Protein design software is used to explore the world of ancient protein structures, and very small, primordial amino acid libraries are found to produce a wide variety of key folds. Data from asteroid analyses and protobiotic chemistry experiments are utilised to constrain several primitive amino acid libraries of varying sizes. Protein design software is then used to construct sequences of these amino acids that could emulate a broad range of key protein folds including metabolic (all enzymes of the reverse tricarboxylic acid cycle), redox, electron-transfer and ribosomal, among others. AlphaFold2 is employed to predict 3D structures from these sequences, which are compared to their native counterparts using the Template Modelling Score. A library of only 6 amino acids—those of highest measured abundance on asteroid Bennu—can reproduce all folds in the test set. Two libraries of just 7 amino acids, constrained by the Miller–Urey experiment and Murchison meteorite data, and one library with 8 amino acids constrained by another Miller–Urey experiment, are also able to produce all folds considered. It is also demonstrated that a 6 amino acid alphabet—the 5 most abundant on Bennu supplemented by cysteine that could have been supplied by atmospheric haze chemistry—can yield a ferredoxin-like protein with a plausible Fe–S binding geometry. Such broad protein folding with very limited amino acid libraries has significant implications for the origins of life, synthetic biology and medical applications.

Introduction
Despite decades of intensive research, the origins of life remains one of the deepest, most stubborn and enduring mysteries in science. Approaches to this question can be approximately divided into bottom-up and top-down. In the former, attempts are made to recapitulate origins processes in experiments or simulations, perhaps by re-creating early Earth environments and testing for the formation of biomolecules [4], or by creating abstract models of non-equilibrium complex systems [19]. Top-down approaches aim to de-construct the integrated, modular architectures of known life, to reduce and simplify its components and organisation, and thus travel back in evolutionary time [16]. Embarking on this journey takes one through eons of evolutionary transitions and global cataclysms, back to a time before there was atmospheric oxygen, then to before the age of Eukaryotes, coming eventually to a world of simple, single cells.

We cannot derive every detail of the organisms that inhabited the Earth at this primordial stage, approximately 4 billion years ago when the planet had barely stabilised from the late heavy bombardment. However, frontier methods in bioinformatics can give incredible insights into the so-called last universal common ancestor (LUCA) [26] (note that opinions vary greatly as to whether there was one LUCA, or a set of LUCAs, or something even more complex [40]). Since at that time the evolutionary machinery itself was in the process of emerging from a range of potential candidates (alternative genetic coding systems, ribosomal structures, metabolic systems, etc.), origins researchers can garner constraints and guidance by exploring various possibility spaces that were key to life’s emergence. One such possibility space is the world of proteins and the folded structures that endow them with their astounding functions. While there is ongoing, intensive debate regarding the sequence with which life’s functions arose (or indeed whether thinking of life’s emergence as a chronological, serial sequence of function discoveries makes philosophical sense [5]), there is widespread agreement that primitive proteins played a key, early role. This is because native minerals and putative inorganic ligands have, in general, limited catalytic power on their own. Peptides on the other hand, even rather small ones, can have significant catalytic potency [7], and more broadly, enzymes are the engines that power life as we know it [11, 34, 43].

In this work, we set out to chart the world of early protein folds using artificial intelligence (AI) tools, in particular, a set of enzymes such as one might need to drive a primitive metabolic cycle as well as other key examples including the TIM Barrel, Ferredoxin, Thioredoxin, ribosomal proteins and Ribonuclease H. Much work in the origins field has suggested that the so-called ‘reverse tricarboxylic acid’ (rTCA) cycle, or reverse Krebs cycle, could have served as the earliest metabolic system [20, 35]. This is due to the fact that the precursor inputs were readily available in early Earth environments, it is biologically ubiquitous and universal, and certain inorganic substances appear able to drive some of its reaction steps [27].

Given this hypothesis of the rTCA being the first metabolic system, we can endeavour to find the minimal set of protein folds required for this cycle to function. Enzyme function depends upon various physical chemical properties, and in this work we focus specifically on folds, perhaps the most defining feature of proteins. We targeted an intermediate, progenote stage of life’s emergence. Approximately speaking this would be after the true origin of life, but before all of the known functions of the LUCA were in place. It is assumed that at this stage peptides and small proteins were being synthesised, and a resolution of ‘the hydrolysis problem’ had emerged. We also assume that the symmetry-breaking event(s) which led to the fundamental property of homochirality—ubiquitous in life as we know it—had already come to pass [9, 17, 29, 30]. We thus only consider left-handed or L amino acids (AAs) in this work, though there is increasing interest and caution regarding the so-called ‘Mirror world’, a stereo-inverted biochemical system of only D (right-handed) AAs, that should also be fully viable [2].

Given the abundance of AAs in meteorites, asteroids and protobiotic [41] chemistry experiments, it is clear that subsets of simple AAs would have been available ‘on tap’ in various early Earth scenarios [15, 31]. What is not clear however, is the point at which the larger, more complex, challenging to synthesise AAs were incorporated into biochemistry. These are generally considered to be the ‘late 10’ AAs: cysteine, phenylalanine, histidine, lysine, methionine, asparagine, glutamine, arginine, tryptophan, tyrosine. Note however, that the membership of this list depends upon the availability of key precursors or elements. For example, phenylalanine and methionine can sometimes be detected in trace amounts in protobiotic chemistry experiments [18, 31]. In addition, new studies suggest that cysteine may have been supplied to the surface of the Archaen Earth through atmospheric haze chemistry [32].

Recent experimental work has shown that the canonical 20 AAs are highly favourable compared to other subsets of non-canonical AAs, in terms of their ability to form folded structures, which are key to protein function [23, 24]. Given this and other compelling evidence for canonical AAs outcompeting non-canonical AAs in terms of foldability, we can ask whether reduced libraries of canonical AAs—such as would be available in protobiotic environments—might also be able to form essential protein folds.

Several previous works have considered the feasibility of reduced AA libraries for the formation of functional proteins. For example, Walter et al. [39] engineered an active chorismate mutase using an AA library of size 9, demonstrating that substantially reduced alphabets can still support both fold and function. Timm et al. [37] designed a 13-residue peptide that coordinates a di-Ni cluster and catalyses hydrogen evolution, showing that simple metal-binding peptides can perform proto-hydrogenase chemistry.

In Gibney et al. [14], very short (16 residues) peptides were created from a library of just 8 AAs that can coordinate an Fe-S cluster and the authors also built a small four-helix bundle that binds both Fe–S clusters and hemes. This shows that simple, minimal sequences can organise multiple redox cofactors and carry out basic electron-transfer roles. Riddle et al. [33] showed that a small β-sheet protein (the SH domain) can be built largely from a 5 AA library, whereas a 3-letter set failed. Despite drastic sequence simplification, the engineered domains folded rapidly and remained functional, suggesting that complex protein architecture doesn’t require the full modern alphabet.

Tretyachenko et al. [38] constructed large sets of random AA sequences (~ 105 residues long) using either the full 20 canonical AAs or a 10 AA ‘prebiotic’ subset, and found that compact, protease-resistant structures can form from both libraries. It was found that the early-alphabet proteins were intrinsically more soluble and refoldable. They also showed that modern-alphabet proteins rely more on chaperones for solubility, while early-alphabet proteins may achieve structure via salts, metal ions, or oligomerisation—suggesting different routes to foldability early in life’s history.

Taking similar inspiration to our own, Solis [36] explored whether primitive AA libraries could have formed the basis of a metabolic system early in life’s history. They considered whether a primitive 10 AA library can encode modern-like protein structures. Surveying thousands of proteins with an information-theoretic metric, it was found that the primitive set preserved fold information especially well for mixed α/β classes, implying that early polypeptides could fold before the full 20 AA alphabet emerged.

Murphy et al. [28] showed that reducing the AA library to 10–12 residues preserves most sequence information needed to produce correct folds for many protein families. However, they observed a sharp drop in matching for smaller libraries, implying a practical lower bound of 10–12 letters for capturing diverse folds via sequence patterns. Longo et al. [22] built ‘primitive’ β-trefoil proteins using reduced alphabets of size 12 or 13, strongly biased towards protobiotic AAs (74–79%), and showed they still fold and crystalise while preserving the β-trefoil architecture. Their most simplified variant folded only at high salt, suggesting primitive libraries can support foldable proteins in saline environments.

Compared to prior studies, our work targets library sizes at the lower end of what has previously been considered necessary or possible for broad structural matching, and we aimed to predict specific backbone structures over a wide range of protein families.

In order to design protein structures from primitive AA libraries, we use four main steps: 1) Library constraining using measurement data, 2) Sequence prediction using ProteinMPNN, 3) Structure prediction using Alphafold2, and 4) Structure comparison using the TM-Score. These steps are described in detail in Methods and shown in schematic form in Figure 1.

Results
Our primordial protein modelling pipeline produces candidate structures for a range of characteristic folds, over 4 different AA libraries which are varied in size from a minimum of 4 residues up to a corresponding maximum (see Methods). These candidates are directly compared to their native counterparts and the resulting scores as a function of library size are shown in Figure 2 and Figure 3.

We see that almost no structural fold matching is achieved for libraries of size 4, though note the exception of Succinyl CoA Synthase, within which the enzyme fold with PDB code 1OI7 can be formed using only G, A, P and V (it was not possible to form this fold with a library size of < 4 AAs). Beyond these very small libraries, many folds can be formed with 5 AAs when using meteorite-derived constraints. Our main result is that the 6 AAs quantitatively observed in asteroid Bennu (Glycine (G), Alanine (A), Proline (P), Valine (V), Leucine (L) and Glutamic Acid (E)) are sufficient to produce all folds considered (Figure 2a), which is an extraordinary diversity of structures for such a small AA library.

The Murchison (CM2) library (Figure 2b) was able to reproduce all considered folds with only 7 AAs: Alanine (A), Glutamic acid (E), Glycine (G), Proline (P), Serine (S), Threonine (T) and Valine (V).

The volcanic Miller-Urey library of size 7 appears sufficient to reproduce all folds tested Figure 3a. This library includes Glycine (G), Alanine (A), Serine (S), Aspartic acid (D), Valine (V), Glutamic acid (E) and Phenylalanine (F). The last member, Phenylalanine, is rarely considered an ancient AA, and indeed it appears at a low concentration in the results of Johnson et al. [18].

At a library size of 8, all folds could be formed by the H2S Miller-Urey set [31] (Figure 3b). This lends strong support to the hypothesis that primordial, abiotic sources of AAs could enable the emergence of an expansive range of protein folds.

Note that while a TM-score of greater than 0.5 is conventionally used to imply a strong structural match, we could also raise our threshold to TM-scores of at least 0.75 and the conclusions described above would be unaffected.

While this suggests a reduced upper bound on the required AA set required for a broad range of key protein folds, it does not rule out the possibility of complete fold matching with even smaller libraries. The complete space of protein sequences for even small AA alphabets is astronomically large (specifically, it is PN, where P is the library size and N is the number of residues in the protein), e.g., for 6 AAs and a peptide of length 100, there are ~6.5×1077 members of the sequence space. Given that we cannot possibly sample the sequence space of even small proteins, it is testament to the incredible power of ProteinMPNN that we could uncover such well matched folds with relatively small numbers of sequences (namely, 10 sequences for each specific protein structure tested). With deeper exploration of sequence space and tuning of the AI models, we may be able to reduce the upper bound of 6 AAs even further. It may also be possible to discover structural matching with sequences of different lengths to the original, native sequences [10].

Figure 4 shows two example structures for comparison. In the upper panel we see a Succinyl CoA Synthase protein (PDB code 1OI7), and in the lower panel a Malate Dehydrogenase with PDB code 7CGD. The TM-score for the upper panel structures is 0.94, and 0.9 for the lower panel.

It is natural to question how a primitive six-member library such as the ‘Bennu’ set (G, A, P, V, L, E)—mostly neutral or hydrophobic, with a single acidic residue (E), and lacking basic and aromatic residues—could support binding or catalysis. In ferredoxins, Fe–S clusters are generally ligated by cysteines. Thus, without a non-canonical substitute, inclusion of cysteine at specific positions would likely be required to bind and stabilize the Fe–S cofactor and enable redox function. While cysteine was long thought to be absent or scarce in early Earth scenarios, recent work suggests that it could have been supplied from the atmosphere via organic haze chemistry [32].

As such, we examined whether a modified Bennu six-member library, with cysteine substituted for Glutamic acid (G, A, P, V, L, C), could re-capitulate Ferredoxin structures. An example structure (PDB code 1RFK) generated using just these 6 residues is shown in Figure 5. The TM-score of the structural alignment between designed and native was 0.91. Both the native and designed sequences have coordinating cysteines at positions 38, 43, 46, and 76 (or 41, 46, 49 and 81 factoring in a 3-residue frameshift due to Xs in the native sequence), and the local geometry of plausible Fe-S cluster placements suggest that the primitive version may indeed be capable of binding the Fe-S cluster. Future work will use molecular-dynamics simulations to rigorously evaluate the energetic stability of Fe–S cluster binding across this design and the broader set of related architectures.

Methods
Predicting 3D protein structure just from an AA sequence was a computationally formidable task until recent years, when advanced AI algorithms effectively ‘solved’ this problem through training on large databases of known examples. Models such as Alphafold 2 and 3, and ESMFold can compute accurate protein structures in minutes, with associated confidence scores [1, 21, 25]. This opens up an invaluable opportunity for origins researchers and the exploratory reconstruction of primitive proteins.

In the present work, we used the sequence design model, ProteinMPNN to infer possible sequences given various target folds [8], and several reduced AA libraries from which the proteins could be built. Specifically, we used representative AA subsets constrained by the Miller-Urey experiment [3, 18, 31] and meteorites, namely Murchison and the recently studied Bennu asteroid [15]. We started from a minimal set of 4 AAs in all cases and re-introduced members of the library in order of measured abundance.

We considered all the enzymes present within the rTCA cycle: 2-Oxoglutarate Synthase, Aconitase, Pyruvate Synthase, Citrate Synthase, Fumarase, Fumarate Reductase, Isocitrate Dehydrogenase, Malate Dehydrogenase, and Succinyl-CoA Synthase. Also included in our test set were Ferredoxin, Thioredoxin, the TIM Barrel fold, ribosomal proteins, and Ribonuclease H. We used ProteinMPNN [8] to design sequences using targets from at least two specific entries in the Protein Data Bank for each of these protein classes. This design process takes as a constraint the relevant reduced AA library under evaluation, candidate sequence generation with ProteinMPNN, and modelling of the resulting protein structures using Alphafold2. Finally, we validated the structural similarity between native structures and the modelled, primitive analogues using the ‘TM-Score’ [42, 44].

Note that recent experimental work has confirmed the efficacy of Alphafold2’s structure predictions for sequences derived from primitive AA libraries [13]. This lends strong empirical credence to the predictions of our modelling pipeline.

Library Constraining
In the following we use standard single-letter AA codes: Alanine (A), cysteine (C), aspartic acid (D), glutamic acid (E), phenylalanine (F), glycine (G), isoleucine (I), leucine (L), methionine (M), proline (P), serine (S), threonine (T), valine (V).

There are many (in fact an astronomically large number) of possible AA libraries that early life may have used. However, it is reasonable to assume that many of them can be ruled out due to kinetic and thermodynamic limits to abiotic synthesis. Certain AAs, such as A and G, are found in many protobiotic environments and are synthesised in various astronomical environments, such as interstellar clouds [6]. If we focus upon plausible early Earth conditions, we can use protobiotic chemistry and meteorite analyses as guidance to the specific AAs that might have been provided to early life abiotically. In the present work we used four AA libraries, two derived from meteorite measurements, and two from Miller-Urey protobiotic chemistry experiments.

Our first library was constrained by recent, high precision measurements of samples from the asteroid Bennu, collected by the OSIRIS-REx mission. We used measured abundances from Extended Data Table 2 of Glavin et al. [15] to construct an abundance-ranked list of AAs: G, A, P, V, L and E. The smallest library of 4 consisted of A, G, P and V, and the largest consisted of 6 AAs: A, E, G, L, P, and V.

Our second library was derived from analyses of the well-studied Murchison meteorite. We used the measured abundances from Extended Data Table 2 of Glavin et al. [15] to construct an abundance-ranked list of AAs: G, A, P, V, T, E, S, L, I. The smallest library of 4 consisted of A, G, P and V (identical to Bennu’s smallest library), and the largest consisted of 9 AAs: A, E, G, I, L, P, S, T and V.

Our third library made use of an analysis of Stanley Miller’s 1953–54 University of Chicago protobiotic chemistry experiments [18]. This apparatus “simulates the spark discharge synthesis by lightning in a steam-rich volcanic eruption”. We ranked the detected canonical AAs in order of abundance: G, A, S, D, V, E, F. We then used progressively larger libraries starting with 4 (A, D, G, and S), and adding one at a time until reaching all 7 detected AAs (A, D, E, F, G, S and V).

Similarly, we ranked the AAs detected from the Miller-Urey experiments described in Parker et al. [31], the so-called ‘1958 Miller H2S-rich spark discharge experiment’, in order of abundance: A, G, V, S, T, I, E, L, D, M. As above, we started with a minimal library of 4 (A, G, S and V), incrementing by one until reaching a maximum library of 10: A, D, E, G, I, L, M, S, T and V.

Table 1 shows the four AA libraries used to generate our results. These libraries are derived by listing the AAs observed in the respective experiments in rank order of abundance, and choosing a size (e.g., the 7 most abundant AAs measured in the sample).

Sequence Design
With a given AA library in hand, the next step in our pipeline was to design sequences given a target structure. All target structures were sourced from the Protein Data Bank, and we used ProteinMPNN to design the sequences [8]. ProteinMPNN is freely available on Github, and the code is available as a Colab Notebook. In the present work, we simply entered the target structure using the relevant PDB code, constrained the model to only design chain “A”, left the sampling temperature at the default value of 0.1, and used the “omit AAs” flag to enter the AAs that the algorithm should ignore due to the reduced AA library.

For each class of proteins, e.g., Malate Dehydrogenase, we picked at least two specific entries from the PDB, and made 10 separate sequence predictions for each PDB entry. The specific PDB target structures are given in Table 2.

Structure prediction
With a given AA sequence in hand, the next step in our pipeline was to predict the protein structure arising from such a sequence. We used the popular Alphafold2 algorithm, freely available as a Colab notebook. We did not modify any of the parameters or options away from their default values in the code, hence this was simply a matter of entering the AA sequence, running the algorithm, and downloading the PDB output files corresponding to the folded structure.

Structure Comparison
With a given primitive AA structure in hand in the form of a PDB file, the final stage in our pipeline was to compare this analogue structure to the original target structure. For this purpose, we use the commonly employed Template Modeling score (TM-Score) [42, 44]. The TM-score has an online app as well as a C++ code that rapidly assigns a matching score between two protein structures. In the present work, we utilised the ’TM-Align’ C++ code to compute TM-Score values. These scores were used for measuring structural matching throughout our analysis.

In order to cross-validate our results, we also computed structural comparisons using the Root mean square deviation (RMSD), and found very similar results to those obtained using the TM-Score, as presented in Supplementary Figures 1 and 2. We also compared the RMSD and TM-Score to the Protein-MPNN Fit Score, Alphafold2 pTM and Maximum Substructure score for specific protein examples. As shown in Supplementary Tables 1 and 2, and discussed in the Supplementary Information, the TM-Score and RMSD were found to be significantly more robust and reliable as structural comparators than the other three metrics listed above, as supported by the former’s widespread use in the field of protein engineering.

In order to control for potential biases or inaccuracies due to Alphafold2’s specific features, including its use of multiple sequence alignments (MSAs), we re-computed structures for all folds considered using the ESMFold model [21]. We found very similar results to those obtained with Alphafold2, as described in the Supplementary Information and presented in Supplementary Figures 3 and 4.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper addresses the hypothesis that early metabolism (specifically, the reverse Krebs (rTCA) cycle) could have been supported by proteins constructed from a dramatically reduced amino acid alphabet. Using structure design (ProteinMPNN) and prediction (AlphaFold2), the authors show that a library of just six amino acids, constrained by meteorite and prebiotic chemistry data, suffices to recapitulate all major folds associated with the rTCA enzymes, and possibly others like Ferredoxin and Thioredoxin. Libraries of 7–8 amino acids, especially those informed by Miller-Urey experiments, perform similarly or better. This is an exciting idea that connects origins-of-life chemistry to modern AI tools. That said, while the conceptual direction is strong, the paper as written feels more like an enthusiastic blog post than a finished research manuscript. Many aspects of the work are underdeveloped, or the presentation is of uneven quality. It’d help to sharpen the technical framing, trim unnecessary methodological digressions, and focus more deeply on the most compelling molecular reconstructions.\n\nTechnical Evaluation: Much of the paper’s energy is spent on cataloging TM-scores, pTM, RMSD, MaxSub, etc., but that obscures rather than clarifies. Rather than showing the min–mean–max range of potential generations, why not focus on the very best entities and dive into them more deeply?\n1. There’s a missed opportunity in terms of going further with the computational analysis—for example, identifying whether cofactor binding pockets are preserved, whether electrostatic features near active sites are plausible, or if known PPIs between the entities are preserved in computational models. All of these would strengthen the case.\n2. TM-score > 0.5 is insufficient. One of the oddities of AF2 and other modeling tools is that, because they’ve seen these PDB structures during training, they’re biased to predict a similar structure if something looks like even a semi-plausible fit (e.g., see this tweet thread: https://x.com/sokrypton/status/1812769477228200086). This suggests that a bit more skepticism is warranted, and potentially higher thresholds.\n3. Relatedly, it’s unclear to me if the authors explored many ProteinMPNN candidates. If not, they should. 5 is too little-- if you're doing in silico explorations, why not go extensive and deep? Try different length settings as well (e.g., DOI: 10.1101/2024.08.13.607858)\n4. A discussion of chirality is entirely absent. The prebiotic chemistry literature is deeply concerned with handedness, and the authors should at least acknowledge the role of stereochemistry in foldability and binding. One option might be to take the most plausible structures and do a molecular dynamics simulation with left- and right-handed amino acids and see if the structure is preserved.\n\nPresentation: The tone of the manuscript is casual and occasionally conversational, which I enjoyed, but the writing could benefit from a careful editing pass. There are multiple moments where typos (e.g., “addtion” in the abstract), loose phrasing, or inconsistent terminology (“prebiotic” vs. “protobiotic?”) undermine the clarity of an otherwise good point.\nI would also strongly suggest the authors look to some collaborations with molecular biologists to see if some of these candidates fold and perform the functions needed. You can order the relevant proteins online and do at least some basic tests. That might be beyond the scope of this work, but it would really elevate the impact.", "reviewer_2": "Bartlett and colleagues present analyses that demonstrate that reduced amino acid (AAs) libraries containing a subset of the “ancient” canonical AAs can be used to reconstruct the folds of the enzymes of the reverse tricarboxylic acid (rTCA) cycle. They employ fixed-backbone sequence design computational methods to predict amino acid sequences that are likely to fold and adopt the backbone structures of the enzymes of the rTCA cycle, but only using AAs from four primordial libraries. These four libraries include two nominates from analyses of an asteroid and meteorite, and two libraries nominated from Miller-Urey experiments on early life. Following fixed-backbone sequence design with ProteinMPNN, the authors used AlphaFold to predict structures for the designed sequences, and compared the structural similarity of the predicted structures to the native backbones using a series of structural similarity metrics (i.e., RMSD and TM-score).\n\nI found this to be a fascinating work in its exploration of early life scenarios, as determined by constrained AA libraries and a focus on the rTCA cycle, and in its clever use of fixed-backbone sequence design AI methods to address an interesting question about the origins of life. However, outstanding questions remain, particularly with respect to the sequence homology/novelty of the designed sequences, the structural match of predicted structures relative to the broader set of known natural protein structures, and extension of the approach/analyses to other early proteins besides the rTCA enzymes, to see whether folds from other classes of early proteins can also be recapitulated by these constrained AA libraries. While experimental validation would greatly strengthen the manuscript (please see my final comment), to me it is not strictly necessary for manuscript acceptance, provided the rest of my comments are addressed adequately and any new results are incorporated accordingly, as I found the application of fixed-backbone sequence design to analysis of early life scenarios to be very interesting.\n\nComments:\n1. The authors focus on structures/folds of the enzymes of the rTCA cycle, given hypotheses about it being the first metabolic system. I believe the conclusions would be greatly strengthened by extending the approach and analyses to other early proteins – do the same reduced sets of AAs suffice to recapitulate folds from other early proteins, e.g., early ribosomal proteins? If not, to what degree does the AA library need to be expanded to recaptiulate other sets of folds? Such an analysis, echoing out in layers of evolutionary time, would be extremely interesting, and provide a “thresholding” or “saturation” type analysis in terms of investigating how far these reduced sets of AAs extend. Could the authors please show analysis on other sets of early metabolic proteins or other early proteins? This would help support some of the central claims of the manuscript, such as “that primordial abiotic sources of AAs could enable the emergence of metabolic protein folds”.\n2. The authors main pipeline is: structures for enzymes of the rTCA, to fixed-backbone sequence design with ProteinMPNN, to structure prediction with AlphaFold, to structure comparison relative to the original structure using various metrics. However the authors focus exclusively on structure comparisons. There are no sequence level comparisons between the ProteinMPNN-designed sequences and the native sequences corresponding to the PDB structures. This is absolutely necessary. For example, for each enzyme and each set of designs, what is the sequence homology between the designed sequences and the originals? What fraction of residues in the natural sequences is composed of the AA members of each of the reduced-set AA libraries? Are established/known motifs recapitulated in the designed sequences, for example via a k-mer analysis? What is the distance (ie Hamming or Levenshtein distance) between the designed sequences and the closest sequence level match? Et cetera. Such analyses are critical to provide a more complete picture of the residue-level composition of the rTCA sequences, comparing the natural proteins to the designs over the reduced-AA libraries. It could be that these rTCA sequences are already greatly enriched for ancient AAs, and such analyses would help expose this.\n3. Continuing off (2), it could also be possible to extend such sequence-level analyses across phylogenetic trees, looking at the amino acid sequences for rTCA enzymes from various species, from earlier to later in the phylogenetic tree. We would hypothesize to see greater sequence homology earlier in the phylogenetic tree, if the hypothesis that there is greater AA diversification beyond the ancient AAs in later evolutionary times holds.\n4. Similarly, continuing off (2), the authors should perform BLAST searches of the designed sequences to determined the closest natural protein match, and report the distance to the closest natural protein match for their designs.\n5. How high-confidence are the predicted structures for the designs? Please report pLDDT metrics for the predicted structures. Apologies if this was missed. Additionally, please show structure predictions, along with pLDDT, TM-score, and sequence homology (see points above), for all rTCA enzymes in the Supplemental information.\n6. How common are these folds over the greater space of natural proteins? A Foldseek type analysis over AlphaFoldDB would help contextualize the redundancy of these folds across the space of natural proteins.\n7. For the current Figures 1 and 2, since the rank order in each AA constraints library is different, please indicate which AAs are present in each increment, rather than just “No. Of Amino Acids in Library”.\n8. I suggest to add an overview schematic Figure to the manuscript – this would greatly help contextualize the approach and goals of the work overall.\n9. The authors sometimes use “AI protein models” loosely – please be more specific.\n10. Parts of the Introduction are lacking citations; for example, “Predicting protein structure... Models such as Alphafold...”. This is just one example – please go back through the Introduction and add citations.\n11. The possibility of experimental functional validation, with the inclusion of small amounts of positively charged AAs, is highly enticing. While I recognize that the authors are leaving this to future work, they write “we are in the process of producing and experimentally validating our best-matching sequences”. Any degree of experimental validation – from structural characterization to enzymatic activity characterization of variants with positively charged AAs – would immensely strengthen the manuscript. If the authors have progressed in their experiments, please include these in the Results. Otherwise, I would absolutely love for *some* degree of experimental validation to be included, even if it is showing that some of the designed sequences can be expressed (i.e., via a cell-free system) and adopt the expected secondary/tertiary structures following in vitro expression."}, "author_response": "Response to Reviewers for NCOMMS-25-40849A\nStuart Bartlett, Arushi Gupta, Dominic Phung, Josh Goldford, Jiani Yang, Klara\nHlouchova, Woodward Fischer and Yuk L. Yung\nWe thank both reviewers for their insightful feedback. Additional text is shown in blue in\nthe revised version, and discarded text has been struck through.\nWe have revised the paper according to the feedback given (see below for individual\npoints). Many extensions of our work were suggested and we wholeheartedly agree with\nthe importance and merit of all these additional studies. At the same time, we would like\nto caution that completing all such studies would require significant time and other\nresources, so while we agree that such analyses should be carried out, our strong desire\nwith the current paper is simply to disseminate a basic result: that many protein folds\ncan be reproduced with highly limited libraries, especially that of asteroid Bennu given\nhow recently its organic repertoire has been revealed. Right now there is a great deal of\nexcitement about Bennu, given the incredible ongoing discoveries concerning its\nbiologically-relevant precursor molecules. Similarly, the field of AI-driven molecular\nbiology is making waves as new generations of both structure and function prediction\nsoftware are released. Thus, we feel that our results are very timely and could spark\ninspiration and new research endeavours for many groups across the world.\nIt is for the above reasons that we would prefer to defer several of the suggested\nextensions to future work, such that our current results can be published sooner rather\nthan later to allow a wide range of experts to build upon our findings.\nReviewer #1 (Remarks to the Author):\nThis paper addresses the hypothesis that early metabolism (specifically, the reverse Krebs\n(rTCA) cycle) could have been supported by proteins constructed from a dramatically reduced\namino acid alphabet. Using structure design (ProteinMPNN) and prediction (AlphaFold2),\nthe authors show that a library of just six amino acids, constrained by meteorite and prebiotic\nchemistry data, suffices to recapitulate all major folds associated with the rTCA enzymes, and\npossibly others like Ferredoxin and Thioredoxin. Libraries of 7–8 amino acids, especially those\ninformed by Miller-Urey experiments, perform similarly or better. This is an exciting idea that\nconnects origins-of-life chemistry to modern AI tools. That said, while the conceptual direction is\nstrong, the paper as written feels more like an enthusiastic blog post than a finished research\nmanuscript. Many aspects of the work are underdeveloped, or the presentation is of uneven\nquality. It’d help to sharpen the technical framing, trim unnecessary methodological digressions,\nand focus more deeply on the most compelling molecular reconstructions.\nWe thank the reviewer for these comments.\nTechnical Evaluation: Much of the paper’s energy is spent on cataloging TM-scores, pTM,\nRMSD, MaxSub, etc., but that obscures rather than clarifies. Rather than showing the\nmin–mean–max range of potential generations, why not focus on the very best entities and dive\ninto them more deeply?\nWe thank the reviewer for this observation. We agree that the graphs of the distributions\nof score values could be a source of confusion rather than clarification so we have\nomitted that subsection.\nPlease note that the manuscript itself only focuses on the TM-score (the other metrics\nare in the Methods section). We felt that having a suite of metrics outside of the main text\nwould provide a broader analysis base to not only support the validity of the TM-score\nresults, but also inform those readers who might be more familiar with other metrics\n(e.g., RMSD).\nWe did try to highlight two of our best sequences in Fig. 3, and detailed scores presented\nin Tables 3 and 4. Further analysis of these and other high-scoring sequences, including\nbinding and function, are the logical next steps in this new area of study, and we feel they\nare better suited to dedicated future investigations and publications.\n1. There’s a missed opportunity in terms of going further with the computational analysis—for\nexample, identifying whether cofactor binding pockets are preserved, whether electrostatic\nfeatures near active sites are plausible, or if known PPIs between the entities are preserved in\ncomputational models. All of these would strengthen the case.\nWe completely agree that these suggestions would reveal important additional\ninformation about the properties of our protein structures. We feel that this and many\nother interesting extensions form a hierarchy of new studies, with the present work as\nthe starting point. While we (and hopefully others in the near future) are working on these\nmyriad extensions as we speak, our preference is for the present paper to have a concise\nfocus, namely the fact that very limited libraries can produce a diverse range of folds. We\nfeel that the various fascinating extensions that stem from the present work deserve their\nown dedicated publications, given the large number of results, figures, discussions, etc.\nthat will stem from each of these studies. We have made a list of all the interesting next\nsteps suggested by the reviewers and others in the Conclusions and Future Work\nsection.\n2. TM-score > 0.5 is insufficient. One of the oddities of AF2 and other modeling tools is that,\nbecause they’ve seen these PDB structures during training, they’re biased to predict a similar\nstructure if something looks like even a semi-plausible fit (e.g., see this tweet thread:\nhttps://x.com/sokrypton/status/1812769477228200086). This suggests that a bit more\nskepticism is warranted, and potentially higher thresholds.\nThis is an insightful observation, and we agree that caution concerning the interpretation\nof the score values is absolutely warranted. We also agree that the limitations of tools\nsuch as AF2 should be fervently attended to in any study such as ours.\nIn order to assess the influence of AF2’s multiple sequence alignment dependencies, we\nre-ran our analysis with ESMFold, since it is a single-sequence structure prediction\nmodel. The ESMFold results were essentially identical to those achieved with Alphafold2\napart from 3 protein families that did not achieve structural matching for the Miller-Urey\nvolcanic library, even at size 7. We have added a section discussing this additional\nsensitivity study.\nOur threshold of TM-score ≥ 0.5 was chosen purely on the basis of standard practice and\nthe guidance given by the creators of the TM-score (Xu & Zhang 2010, Zhang & Skolnick\n2004). We could, however, raise our threshold to 0.8 instead of 0.5, and even then our\nprimary conclusions would remain unchanged: we achieve complete structural matching\nwith library sizes of 6, 7, 7 and 8 for Bennu, Murchison, Miller-Urey volcanic and\nMiller-Urey H2S-rich, respectively. We have added a short section in the text to reflect\nthis result.\nNote that one of the reasons we included additional metric tests such as an RMSD≤3Å\n(shown in Figs. 6 and 7) is to verify that the TM-score results are representative and\nrobust. Indeed there was broad agreement across all metrics, as illustrated through the\nexamples in Tables 3 and 4.\n3. Relatedly, it’s unclear to me if the authors explored many ProteinMPNN candidates. If not,\nthey should. 5 is too little-- if you're doing in silico explorations, why not go extensive and deep?\nIndeed, this is a logical suggestion. Running ProteinMPNN is extremely fast and requires\nvery little compute, but Alphfold2 does come with a modest to significant (depending on\nsequence length) compute burden. If we wished to run, e.g., 1000s of sequences there\nwould be a considerable time burden.\nWith that being said, we accept the critique of 5 sequences per PDB code enzyme being a\nsmall sample size, so we doubled the number to 10 for both the AF2 and ESMFold\nanalyses. Having a larger sample size improved our results overall, so we appreciate this\nsuggestion.\nTry different length settings as well (e.g., DOI: 10.1101/2024.08.13.607858)\nWe appreciate this excellent suggestion. We prefer to defer this subproject to future\nwork, but have mentioned it with the relevant citation in the text.\n4. A discussion of chirality is entirely absent. The prebiotic chemistry literature is deeply\nconcerned with handedness, and the authors should at least acknowledge the role of\nstereochemistry in foldability and binding. One option might be to take the most plausible\nstructures and do a molecular dynamics simulation with left- and right-handed amino acids and\nsee if the structure is preserved.\nWe completely agree with the reviewer on the importance of chirality to the origins of life\nmystery. We have modified the text to briefly discuss this issue and how it relates to our\nwork, including the fact that the scenario envisioned in our work is one in which the\nhomochirality selection event/process has already occurred. Clearly a racemic mixture of\namino acids would not be able to form recognisable folds, therefore the phase of the\nemergence of life we are focussing upon is one proceeding the emergence of\nhomochirality, as explained by, e.g., Donna Blackmond’s work.\nGiven recent work on the ‘mirrorworld’, and the apparent identical physical and chemical\nproperties of chirally-inverted biochemical systems, it seems likely that inverting the\nchirality of all amino acids would result in identical structures at every level, apart from\nthe fact that the structures would have opposite handedness (the inversion of\nhandedness would propagate through all levels of the structure from amino acid\nupwards). We have added a brief discussion of this to the text.\nAs with other extensions, molecular dynamics simulations would incur a considerable\ntime and compute burden, and we would prefer to defer such studies to future work, to\npreserve a more concise message in this manuscript.\nPresentation: The tone of the manuscript is casual and occasionally conversational, which I\nenjoyed, but the writing could benefit from a careful editing pass. There are multiple moments\nwhere typos (e.g., “addtion” in the abstract), loose phrasing, or inconsistent terminology\n(“prebiotic” vs. “protobiotic?”) undermine the clarity of an otherwise good point.\nWe appreciate this observation. We have ensured that only protobiotic chemistry is used\nin the manuscript, and cited a recent paper that describes this terminology in detail. We\nhave also corrected all typos and removed inconsistencies.\nI would also strongly suggest the authors look to some collaborations with molecular biologists\nto see if some of these candidates fold and perform the functions needed. You can order the\nrelevant proteins online and do at least some basic tests. That might be beyond the scope of\nthis work, but it would really elevate the impact.\nWe completely agree with this suggestion, and in fact our collaborators just made a\npreliminary release of their experimental findings. They found that Alphafold2’s\npredictions for sequences derived from primitive AA libraries are indeed borne out by\nempirical structural analyses on real proteins. We have added some sentences\nemphasising the fact that we now have experimental support for the validity of our\nprotein design and structure prediction pipeline.\nReviewer #2 (Remarks to the Author):\nBartlett and colleagues present analyses that demonstrate that reduced amino acid (AAs)\nlibraries containing a subset of the “ancient” canonical AAs can be used to reconstruct the folds\nof the enzymes of the reverse tricarboxylic acid (rTCA) cycle. They employ fixed-backbone\nsequence design computational methods to predict amino acid sequences that are likely to fold\nand adopt the backbone structures of the enzymes of the rTCA cycle, but only using AAs from\nfour primordial libraries. These four libraries include two nominates from analyses of an asteroid\nand meteorite, and two libraries nominated from Miller-Urey experiments on early life. Following\nfixed-backbone sequence design with ProteinMPNN, the authors used AlphaFold to predict\nstructures for the designed sequences, and compared the structural similarity of the predicted\nstructures to the native backbones using a series of structural similarity metrics (i.e., RMSD and\nTM-score).\nI found this to be a fascinating work in its exploration of early life scenarios, as determined by\nconstrained AA libraries and a focus on the rTCA cycle, and in its clever use of fixed-backbone\nsequence design AI methods to address an interesting question about the origins of life.\nHowever, outstanding questions remain, particularly with respect to the sequence\nhomology/novelty of the designed sequences, the structural match of predicted structures\nrelative to the broader set of known natural protein structures, and extension of the\napproach/analyses to other early proteins besides the rTCA enzymes, to see whether folds from\nother classes of early proteins can also be recapitulated by these constrained AA libraries.\nWhile experimental validation would greatly strengthen the manuscript (please see my final\ncomment), to me it is not strictly necessary for manuscript acceptance, provided the rest of my\ncomments are addressed adequately and any new results are incorporated accordingly, as I\nfound the application of fixed-backbone sequence design to analysis of early life scenarios to be\nvery interesting.\nWe greatly appreciate these comments.\nComments:\n1. The authors focus on structures/folds of the enzymes of the rTCA cycle, given hypotheses\nabout it being the first metabolic system. I believe the conclusions would be greatly\nstrengthened by extending the approach and analyses to other early proteins – do the same\nreduced sets of AAs suffice to recapitulate folds from other early proteins, e.g., early ribosomal\nproteins? If not, to what degree does the AA library need to be expanded to recaptiulate other\nsets of folds? Such an analysis, echoing out in layers of evolutionary time, would be extremely\ninteresting, and provide a “thresholding” or “saturation” type analysis in terms of investigating\nhow far these reduced sets of AAs extend. Could the authors please show analysis on other\nsets of early metabolic proteins or other early proteins? This would help support some of the\ncentral claims of the manuscript, such as “that primordial abiotic sources of AAs could enable\nthe emergence of metabolic protein folds”.\nWe appreciate this insightful suggestion, and have extended our analysis to include the\nfollowing additional proteins:\n● TIM-Barrel: PDB 1TRE, 1B9B\n● Ribonuclease H: PDB 2RN2, 3O3G\n● Ribosomal proteins: PDB 1BXE, 1RSS\nThese additional folds were all successfully formed using small libraries and hence\ngreatly strengthen our general thesis, so we appreciate this suggestion.\nFuture work will endeavor to apply our method to very large sets of proteins (100s to\n1000s).\n2. The authors main pipeline is: structures for enzymes of the rTCA, to fixed-backbone\nsequence design with ProteinMPNN, to structure prediction with AlphaFold, to structure\ncomparison relative to the original structure using various metrics. However the authors focus\nexclusively on structure comparisons. There are no sequence level comparisons between the\nProteinMPNN-designed sequences and the native sequences corresponding to the PDB\nstructures. This is absolutely necessary. For example, for each enzyme and each set of designs,\nwhat is the sequence homology between the designed sequences and the originals? What\nfraction of residues in the natural sequences is composed of the AA members of each of the\nreduced-set AA libraries? Are established/known motifs recapitulated in the designed\nsequences, for example via a k-mer analysis? What is the distance (ie Hamming or Levenshtein\ndistance) between the designed sequences and the closest sequence level match? Et cetera.\nSuch analyses are critical to provide a more complete picture of the residue-level composition of\nthe rTCA sequences, comparing the natural proteins to the designs over the reduced-AA\nlibraries. It could be that these rTCA sequences are already greatly enriched for ancient AAs,\nand such analyses would help expose this.\nWe appreciate this highly insightful suggestion and agree that it would yield fascinating\ndeeper insights into our results. We conducted initial analyses in this direction and came\nto the conclusion that such a study is deserving of its own dedicated manuscript to be\nwritten in the near future. We feel that if we attempt to combine the results of such a\nstudy with those of the present manuscript, the message and overall thesis will become\nconvoluted, and the paper will also have to be significantly longer, given the much\nbroader range of discussions and conclusions required. However, we have added this\nand other interesting suggestions for extensions in the final section of the paper.\n3. Continuing off (2), it could also be possible to extend such sequence-level analyses across\nphylogenetic trees, looking at the amino acid sequences for rTCA enzymes from various\nspecies, from earlier to later in the phylogenetic tree. We would hypothesize to see greater\nsequence homology earlier in the phylogenetic tree, if the hypothesis that there is greater AA\ndiversification beyond the ancient AAs in later evolutionary times holds.\nThis is a wonderful suggestion, greatly appreciated. While we would be extremely keen to\ncomplete this additional study, we estimate that it would require significant time to\ncomplete and would warrant its own dedicated manuscript. For these reasons, we would\nprefer to defer this extension to future work, and have mentioned it in the final section of\nthe paper.\n4. Similarly, continuing off (2), the authors should perform BLAST searches of the designed\nsequences to determine the closest natural protein match, and report the distance to the closest\nnatural protein match for their designs.\nAs above, we greatly appreciate this suggestion and have added a comment regarding\nthe great promise that such a study holds for this new area. However, we prefer to defer\nthis extension to future work to ensure that the present manuscript is concise and\nfocussed.\n5. How high-confidence are the predicted structures for the designs? Please report pLDDT\nmetrics for the predicted structures. Apologies if this was missed. Additionally, please show\nstructure predictions, along with pLDDT, TM-score, and sequence homology (see points above),\nfor all rTCA enzymes in the Supplemental information.\nAll of these details will be available in the supplementary data files that will accompany\nthe published paper.\n6. How common are these folds over the greater space of natural proteins? A Foldseek type\nanalysis over AlphaFoldDB would help contextualize the redundancy of these folds across the\nspace of natural proteins.\nAs above, we very much appreciate this suggestion and have added a comment\nregarding the promise that such a study holds. However, we prefer to defer this to future\nwork to ensure that the present manuscript is concise and focussed.\n7. For the current Figures 1 and 2, since the rank order in each AA constraints library is\ndifferent, please indicate which AAs are present in each increment, rather than just “No. Of\nAmino Acids in Library”.\nWe have modified the figures according to this suggestion.\n8. I suggest to add an overview schematic Figure to the manuscript – this would greatly help\ncontextualize the approach and goals of the work overall.\nGreat suggestion, we have added this to the paper.\n9. The authors sometimes use “AI protein models” loosely – please be more specific.\nWe have updated the text to be more specific.\n10. Parts of the Introduction are lacking citations; for example, “Predicting protein structure...\nModels such as Alphafold...”. This is just one example – please go back through the Introduction\nand add citations.\nThe sentence introducing alphafold and ESMfold does have citations to the key papers\nfor both of those tools. We have also added a citation for Alphafold3.\n11. The possibility of experimental functional validation, with the inclusion of small amounts of\npositively charged AAs, is highly enticing. While I recognize that the authors are leaving this to\nfuture work, they write “we are in the process of producing and experimentally validating our\nbest-matching sequences”. Any degree of experimental validation – from structural\ncharacterization to enzymatic activity characterization of variants with positively charged AAs –\nwould immensely strengthen the manuscript. If the authors have progressed in their\nexperiments, please include these in the Results. Otherwise, I would absolutely love for *some*\ndegree of experimental validation to be included, even if it is showing that some of the designed\nsequences can be expressed (i.e., via a cell-free system) and adopt the expected\nsecondary/tertiary structures following in vitro expression.\nWe completely agree with this suggestion, and in fact our collaborators just made a\npreliminary release of their experimental findings. They found that Alphafold2’s\npredictions for sequences derived from primitive AA libraries are indeed borne out by\nempirical structural analyses on real proteins. We have added some sentences\nemphasising the fact that we now have experimental support for the validity of our\nprotein design and structure prediction pipeline.\nReviewer #2 (Remarks on code availability):\nsimple combination of existing colab notebooks. It is more likely that users wanting to perform\nsimilar analyses will combine the source colab notebooks/scripts on their own, rather than using\nthe authors' combination.\nYes, we completely understand this comment and actually considered not including our\ncode with the paper. The only reason for inclusion was the Nature guidelines but we will\nclarify with the editor which is the better option."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The manuscript is improved but remains primarily a proof-of-concept demonstration. The authors chose to engage substantively with only a few of the raised concerns, deferring much of the rest to future work.\n\nWhat was addressed: The ESMFold sensitivity analysis is a useful addition that directly addresses concerns about AlphaFold2's MSA-based biases. The results largely replicate across structure prediction methods, providing meaningful reassurance. The expanded protein set (adding TIM-barrel, ribosomal proteins, Ribonuclease H) and increased sequence sampling are also improvements.\n\nWhat was deferred: A good chunk of what was requested has essentially become a laundry list of future directions. The deeper structural analyses I suggested (cofactor binding pockets, electrostatics near active sites) are deferred. Similarly, the sequence-level analyses requested by Reviewer 2 (homology comparisons, k-mer analysis, BLAST searches) have been collected into a \"future work\" list. I understand the desire to keep this paper focused and leave additional analyses for later publications. The core observation---that six amino acids can recapitulate diverse protein folds---is interesting and timely given the Bennu data. However, as the paper stands, it is an interesting contribution but a limited one. Whether that is sufficient for this venue is an editorial decision.\n\nNit-pick: The response document refers to \"Fig. 3\" when discussing structural comparisons, but Figure 3 shows TM-score curves; the authors likely meant Figure 4, which contains the structural overlays.", "reviewer_2": "I thank the authors for their rebuttal and addresses to my comments. The extension of the analysis to additional proteins greatly strengthens the results and claims. I understand that the authors would like to reserve the sequence-level, evolutionary/phylogenetic, and experimental analyses for future work; my final remaining comment is that these be discussed in full sentence text in the final section, rather than bullet summaries. Provided that is addressed, I recommend the manuscript for publication."}, "author_response": "Response to Reviewers for NCOMMS-25-40849A\nStuart Bartlett, Arushi Gupta, Dominic Phung, Josh Goldford, Jiani Yang, Klara\nHlouchova, Woodward Fischer and Yuk L. Yung\nWe thank both reviewers for their final comments on our manuscript, as well as previous\ncomments which led to significant improvements in the paper and results. Based on Reviewer\n2’s suggestion, we have converted the bullet point list of immediate extensions to flowing text."}]

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