Uncertainty of prebiotic scenarios: the case of the non-enzymatic reverse tricarboxylic acid cycle

Reactant Discovery with an Ab Initio Nanoreactor: Exploration of Astrophysical N-Heterocycle Precursors and Formation Pathways

The incorporation of nitrogen atoms into cyclic compounds is essential for terrestrial life; nitrogen-containing (N-)heterocycles make up DNA and RNA nucleobases, several amino acids, B vitamins, porphyrins, and other components of biomolecules. The discovery of these molecules on meteorites with non-terrestrial isotopic abundances supports the hypothesis of exogenous delivery of prebiotic material to early Earth; however, there has been no detection of these species in interstellar environments, indicating that there is a need for greater knowledge of their astrochemical formation and destruction pathways. Here, we present results of simulations of gas-phase pyrrole and pyridine formation from an ab initio nanoreactor, a first-principles molecular dynamics simulation method that accelerates reaction discovery by applying non-equilibrium forces that are agnostic to individual reaction coordinates. Using the nanoreactor in a retrosynthetic mode, starting with the N-heterocycle of interest and a radical leaving group, then considering the discovered reaction pathways in reverse, a rich landscape of N-heterocycle-forming reactivity can be found. Several of these reaction pathways, when mapped to their corresponding minimum energy paths, correspond to novel barrierless formation pathways for pyridine and pyrrole, starting from both detected and hypothesized astrochemical precursors. This study demonstrates how first-principles reaction discovery can build mechanistic knowledge in astrochemical environments as well as in early Earth models such as Titan's atmosphere where N-heterocycles have been tentatively detected.

Kinetics and coexistence of autocatalytic reaction cycles

Biological reproduction rests ultimately on chemical autocatalysis. Autocatalytic chemical cycles are thought to have played an important role in the chemical complexification en route to life. There are two, related issues: what chemical transformations allow such cycles to form, and at what speed they are operating. Here we investigate the latter question for solitary as well as competitive autocatalytic cycles in resource-unlimited batch and resource-limited chemostat systems. The speed of growth tends to decrease with the length of a cycle. Reversibility of the reproductive step results in parabolic growth that is conducive to competitive coexistence. Reversibility of resource uptake also slows down growth. Unilateral help by a cycle of its competitor tends to favour the competitor (in effect a parasite on the helper), rendering coexistence unlikely. We also show that deep learning is able to predict the outcome of competition just from the topology and the kinetic rate constants, provided the training set is large enough. These investigations pave the way for studying autocatalytic cycles with more complicated coupling, such as mutual catalysis.

The modular biochemical reaction network structure of cellular translation

Translation is an essential attribute of all living cells. At the heart of cellular operation, it is a chemical information decoding process that begins with an input string of nucleotides and ends with the synthesis of a specific output string of peptides. The translation process is interconnected with gene expression, physiological regulation, transcription, and responses to signaling molecules, among other cellular functions. Foundational efforts have uncovered a wealth of knowledge about the mechanistic functions of the components of translation and their many interactions between them, but the broader biochemical connections between translation, metabolism and polymer biosynthesis that enable translation to occur have not been comprehensively mapped. Here we present a multilayer graph of biochemical reactions describing the translation, polymer biosynthesis and metabolism networks of an Escherichia coli cell. Intriguingly, the compounds that compose these three layers are distinctly aggregated into three modes regardless of their layer categorization. Multimodal mass distributions are well-known in ecosystems, but this is the first such distribution reported at the biochemical level. The degree distributions of the translation and metabolic networks are each likely to be heavy-tailed, but the polymer biosynthesis network is not. A multimodal mass-degree distribution indicates that the translation and metabolism networks are each distinct, adaptive biochemical modules, and that the gaps between the modes reflect evolved responses to the functional use of metabolite, polypeptide and polynucleotide compounds. The chemical reaction network of cellular translation opens new avenues for exploring complex adaptive phenomena such as percolation and phase changes in biochemical contexts.

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

Evolutionary Origin and Functional Diversification of Aminotransferases

Aminotransferases (ATs) are pyridoxal 5′-phosphate–dependent enzymes that catalyze the transamination reactions between amino acid donor and keto acid acceptor substrates. Modern AT enzymes constitute ∼2% of all classified enzymatic activities, play central roles in nitrogen metabolism, and generate multitude of primary and secondary metabolites. ATs likely diverged into four distinct AT classes before the appearance of the last universal common ancestor and further expanded to a large and diverse enzyme family. Although the AT family underwent an extensive functional specialization, many AT enzymes retained considerable substrate promiscuity and multifunctionality because of their inherent mechanistic, structural, and functional constraints. This review summarizes the evolutionary history, diverse metabolic roles, reaction mechanisms, and structure–function relationships of the AT family enzymes, with a special emphasis on their substrate promiscuity and multifunctionality. Comprehensive characterization of AT substrate specificity is still needed to reveal their true metabolic functions in interconnecting various branches of the nitrogen metabolic network in different organisms.

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.

Thermodynamics of Potential CHO Metabolites in a Reducing Environment

How did metabolism arise and evolve? What chemical compounds might be suitable to support and sustain a proto-metabolism before the advent of more complex co-factors? We explore these questions by using first-principles quantum chemistry to calculate the free energies of CHO compounds in aqueous solution, allowing us to probe the thermodynamics of core extant cycles and their closely related chemical cousins. By framing our analysis in terms of the simplest feasible cycle and its permutations, we analyze potentially favorable thermodynamic cycles for CO₂ fixation with H₂ as a reductant. We find that paying attention to redox states illuminates which reactions are endergonic or exergonic. Our results highlight the role of acetate in proto-metabolic cycles, and its connection to other prebiotic molecules such as glyoxalate, glycolaldehyde, and glycolic acid.

Chemical Basis of Carbon Fixation Autotrophic Paleometabolism

Abstract

On the basis of biomimetic, phylometabolic, and thermodynamic analysis of modern CO2 assimilation pathways, a paleophenotypic reconstruction of ancient autotrophic metabolism systems was carried out. As a chemical basis for CO2 fixation paleometabolism, metabolic networks capable of self-reproduction and evolution are considered, and the reversibility of the transformation reactions of its intermediates is the most important factor in self-development of this network. The substances of the C–H–O system, paragenetically associated with hydrocarbons, create a phase space, which is a set of universal intermediates of the autotrophic paleometabolism chemical network. The concept of two strategies for the origin and development of autotrophic carbon fixation paleometabolism in the oxidized (CO2) and reduced (CH4) redox regimes of degassing of the ancient Earth is proposed. It was shown that P, T, and the redox conditions of hydrothermal systems of the early Archean were favorable for the development of primary methanotrophic metabolism.

Kinetic Selection in the Out-of-Equilibrium Autocatalytic Reaction Networks that Produce Macrocyclic Peptides

Autocatalytic reaction networks are instrumental for validating scenarios for the emergence of life on Earth and for synthesizing life de novo. Here, we demonstrate that dimeric thioesters of tripeptides with the general structure (Cys-Xxx-Gly-SEt)₂ form strongly interconnected autocatalytic reaction networks that predominantly generate macrocyclic peptides up to 69 amino acids long. Some macrocycles of 6-12 amino acids were isolated from the product pool and were characterized by NMR spectroscopy and single-crystal X-ray analysis. We studied the autocatalytic formation of macrocycles in a flow reactor in the presence of acrylamide, whose conjugate addition to thiols served as a model "removal" reaction. These results indicate that even not template-assisted autocatalytic production combined with competing removal of molecular species in an open compartment could be a feasible route for selecting functional molecules during the pre-Darwinian stages of molecular evolution.

Capillary electrophoresis method for analysis of inorganic and organic anions related to habitability and the search for life

In situ missions of exploration require analytical methods that are capable of detecting a wide range of molecular targets in complex matrices without a priori assumptions of sample composition. Furthermore, these methods should minimize the number of reagents needed and any sample preparation steps. We have developed a method for the detection of metabolically relevant inorganic and organic anions that is suitable for implementation on in situ spaceflight missions. Using 55 mM acetic acid, 50 mM triethylamine, and 5% glycerol, more than 21 relevant anions are separated in less than 20 min. The method is robust to sample ionic strength, tolerating high concentrations of background salts (up to 900 mM NaCl and 300 mM MgSO₄ ). This is an important feature for future missions to ocean worlds. The method was validated using a culture of Escherichia coli and with high salinity natural samples collected from Mono Lake, California.

A thermodynamic atlas of carbon redox chemical space

Carbon redox chemistry plays a fundamental role in biology. However, the thermodynamic and physicochemical principles underlying the rise of metabolites involved in redox biochemistry remain poorly understood. Our work introduces the theory and techniques that allow us to quantify and understand the global energy landscape of carbon redox biochemistry. We analyze the space of all possible oxidation states of linear-chain molecules with two to five carbon atoms and generate a detailed atlas of the thermodynamic stability of metabolites in comparison to nonbiological molecules. Although the emergence of life required the underlying chemistry to bootstrap itself out of equilibrium, a quantitative understanding of the environment-dependent thermodynamic landscape of prebiotic molecules will be extremely valuable for future origins of life models.

Redox biochemistry plays a key role in the transduction of chemical energy in living systems. However, the compounds observed in metabolic redox reactions are a minuscule fraction of chemical space. It is not clear whether compounds that ended up being selected as metabolites display specific properties that distinguish them from nonbiological compounds. Here, we introduce a systematic approach for comparing the chemical space of all possible redox states of linear-chain carbon molecules to the corresponding metabolites that appear in biology. Using cheminformatics and quantum chemistry, we analyze the physicochemical and thermodynamic properties of the biological and nonbiological compounds. We find that, among all compounds, aldose sugars have the highest possible number of redox connections to other molecules. Metabolites are enriched in carboxylic acid functional groups and depleted of ketones and aldehydes and have higher solubility than nonbiological compounds. Upon constructing the energy landscape for the full chemical space as a function of pH and electron-donor potential, we find that metabolites tend to have lower Gibbs energies than nonbiological molecules. Finally, we generate Pourbaix phase diagrams that serve as a thermodynamic atlas to indicate which compounds are energy minima in redox chemical space across a set of pH values and electron-donor potentials. While escape from thermodynamic equilibrium toward kinetically driven states is a hallmark of life and its origin, we envision that a deeper quantitative understanding of the environment-dependent thermodynamic landscape of putative prebiotic molecules will provide a crucial reference for future origins-of-life models.

Prebiotic Reaction Networks in Water

A prevailing strategy in origins of life studies is to explore how chemistry constrained by hypothetical prebiotic conditions could have led to molecules and system level processes proposed to be important for life's beginnings. This strategy has yielded model prebiotic reaction networks that elucidate pathways by which relevant compounds can be generated, in some cases, autocatalytically. These prebiotic reaction networks provide a rich platform for further understanding and development of emergent "life-like" behaviours. In this review, recent advances in experimental and analytical procedures associated with classical prebiotic reaction networks, like formose and Miller-Urey, as well as more recent ones are highlighted. Instead of polymeric networks, i.e., those based on nucleic acids or peptides, the focus is on small molecules. The future of prebiotic chemistry lies in better understanding the genuine complexity that can result from reaction networks and the construction of a centralised database of reactions useful for predicting potential network evolution is emphasised.

A plausible metal-free ancestral analogue of the Krebs cycle composed entirely of α-ketoacids

Efforts to decipher the prebiotic roots of metabolic pathways have focused on recapitulating modern biological transformations, with metals typically serving in place of cofactors and enzymes. Here we show that the reaction of glyoxylate with pyruvate under mild aqueous conditions produces a series of α-ketoacid analogues of the reductive citric acid cycle without the need for metals or enzyme catalysts. The transformations proceed in the same sequence as the reverse Krebs cycle, resembling a protometabolic pathway, with glyoxylate acting as both the carbon source and reducing agent. Furthermore, the α-ketoacid analogues provide a natural route for the synthesis of amino acids by transamination with glycine, paralleling the extant metabolic mechanisms and obviating the need for metal-catalysed abiotic reductive aminations. This emerging sequence of prebiotic reactions could have set the stage for the advent of increasingly sophisticated pathways operating under catalytic control.

Nonenzymatic Metabolic Reactions and Life's Origins

Prebiotic chemistry aims to explain how the biochemistry of life as we know it came to be. Most efforts in this area have focused on provisioning compounds of importance to life by multistep synthetic routes that do not resemble biochemistry. However, gaining insight into why core metabolism uses the molecules, reactions, pathways, and overall organization that it does requires us to consider molecules not only as synthetic end goals. Equally important are the dynamic processes that build them up and break them down. This perspective has led many researchers to the hypothesis that the first stage of the origin of life began with the onset of a primitive nonenzymatic version of metabolism, initially catalyzed by naturally occurring minerals and metal ions. This view of life's origins has come to be known as "metabolism first". Continuity with modern metabolism would require a primitive version of metabolism to build and break down ketoacids, sugars, amino acids, and ribonucleotides in much the same way as the pathways that do it today. This review discusses metabolic pathways of relevance to the origin of life in a manner accessible to chemists, and summarizes experiments suggesting several pathways might have their roots in prebiotic chemistry. Finally, key remaining milestones for the protometabolic hypothesis are highlighted.

Reactivity of Metabolic Intermediates and Cofactor Stability under Model Early Earth Conditions

Understanding the emergence of metabolic pathways is key to unraveling the factors that promoted the origin of life. One popular view is that protein cofactors acted as catalysts prior to the evolution of the protein enzymes with which they are now associated. We investigated the stability of acetyl coenzyme A (Acetyl Co-A, the group transfer cofactor in citric acid synthesis in the TCA cycle) under early Earth conditions, as well as whether Acetyl Co-A or its small molecule analogs thioacetate or acetate can catalyze the transfer of an acetyl group onto oxaloacetate in the absence of the citrate synthase enzyme. Several different temperatures, pH ranges, and compositions of aqueous environments were tested to simulate the Earth's early ocean and its possible components; the effect of these variables on oxaloacetate and cofactor chemistry were assessed under ambient and anoxic conditions. The cofactors tested are chemically stable under early Earth conditions, but none of the three compounds (Acetyl Co-A, thioacetate, or acetate) promoted synthesis of citric acid from oxaloacetate under the conditions tested. Oxaloacetate reacted with itself and/or decomposed to form a sequence of other products under ambient conditions, and under anoxic conditions was more stable; under ambient conditions the specific chemical pathways observed depended on the environmental conditions such as pH and presence/absence of bicarbonate or salt ions in early Earth ocean simulants. This work demonstrates the stability of these metabolic intermediates under anoxic conditions. However, even though free cofactors may be stable in a geological environmental setting, an enzyme or other mechanism to promote reaction specificity would likely be necessary for at least this particular reaction to proceed.

Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism

It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals before genetically encoded enzymes. A major challenge in unravelling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms with physico-chemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fuelled by a thioester- and redox-driven variant of the reductive tricarboxylic acid cycle that is capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors are not necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a protocell, and find that different combinations of carbon sources and electron donors can support the continuous production of a minimal ancient 'biomass' composed of putative early biopolymers and fatty acids.

Hidden Concepts in the History and Philosophy of Origins-of-Life Studies: a Workshop Report

In this review, we describe some of the central philosophical issues facing origins-of-life research and provide a targeted history of the developments that have led to the multidisciplinary field of origins-of-life studies. We outline these issues and developments to guide researchers and students from all fields. With respect to philosophy, we provide brief summaries of debates with respect to (1) definitions (or theories) of life, what life is and how research should be conducted in the absence of an accepted theory of life, (2) the distinctions between synthetic, historical, and universal projects in origins-of-life studies, issues with strategies for inferring the origins of life, such as (3) the nature of the first living entities (the "bottom up" approach) and (4) how to infer the nature of the last universal common ancestor (the "top down" approach), and (5) the status of origins of life as a science. Each of these debates influences the others. Although there are clusters of researchers that agree on some answers to these issues, each of these debates is still open. With respect to history, we outline several independent paths that have led to some of the approaches now prevalent in origins-of-life studies. These include one path from early views of life through the scientific revolutions brought about by Linnaeus (von Linn.), Wöhler, Miller, and others. In this approach, new theories, tools, and evidence guide new thoughts about the nature of life and its origin. We also describe another family of paths motivated by a" circularity" approach to life, which is guided by such thinkers as Maturana & Varela, Gánti, Rosen, and others. These views echo ideas developed by Kant and Aristotle, though they do so using modern science in ways that produce exciting avenues of investigation. By exploring the history of these ideas, we can see how many of the issues that currently interest us have been guided by the contexts in which the ideas were developed. The disciplinary backgrounds of each of these scholars has influenced the questions they sought to answer, the experiments they envisioned, and the kinds of data they collected. We conclude by encouraging scientists and scholars in the humanities and social sciences to explore ways in which they can interact to provide a deeper understanding of the conceptual assumptions, structure, and history of origins-of-life research. This may be useful to help frame future research agendas and bring awareness to the multifaceted issues facing this challenging scientific question.

A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions

A quantitative understanding of the thermodynamics of biochemical reactions is essential for accurately modeling metabolism. The group contribution method (GCM) is one of the most widely used approaches to estimate standard Gibbs energies and redox potentials of reactions for which no experimental measurements exist. Previous work has shown that quantum chemical predictions of biochemical thermodynamics are a promising approach to overcome the limitations of GCM. However, the quantum chemistry approach is significantly more expensive. Here, we use a combination of quantum chemistry and machine learning to obtain a fast and accurate method for predicting the thermodynamics of biochemical redox reactions. We focus on predicting the redox potentials of carbonyl functional group reductions to alcohols and amines, two of the most ubiquitous carbon redox transformations in biology. Our method relies on semiempirical quantum chemistry calculations calibrated with Gaussian process (GP) regression against available experimental data and results in higher predictive power than the GCM at low computational cost. Direct calibration of GCM and fingerprint-based predictions (without quantum chemistry) with GP regression also results in significant improvements in prediction accuracy, demonstrating the versatility of the approach. We design and implement a network expansion algorithm that iteratively reduces and oxidizes a set of natural seed metabolites and demonstrate the high-throughput applicability of our method by predicting the standard potentials of more than 315 000 redox reactions involving approximately 70 000 compounds. Additionally, we developed a novel fingerprint-based framework for detecting molecular environment motifs that are enriched or depleted across different regions of the redox potential landscape. We provide open access to all source code and data generated.

Prebiotic chemistry in neutral/reduced-alkaline gas-liquid interfaces

The conditions for the potential abiotic formation of organic compounds from inorganic precursors have great implications for our understanding of the origin of life on Earth and for its possible detection in other environments of the Solar System. It is known that aerosol-interfaces are effective at enhancing prebiotic chemical reactions, but the roles of salinity and pH have been poorly investigated to date. Here, we experimentally demonstrate the uniqueness of alkaline aerosols as prebiotic reactors that produce an undifferentiated accumulation of a variety of multi-carbon biomolecules resulting from high-energy processes (in our case, electrical discharges). Using simulation experiments, we demonstrate that the detection of important biomolecules in tholins increases when plausible and particular local planetary environmental conditions are simulated. A greater diversity in amino acids, carboxylic acids, N-heterocycles, and ketoacids, such as glyoxylic and pyruvic acid, was identified in tholins synthetized from reduced and neutral atmospheres in the presence of alkaline aqueous aerosols than that from the same atmospheres but using neutral or acidic aqueous aerosols.

Uncertainty quantification for quantum chemical models of complex reaction networks

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

Systems protobiology: origin of life in lipid catalytic networks

Life is that which replicates and evolves, but there is no consensus on how life emerged. We advocate a systems protobiology view, whereby the first replicators were assemblies of spontaneously accreting, heterogeneous and mostly non-canonical amphiphiles. This view is substantiated by rigorous chemical kinetics simulations of the graded autocatalysis replication domain (GARD) model, based on the notion that the replication or reproduction of compositional information predated that of sequence information. GARD reveals the emergence of privileged non-equilibrium assemblies (composomes), which portray catalysis-based homeostatic (concentration-preserving) growth. Such a process, along with occasional assembly fission, embodies cell-like reproduction. GARD pre-RNA evolution is evidenced in the selection of different composomes within a sparse fitness landscape, in response to environmental chemical changes. These observations refute claims that GARD assemblies (or other mutually catalytic networks in the metabolism first scenario) cannot evolve. Composomes represent both a genotype and a selectable phenotype, anteceding present-day biology in which the two are mostly separated. Detailed GARD analyses show attractor-like transitions from random assemblies to self-organized composomes, with negative entropy change, thus establishing composomes as dissipative systems-hallmarks of life. We show a preliminary new version of our model, metabolic GARD (M-GARD), in which lipid covalent modifications are orchestrated by non-enzymatic lipid catalysts, themselves compositionally reproduced. M-GARD fills the gap of the lack of true metabolism in basic GARD, and is rewardingly supported by a published experimental instance of a lipid-based mutually catalytic network. Anticipating near-future far-reaching progress of molecular dynamics, M-GARD is slated to quantitatively depict elaborate protocells, with orchestrated reproduction of both lipid bilayer and lumenal content. Finally, a GARD analysis in a whole-planet context offers the potential for estimating the probability of life's emergence. The invigorated GARD scrutiny presented in this review enhances the validity of autocatalytic sets as a bona fide early evolution scenario and provides essential infrastructure for a paradigm shift towards a systems protobiology view of life's origin.

The Matter Simulation (R)evolution

To date, the program for the development of methods and models for atomistic and continuum simulation directed toward chemicals and materials has reached an incredible degree of sophistication and maturity. Currently, one can witness an increasingly rapid emergence of advances in computing, artificial intelligence, and robotics. This drives us to consider the future of computer simulation of matter from the molecular to the human length and time scales in a radical way that deliberately dares to go beyond the foreseeable next steps in any given discipline. This perspective article presents a view on this future development that we believe is likely to become a reality during our lifetime.

Efficient prediction of reaction paths through molecular graph and reaction network analysis

Despite remarkable advances in computational chemistry, prediction of reaction mechanisms is still challenging, because investigating all possible reaction pathways is computationally prohibitive due to the high complexity of chemical space. A feasible strategy for efficient prediction is to utilize chemical heuristics. Here, we propose a novel approach to rapidly search reaction paths in a fully automated fashion by combining chemical theory and heuristics. A key idea of our method is to extract a minimal reaction network composed of only favorable reaction pathways from the complex chemical space through molecular graph and reaction network analysis. This can be done very efficiently by exploring the routes connecting reactants and products with minimum dissociation and formation of bonds. Finally, the resulting minimal network is subjected to quantum chemical calculations to determine kinetically the most favorable reaction path at the predictable accuracy. As example studies, our method was able to successfully find the accepted mechanisms of Claisen ester condensation and cobalt-catalyzed hydroformylation reactions.

Exploring astrobiology using in silico molecular structure generation

The origin of life is typically understood as a transition from inanimate or disorganized matter to self-organized, 'animate' matter. This transition probably took place largely in the context of organic compounds, and most approaches, to date, have focused on using the organic chemical composition of modern organisms as the main guide for understanding this process. However, it has gradually come to be appreciated that biochemistry, as we know it, occupies a minute volume of the possible organic 'chemical space'. As the majority of abiotic syntheses appear to make a large set of compounds not found in biochemistry, as well as an incomplete subset of those that are, it is possible that life began with a significantly different set of components. Chemical graph-based structure generation methods allow for exhaustive in silico enumeration of different compound types and different types of 'chemical spaces' beyond those used by biochemistry, which can be explored to help understand the types of compounds biology uses, as well as to understand the nature of abiotic synthesis, and potentially design novel types of living systems.This article is part of the themed issue 'Reconceptualizing the origins of life'.

Computational exploration of the chemical structure space of possible reverse tricarboxylic acid cycle constituents

The reverse tricarboxylic acid (rTCA) cycle has been explored from various standpoints as an idealized primordial metabolic cycle. Its simplicity and apparent ubiquity in diverse organisms across the tree of life have been used to argue for its antiquity and its optimality. In 2000 it was proposed that chemoinformatics approaches support some of these views. Specifically, defined queries of the Beilstein database showed that the molecules of the rTCA are heavily represented in such compound databases. We explore here the chemical structure “space,” e.g. the set of organic compounds which possesses some minimal set of defining characteristics, of the rTCA cycle’s intermediates using an exhaustive structure generation method. The rTCA’s chemical space as defined by the original criteria and explored by our method is some six to seven times larger than originally considered. Acknowledging that each assumption in what is a defining criterion making the rTCA cycle special limits possible generative outcomes, there are many unrealized compounds which fulfill these criteria. That these compounds are unrealized could be due to evolutionary frozen accidents or optimization, though this optimization may also be for systems-level reasons, e.g., the way the pathway and its elements interface with other aspects of metabolism.

Metals promote sequences of the reverse Krebs cycle

The rTCA cycle (also known as the reverse Krebs cycle) is a central anabolic biochemical pathway whose origins are proposed to trace back to geochemistry, long before the advent of enzymes, RNA or cells, and whose imprint still remains intimately embedded in the structure of core metabolism. If it existed, a primordial version of the rTCA cycle would necessarily have been catalyzed by naturally occurring minerals at the earliest stage of the transition from geochemistry to biochemistry. Here we report non-enzymatic promotion of multiple reactions of the rTCA cycle in consecutive sequence, whereby 6 of its 11 reactions are promoted by Zn²⁺, Cr³⁺ and Fe⁰ in an acidic aqueous solution. Two distinct three-reaction sequences can be achieved under a common set of conditions. Selectivity is observed for reduction reactions producing rTCA cycle intermediates compared to those leading off-cycle. Reductive amination of ketoacids to furnish amino acids is observed under similar conditions. The emerging reaction network supports the feasibility of primitive anabolism in an acidic, metal-rich reducing environment.

Sulfate radicals enable a non-enzymatic Krebs cycle precursor

The evolutionary origins of the tricarboxylic acid cycle (TCA), or Krebs cycle, are so far unclear. Despite a few years ago, the existence of a simple non-enzymatic Krebs-cycle catalyst has been dismissed 'as an appeal to magic', citrate and other intermediates have meanwhile been discovered on a carbonaceous meteorite and do interconvert non-enzymatically. To identify the non-enzymatic Krebs cycle catalyst, we used combinatorial, quantitative high-throughput metabolomics to systematically screen iron and sulfate reaction milieus that orient on Archean sediment constituents. TCA cycle intermediates are found stable in water and in the presence of most iron and sulfate species, including simple iron-sulfate minerals. However, we report that TCA intermediates undergo 24 interconversion reactions in the presence of sulfate radicals that form from peroxydisulfate. The non-enzymatic reactions critically cover a topology as present in the Krebs cycle, the glyoxylate shunt and the succinic semialdehyde pathways. Assembled in a chemical network, the reactions achieve more than ninety percent carbon recovery. Our results show that a non-enzymatic precursor for the Krebs cycle is biologically sensible, efficient, and forms spontaneously in the presence of sulfate radicals.

Coenzyme world model of the origin of life

The origin of life means the emergence of heritable and evolvable self-reproduction. However the mechanisms of primordial heredity were different from those in contemporary cells. Here I argue that primordial life had no nucleic acids; instead heritable signs were represented by isolated catalytically active self-reproducing molecules, similar to extant coenzymes, which presumably colonized surfaces of oil droplets in water. The model further assumes that coenzyme-like molecules (CLMs) changed surface properties of oil droplets (e.g., by oxidizing terminal carbons), and in this way created and sustained favorable conditions for their own self-reproduction. Such niche-dependent self-reproduction is a necessary condition for cooperation between different kinds of CLMs because they have to coexist in the same oil droplets and either succeed or perish together. Additional kinds of hereditary molecules were acquired via coalescence of oil droplets carrying different kinds of CLMs or via modification of already existing CLMs. Eventually, polymerization of CLMs became controlled by other polymers used as templates; and this kind of template-based synthesis eventually resulted in the emergence of RNA-like replicons. Apparently, oil droplets transformed into the outer membrane of cells via engulfing water, stabilization of the surface, and osmoregulation. In result, the metabolism was internalized allowing cells to accumulate free-floating resources (e.g., animoacids, ATP), which was a necessary condition for the development of protein synthesis. Thus, life originated from simple but already functional molecules, and its gradual evolution towards higher complexity was driven by cooperation and natural selection.