Evolution of complex chemical mixtures reveals combinatorial compression and population synchronicity

Many open questions about the origins of life are centred on the generation of complex chemical species. Past work has characterized specific chemical reactions that might lead to biological molecules. Here we establish an experimental model of chemical evolution to investigate general processes by which chemical systems continuously change. We used water as a chemical reactant, product and medium. We leveraged oscillating water activity at near-ambient temperatures to cause ratcheting of near-equilibrium reactions in mixtures of organic molecules containing carboxylic acids, amines, thiols and hydroxyl groups. Our system (1) undergoes continuous change with transitions to new chemical spaces while not converging throughout the experiment; (2) demonstrates combinatorial compression with stringent chemical selection; and (3) displays synchronicity of molecular populations. Our results suggest that chemical evolution and selection can be observed in organic mixtures and might ultimately be adapted to produce a broad array of molecules with novel structures and functions.

Spontaneous Emergence of Lipid Vesicles in a Coacervate-Based Compartmentalized System

The spontaneous emergence of lipid vesicles in the absence of evolved biological machinery represents a major challenge for bottom-up synthetic biology. We show that coacervate microdroplets could create a compartmentalized environment that enriches lipid molecules and facilitates their spontaneous assembly into lipid vesicles. These vesicles can escape from the coacervate microdroplets in a continuous process under non-equilibrium conditions, resembling a constant production process akin to a "primitive enzyme" factory assembly line. These findings significantly extend our understanding of the intricate interaction between lipid molecules and coacervate microdroplets, shedding light on the emergence of cellular systems and offering a new perspective on the conditions necessary for the development of life on Earth.

Discovery of New Synthetic Routes of Amino Acids in Prebiotic Chemistry

The origin of life on Earth remains one of the most perplexing challenges in biochemistry. While numerous bottom-up experiments under prebiotic conditions have provided valuable insights into the spontaneous chemical genesis of life, there remains a significant gap in the theoretical understanding of the complex reaction processes involved. In this study, we propose a novel approach using a roto-translationally invariant potential (RTIP) formulated with pristine Cartesian coordinates to facilitate the simulation of chemical reactions. By employing RTIP pathway sampling to explore the reactivity of primitive molecules, we identified several low-energy reaction mechanisms, such as two-hydrogen-transfer hydrogenation and HCOOH-catalyzed hydration and amination. This led to the construction of a comprehensive reaction network, illustrating the synthesis pathways for glycine, serine, and alanine. Further thermodynamic analysis highlights the pivotal role of formaldimine as a key precursor in amino acid synthesis, owing to its more favorable reactivity in coupling reactions compared to the traditionally recognized hydrogen cyanide. Our study demonstrates that the RTIP methodology, coupled with a divide-and-conquer strategy, provides new insights into the simulation of complex reaction processes, offering promising applications for advancing organic design and synthesis.

Carbon- and Nitrogen-Based Complexes as Photocatalysts for Prebiotic and Oxygen Chemistry during Earth Evolution

Sunlight has long served as primary energy source on our planet, shaping the behavior of living organisms. Extensive research has been dedicated to unraveling the evolutionary pathways involved. When the formation of Earth atmosphere, it primarily consisted of small gas molecules, which are considered crucial for the emergence of life. Recent demonstrations have shown that these molecules can also be transformed into semiconductors, with the potential to harness solar energy and catalyze chemical reactions as photocatalysts. Building upon this research, this minireview focuses on the potential revolutionary impact of photocatalysis on Earth. Initially, it examines key reactions, such as the formation of prebiotic molecules and the oxygen evolution reaction via water oxidation. Additionally, various C-N complexes in photocatalysts are explored, showcasing their roles in catalyzing chemical reactions. The conclusion and outlook provide a potential pathway for the evolution of Earth, shedding light on the significance of metal-free photocatalysts in development of Earth.

Systematic, computational discovery of multicomponent and one-pot reactions

Discovery of new types of reactions is essential to organic chemistry because it expands the scope of accessible molecular scaffolds and can enable more economical syntheses of existing structures. In this context, the so-called multicomponent reactions, MCRs, are of particular interest because they can build complex scaffolds from multiple starting materials in just one step, without purification of intermediates. However, for over a century of active research, MCRs have been discovered rather than designed, and their number remains limited to only several hundred. This work demonstrates that computers taught the essential knowledge of reaction mechanisms and rules of physical-organic chemistry can design - completely autonomously and in large numbers - mechanistically distinct MCRs. Moreover, when supplemented by models to approximate kinetic rates, the algorithm can predict reaction yields and identify reactions that have potential for organocatalysis. These predictions are validated by experiments spanning different modes of reactivity and diverse product scaffolds.

Estimation of multicomponent reactions' yields from networks of mechanistic steps

This work describes estimation of yields of complex, multicomponent reactions (MCRs) based on the modeled networks of mechanistic steps spanning both the main reaction pathway as well as immediate and downstream side reactions. Because experimental values of the kinetic rate constants for individual mechanistic transforms are extremely sparse, these constants are approximated here using Mayr's nucleophilicity and electrophilicity parameters fine-tuned by correction terms grounded in linear free-energy relationships. With this formalism, the model trained on the mechanistic networks of only 20 - but mechanistically- and yield-diverse MCRs - transfers well to newly discovered MCRs that are based on markedly different mechanisms and types of individual mechanistic transforms. These results suggest that mechanistic-level approach to yield estimation may be a useful alternative to models that are derived from full-reaction data and lack information about yield-lowering side reactions.

From Catalysis of Evolution to Evolution of Catalysis

ConspectusThe mystery of the origins of life is one of the most difficult yet intriguing challenges to which humanity has grappled. How did biopolymers emerge in the absence of enzymes (evolved biocatalysts), and how did long-lasting chemical evolution find a path to the highly selective complex biology that we observe today? In this paper, we discuss a chemical framework that explores the very roots of catalysis, demonstrating how standard catalytic activity based on chemical and physical principles can evolve into complex machineries. We provide several examples of how prebiotic catalysis by small molecules can be exploited to facilitate polymerization, which in biology has transformed the nature of catalysis. Thus, catalysis evolved, and evolution was catalyzed, during the transformation of prebiotic chemistry to biochemistry. Traditionally, a catalyst is defined as a substance that (i) speeds up a chemical reaction by lowering activation energy through different chemical mechanisms and (ii) is not consumed during the course of the reaction. However, considering prebiotic chemistry, which involved a highly diverse chemical space (i.e., high number of potential reactants and products) and constantly changing environment that lacked highly sophisticated catalytic machinery, we stress here that a more primitive, broader definition should be considered. Here, we consider a catalyst as any chemical species that lowers activation energy. We further discuss various demonstrations of how simple prebiotic molecules such as hydroxy acids and mercaptoacids promote the formation of peptide bonds via energetically favored exchange reactions. Even though the small molecules are partially regenerated and partially retained within the resulting oligomers, these prebiotic catalysts fulfill their primary role. Catalysis by metal ions and in complex chemical mixtures is also highlighted. We underline how chemical evolution is primarily dictated by kinetics rather than thermodynamics and demonstrate a novel concept to support this notion. Moreover, we propose a new perspective on the role of water in prebiotic catalysis. The role of water as simply a "medium" obscures its importance as an active participant in the chemistry of life, specifically as a very efficient catalyst and as a participant in many chemical transformations. Here we highlight the unusual contribution of water to increasing complexification over the course of chemical evolution. We discuss possible pathways by which prebiotic catalysis promoted chemical selection and complexification. Taken together, this Account draws a connection line between prebiotic catalysis and contemporary biocatalysis and demonstrates that the fundamental elements of chemical catalysis are embedded within today's biocatalysts. This Account illustrates how the evolution of catalysis was intertwined with chemical evolution from the very beginning.

Multi-Stimuli-Responsive Network of Multicatalytic Reactions using a Single Palladium/Platinum Catalyst

Given her unrivalled proficiency in the synthesis of all molecules of life, nature has been an endless source of inspiration for developing new strategies in organic chemistry and catalysis. However, one feature that remains thus far beyond chemists' grasp is her unique ability to adapt the productivity of metabolic processes in response to triggers that indicate the temporary need for specific metabolites. To demonstrate the remarkable potential of such stimuli-responsive systems, we present a metabolism-inspired network of multicatalytic processes capable of selectively synthesising a range of products from simple starting materials. Specifically, the network is built of four classes of distinct catalytic reactions-cross-couplings, substitutions, additions, and reductions, involving three organic starting materials-terminal alkyne, aryl iodide, and hydrosilane. All starting materials are either introduced sequentially or added to the system at the same time, with no continuous influx of reagents or efflux of products. All processes in the system are catalysed by a multifunctional heteronuclear PdII/PtII complex, whose performance can be controlled by specific additives and external stimuli. The reaction network exhibits a substantial degree of orthogonality between different pathways, enabling the controllable synthesis of ten distinct products with high efficiency and selectivity through simultaneous triggering and suppression mechanisms.

Rethinking life and predicting its origin

The definition, origin and recreation of life remain elusive. As others have suggested, only once we put life into reductionist physical terms will we be able to solve those questions. To that end, this work proposes the phenomenon of life to be the product of two dissipative mechanisms. From them, one characterises extant biological life and deduces a testable scenario for its origin. The proposed theory of life allows its replication, reinterprets ecological evolution and creates new constraints on the search for life.

Alternative Pathways in Astrobiology: Reviewing and Synthesizing Contingency and Non-Biomolecular Origins of Terrestrial and Extraterrestrial Life

The pursuit of understanding the origins of life (OoL) on and off Earth and the search for extraterrestrial life (ET) are central aspects of astrobiology. Despite the considerable efforts in both areas, more novel and multifaceted approaches are needed to address these profound questions with greater detail and with certainty. The complexity of the chemical milieu within ancient geological environments presents a diverse landscape where biomolecules and non-biomolecules interact. This interaction could lead to life as we know it, dominated by biomolecules, or to alternative forms of life where non-biomolecules could play a pivotal role. Such alternative forms of life could be found beyond Earth, i.e., on exoplanets and the moons of Jupiter and Saturn. Challenging the notion that all life, including ET life, must use the same building blocks as life on Earth, the concept of contingency-when expanded beyond its macroevolution interpretation-suggests that non-biomolecules may have played essential roles at the OoL. Here, we review the possible role of contingency and non-biomolecules at the OoL and synthesize a conceptual model formally linking contingency with non-biomolecular OoL theories. This model emphasizes the significance of considering the role of non-biomolecules both at the OoL on Earth or beyond, as well as their potential as agnostic biosignatures indicative of ET Life.

An experimental and numerical model of the behavior of cytosine in aqueous solution under gamma radiation. Relevance in prebiotic chemistry

Cytosine is an essential chemical molecule in living systems, such as DNA and RNA, it is essential in astrobiology to study how it behaves under probable primitive conditions. We looked at how cytosine broke down in aqueous solutions exposed to high radiation levels to learn more about how stable it might have been on the early Earth. We conducted various types of analysis, such as ultraviolet-visible spectroscopy and high-pressure liquid chromatography. We also developed a computer model to describe the kinetic processes and learn more about the molecules involved in the system. This model fits the results of experiments and lets us study cytosine's stability when it is exposed to gamma radiation. It enables researchers to theorize processes that are hard to test in the laboratory and is essential for studying how stable cytosine behaves in high-radiation settings.

Chemical reservoir computation in a self-organizing reaction network

Chemical reaction networks, such as those found in metabolism and signalling pathways, enable cells to process information from their environment1,2. Current approaches to molecular information processing and computation typically pursue digital computation models and require extensive molecular-level engineering3. Despite considerable advances, these approaches have not reached the level of information processing capabilities seen in living systems. Here we report on the discovery and implementation of a chemical reservoir computer based on the formose reaction4. We demonstrate how this complex, self-organizing chemical reaction network can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting. This in chemico information processing system provides proof of principle for the emergent computational capabilities of complex chemical reaction networks, paving the way for a new class of biomimetic information processing systems.

New Insights on the Chemical Origin of Life: The Role of Aqueous Polymerization of N-carboxyanhydrides (NCA)

At the origin, the emergence of proteins was based on crucial prebiotic stages in which simple amino acids-based building blocks spontaneously evolved from the prebiotic soup into random proto-polymers called protoproteins. Despite advances in modern peptide synthesis, these prebiotic chemical routes to protoproteins remain puzzling. We discuss in this perspective how polymer science and systems chemistry are reaching a point of convergence in which simple monomers called N-carboxyanhydrides would be able to form such protoproteins via the emergence of a protometabolic cycle involving aqueous polymerization and featuring macromolecular Darwinism behavior.

Retrosynthesis Zero: Self-Improving Global Synthesis Planning Using Reinforcement Learning

The field of computer-aided synthesis planning (CASP) has witnessed significant growth in recent years. Still, many CASP programs rely on large data sets to train neural networks, resulting in limitations due to the data quality and prior knowledge from chemists. In response, we propose Retrosynthesis Zero (ReSynZ), a reaction template-based method that combines Monte Carlo Tree Search with reinforcement learning inspired by AlphaGo Zero. Unlike other single-step reaction template-based CASP methods, ReSynZ takes complete synthesis paths for complex molecules, determined by reaction rules, as input for training the neural network. ReSynZ enables neural networks trained with relatively small reaction data sets (tens of thousands of data) to generate multiple synthesis pathways for a target molecule and suggest possible reaction conditions. On multiple data sets of molecular retrosynthesis, ReSynZ demonstrates excellent predictive performance compared to existing algorithms. The advantages, such as self-improving model features, flexible reward settings, the potential to surpass human limitations in chemical synthesis route planning, and others, make ReSynZ a valuable tool in chemical synthesis design.

Activation-Induced Senescent Cell Death based on Chiral CoHAu Nanoassemblies with Enantioselective Cascade-Catalytic Ability

Chirality is common in nature, which determines the high enantioselectivity of living systems. Selecting suitable chiral configurations is of great meaning for nanostructures to function better in biological systems. In this study, chiral Co3O4-H2TPPS-Au (CoHAu) nanoassemblies are constructed to accelerate the production ∙OH by consuming D-glucose (D-Glu, widely spread in nature) based on their outstanding enantioselective cascade-catalytic abilities. In particular, D-CoHAu nanoassemblies are more effective in the catalytic conversion of D-Glu than L-CoHAu nanoassemblies. This phenomenon is due to the stronger binding affinity of D-CoHAu nanoassemblies indicated by the lower Km value. Moreover, D-CoHAu nanoassemblies display excellent consumption-ability of D-Glu and production of ∙OH in living cells, which can eliminate senescent cells effectively based on their intracellular enantioselective cascade-catalysis. This research establishes the foundation for bio-mimicking nanostructures with unique functionalities to regulate abnormal biological activities better.

Special Issue "Third Edition: Advances in Molecular Simulation"

Molecular simulation is one of the fastest growing fields in science [...].

Evolution at the Origins of Life?

The role of evolutionary theory at the origin of life is an extensively debated topic. The origin and early development of life is usually separated into a prebiotic phase and a protocellular phase, ultimately leading to the Last Universal Common Ancestor. Most likely, the Last Universal Common Ancestor was subject to Darwinian evolution, but the question remains to what extent Darwinian evolution applies to the prebiotic and protocellular phases. In this review, we reflect on the current status of evolutionary theory in origins of life research by bringing together philosophy of science, evolutionary biology, and empirical research in the origins field. We explore the various ways in which evolutionary theory has been extended beyond biology; we look at how these extensions apply to the prebiotic development of (proto)metabolism; and we investigate how the terminology from evolutionary theory is currently being employed in state-of-the-art origins of life research. In doing so, we identify some of the current obstacles to an evolutionary account of the origins of life, as well as open up new avenues of research.

The First Nucleic Acid Strands May Have Grown on Peptides via Primeval Reverse Translation

The central dogma of molecular biology dictates that, with only a few exceptions, information proceeds from DNA to protein through an RNA intermediate. Examining the enigmatic steps from prebiotic to biological chemistry, we take another road suggesting that primordial peptides acted as template for the self-assembly of the first nucleic acids polymers. Arguing in favour of a sort of archaic "reverse translation" from proteins to RNA, our basic premise is a Hadean Earth where key biomolecules such as amino acids, polypeptides, purines, pyrimidines, nucleosides and nucleotides were available under different prebiotically plausible conditions, including meteorites delivery, shallow ponds and hydrothermal vents scenarios. Supporting a protein-first scenario alternative to the RNA world hypothesis, we propose the primeval occurrence of short two-dimensional peptides termed "selective amino acid- and nucleotide-matching oligopeptides" (henceforward SANMAOs) that noncovalently bind at the same time the polymerized amino acids and the single nucleotides dispersed in the prebiotic milieu. In this theoretical paper, we describe the chemical features of this hypothetical oligopeptide, its biological plausibility and its virtues from an evolutionary perspective. We provide a theoretical example of SANMAO's selective pairing between amino acids and nucleosides, simulating a poly-Glycine peptide that acts as a template to build a purinic chain corresponding to the glycine's extant triplet codon GGG. Further, we discuss how SANMAO might have endorsed the formation of low-fidelity RNA's polymerized strains, well before the appearance of the accurate genetic material's transmission ensured by the current translation apparatus.

The protometabolic nature of prebiotic chemistry

The field of prebiotic chemistry has been dedicated over decades to finding abiotic routes towards the molecular components of life. There is nowadays a handful of prebiotically plausible scenarios that enable the laboratory synthesis of most amino acids, fatty acids, simple sugars, nucleotides and core metabolites of extant living organisms. The major bottleneck then seems to be the self-organization of those building blocks into systems that can self-sustain. The purpose of this tutorial review is having a close look, guided by experimental research, into the main synthetic pathways of prebiotic chemistry, suggesting how they could be wired through common intermediates and catalytic cycles, as well as how recursively changing conditions could help them engage in self-organized and dissipative networks/assemblies (i.e., systems that consume chemical or physical energy from their environment to maintain their internal organization in a dynamic steady state out of equilibrium). In the article we also pay attention to the implications of this view for the emergence of homochirality. The revealed connectivity between those prebiotic routes should constitute the basis for a robust research program towards the bottom-up implementation of protometabolic systems, taken as a central part of the origins-of-life problem. In addition, this approach should foster further exploration of control mechanisms to tame the combinatorial explosion that typically occurs in mixtures of various reactive precursors, thus regulating the functional integration of their respective chemistries into self-sustaining protocellular assemblies.

Single-Photon Ionization Induced New Covalent Bond Formation in Acrylonitrile(AN)-Pyrrole(Py) Clusters

The formation of nitrogen-containing organic compounds is crucial for understanding chemical evolution and the origin of life in the interstellar medium (ISM). In this study, we explore whether acrylonitrile (AN) and pyrrole (Py) can form new nitrogen-containing compounds after single-photon ionization in their gaseous clusters by vacuum ultraviolet (VUV)-infrared (IR) spectroscopy and theoretical calculations. The results show that a strong linear H-bond is formed in neutral AN-Py, while cyclic or bicyclic H-bonded networks are formed in the neutral AN-Py2 cluster. It is found that the structure containing a new C-C covalent bond between two moieties in (AN-Py)+ is formed besides the formation of H-bonded structures after AN-Py is ionized by VUV light. In (AN-Py2)+ cluster cations, new C-C or C-N covalent bonds tend to be formed between two Py, with (Py)2+ as the core in the cluster. The results reveal that new covalent bonds are more likely to be formed between two Py species when AN and Py are present in the cationic clusters. These results provide spectroscopic evidence of the formation of new nitrogen-containing organic compounds from AN and Py induced by VUV, which are helpful for our understanding of the formation of diverse prebiotic molecules in interstellar space.

Synthesis of prebiotic organics from CO2 by catalysis with meteoritic and volcanic particles

The emergence of prebiotic organics was a mandatory step toward the origin of life. The significance of the exogenous delivery versus the in-situ synthesis from atmospheric gases is still under debate. We experimentally demonstrate that iron-rich meteoritic and volcanic particles activate and catalyse the fixation of CO₂, yielding the key precursors of life-building blocks. This catalysis is robust and produces selectively aldehydes, alcohols, and hydrocarbons, independent of the redox state of the environment. It is facilitated by common minerals and tolerates a broad range of the early planetary conditions (150-300 °C, ≲ 10-50 bar, wet or dry climate). We find that up to 6 × 10⁸ kg/year of prebiotic organics could have been synthesized by this planetary-scale process from the atmospheric CO₂ on Hadean Earth.

Multispecies autocatalytic RNA reaction networks in coacervates

Robust localization of self-reproducing autocatalytic chemistries is a key step in the realization of heritable and evolvable chemical systems. While autocatalytic chemical reaction networks already possess attributes such as heritable self-reproduction and evolvability, localizing functional multispecies networks within complex primitive phases, such as coacervates, has remained unexplored. Here, we show the self-reproduction of the Azoarcus ribozyme system within charge-rich coacervates where catalytic ribozymes are produced by the autocatalytic assembly of constituent smaller RNA fragments. We systematically demonstrate the catalytic assembly of active ribozymes within phase-separated coacervates-both in micron-sized droplets as well as in a coalesced macrophase, underscoring the facility of the complex, charge-rich phase to support these reactions in multiple configurations. By constructing multispecies reaction networks, we show that these newly assembled molecules are active, participating both in self- and cross-catalysis within the coacervates. Finally, due to differential molecular transport, these phase-separated compartments endow robustness to the composition of the collectively autocatalytic networks against external perturbations. Altogether, our results establish the formation of multispecies self-reproducing reaction networks in phase-separated compartments which in turn render transient robustness to the network composition.

LinChemIn: SynGraph-a data model and a toolkit to analyze and compare synthetic routes

BACKGROUND

The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction data. On the one side, Computer-Aided Synthesis Planning tools can predict synthetic routes in a model-driven approach; on the other side, experimental routes can be extracted from the Network of Organic Chemistry, in which reaction data are linked in a network. In this context, the need to combine, compare and analyze synthetic routes generated by different sources arises naturally.

RESULTS

Here we present LinChemIn, a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks. Wrapping some third-party packages for handling graph arithmetic and chemoinformatics and implementing new data models and functionalities, LinChemIn allows the interconversion between data formats and data models and enables route-level analysis and operations, including route comparison and descriptors calculation. Object-Oriented Design principles inspire the software architecture, and the modules are structured to maximize code reusability and support code testing and refactoring. The code structure should facilitate external contributions, thus encouraging open and collaborative software development.

CONCLUSIONS

The current version of LinChemIn allows users to combine synthetic routes generated from various tools and analyze them, and constitutes an open and extensible framework capable of incorporating contributions from the community and fostering scientific discussion. Our roadmap envisages the development of sophisticated metrics for routes evaluation, a multi-parameter scoring system, and the implementation of an entire "ecosystem" of functionalities operating on synthetic routes. LinChemIn is freely available at https://github.com/syngenta/linchemin.

Dynamic Environmental Conditions Affect the Composition of a Model Prebiotic Reaction Network

Prebiotic environments are dynamic, containing a range of periodic and aperiodic variations in reaction conditions. However, the impact of the temporal dynamics of environmental conditions upon prebiotic chemical reaction networks has not been investigated. Here, we demonstrate how the magnitude and rate of temporal fluctuations of the catalysts Ca²⁺ and hydroxide control the product distributions of the formose reaction. Surprisingly, the product compositions of the formose reaction under dynamic conditions deviate significantly from those under steady state conditions. We attribute these compositional changes to the non-uniform propagation of fluctuations through the network, thereby shaping reaction outcomes. An examination of temporal concentration patterns showed that collections of compounds responded collectively to perturbations, indicating that key gating reactions branching from the Breslow cycle may be important responsive features of the formose reaction. Our findings show how the compositions of prebiotic reaction networks were shaped by sequential environmental events, illustrating the necessity for considering the temporal traits of prebiotic environments that supported the origin of life.

Thermally Accessible Prebiotic Pathways for Forming Ribonucleic Acid and Protein Precursors from Aqueous Hydrogen Cyanide

The search for prebiotic chemical pathways to biologically relevant molecules is a long-standing puzzle that has generated a menagerie of competing hypotheses with limited experimental prospects for falsification. However, the advent of computational network exploration methodologies has created the opportunity to compare the kinetic plausibility of various channels and even propose new pathways. Here, the space of organic molecules that can be formed within four polar or pericyclic reactions from water and hydrogen cyanide (HCN), two established prebiotic candidates for generating biological precursors, was comprehensively explored with a state-of-the-art exploration algorithm. A surprisingly diverse reactivity landscape was revealed within just a few steps of these simple molecules. Reaction pathways to several biologically relevant molecules were discovered involving lower activation energies and fewer reaction steps compared with recently proposed alternatives. Accounting for water-catalyzed reactions qualitatively affects the interpretation of the network kinetics. The case-study also highlights omissions of simpler and lower barrier reaction pathways to certain products by other algorithms that qualitatively affect the interpretation of HCN reactivity.

Chemical reaction networks and opportunities for machine learning

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

On the evolution of coenzyme biosynthesis

Covering: up to 2022The report provides a broad approach to deciphering the evolution of coenzyme biosynthetic pathways. Here, these various pathways are analyzed with respect to the coenzymes required for this purpose. Coenzymes whose biosynthesis relies on a large number of coenzyme-mediated reactions probably appeared on the scene at a later stage of biological evolution, whereas the biosyntheses of pyridoxal phosphate (PLP) and nicotinamide (NAD+) require little additional coenzymatic support and are therefore most likely very ancient biosynthetic pathways.

Computer-Assisted Design of Sustainable Syntheses of Pharmaceuticals and Agrochemicals from Industrial Wastes

Computer-based strategies vastly enhanced the field of analytical chemistry. The impact of data-driven technologies in shaping organic chemistry strategies long remained comparatively elusive but various tools recently emerged to computationally plan multistep organic syntheses. A recent study elegantly takes benefit of an in-house library of chemical reactions enriched with various metadata to provide numerous, reliable and realistic organic chemistry workflows to structurally-varied drugs of interest, from locally available industrial by-products. The retrieval of the different synthetic pathways and a scoring based on different features, especially comprising sustainability considerations, are also proposed.

Network Analysis Reveals Spatial Clustering and Annotation of Complex Chemical Spaces: Application to Astrochemistry

How are molecules linked to each other in complex systems? In a proof-of-concept study, we have developed the method mol2net (https://zenodo.org/record/7025094) to generate and analyze the molecular network of complex astrochemical data (from high-resolution Orbitrap MS¹ analysis of H₂O:CH₃OH:NH₃ interstellar ice analogs) in a data-driven and unsupervised manner, without any prior knowledge about chemical reactions. The molecular network is clustered according to the initial NH₃ content and unlocked HCN, NH₃, and H₂O as spatially resolved key transformations. In comparison with the PubChem database, four subsets were annotated: (i) saturated C-backbone molecules without N, (ii) saturated N-backbone molecules, (iii) unsaturated C-backbone molecules without N, and (iv) unsaturated N-backbone molecules. These findings were validated with previous results (e.g., identifying the two major graph components as previously described N-poor and N-rich molecular groups) but with additional information about subclustering, key transformations, and molecular structures, and thus, the structural characterization of large complex organic molecules in interstellar ice analogs has been significantly refined.

On the Evolutionary History of the Twenty Encoded Amino Acids

α-Amino acids are essential molecular constituents of life, twenty of which are privileged because they are encoded by the ribosomal machinery. The question remains open as to why this number and why this 20 in particular, an almost philosophical question that cannot be conclusively resolved. They are closely related to the evolution of the genetic code and whether nucleic acids, amino acids, and peptides appeared simultaneously and were available under prebiotic conditions when the first self-sufficient complex molecular system emerged on Earth. This report focuses on prebiotic and metabolic aspects of amino acids and proteins starting with meteorites, followed by their formation, including peptides, under plausible prebiotic conditions, and the major biosynthetic pathways in the various kingdoms of life. Coenzymes play a key role in the present analysis in that amino acid metabolism is linked to glycolysis and different variants of the tricarboxylic acid cycle (TCA, rTCA, and the incomplete horseshoe version) as well as the biosynthesis of the most important coenzymes. Thus, the report opens additional perspectives and facets on the molecular evolution of primary metabolism.

Origins of Life Research: The Conundrum between Laboratory and Field Simulations of Messy Environments

Most experimental results that guide research related to the origin of life are from laboratory simulations of the early Earth conditions. In the laboratory, emphasis is placed on the purity of reagents and carefully controlled conditions, so there is a natural tendency to reject impurities and lack of control. However, life did not originate in laboratory conditions; therefore, we should take into consideration multiple factors that are likely to have contributed to the environmental complexity of the early Earth. This essay describes eight physical and biophysical factors that spontaneously resolve aqueous dispersions of ionic and organic solutes mixed with mineral particles and thereby promote specific chemical reactions required for life to begin.

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed.In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki-Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction.While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers design their products with self-driving laboratories in mind. Challenges in motor function are largely related to handling heterogeneous systems, such as dispensing solids or performing extractions. As a result, it is critical to understand that adapting experimental procedures that were designed for human experimenters is not as simple as transferring those same actions to an automated system, and there may be more efficient ways to achieve the same goal in an automated fashion. Accordingly, for self-driving laboratories, we need to carefully rethink the translation of manual experimental protocols.

A Facile Route for the Formation of Complex Nitrogen-Containing Prebiotic Molecules in the Interstellar Medium

Prebiotic molecules have often been identified in the interstellar medium and meteorite samples. However, we still have only a fragmentary knowledge of the mechanism of the evolutionary process of these prebiotic molecules. With the aid of state-of-the-art vacuum ultraviolet (VUV)-infrared (IR) spectroscopy and ab initio calculations, we reveal a new pathway leading to the formation of the biorelevant molecules carrying amine groups or peptide bonds via the single-photon ionization induced Michael/cyclization reaction of acrylonitrile (AN)-alcohol heterodimer complexes in the gas phase. In the reactions, not only N-H nitrilium cations with H⁺-N≡C-R Lewis structure but also cyclic amine cations with a peptide bond can be formed when the AN reacts with alcohols of increasing molecular size (such as ethanol, propanol, or butanol). This study suggests the possibility of unsaturated nitriles being reduced by ionized alcohols in space, which can further drive sequential Michael addition/cyclization reactions to form more complex biorelevant molecules.

The hierarchical organization of autocatalytic reaction networks and its relevance to the origin of life

Prior work on abiogenesis, the emergence of life from non-life, suggests that it requires chemical reaction networks that contain self-amplifying motifs, namely, autocatalytic cores. However, little is known about how the presence of multiple autocatalytic cores might allow for the gradual accretion of complexity on the path to life. To explore this problem, we develop the concept of a seed-dependent autocatalytic system (SDAS), which is a subnetwork that can autocatalytically self-maintain given a flux of food, but cannot be initiated by food alone. Rather, initiation of SDASs requires the transient introduction of chemical "seeds." We show that, depending on the topological relationship of SDASs in a chemical reaction network, a food-driven system can accrete complexity in a historically contingent manner, governed by rare seeding events. We develop new algorithms for detecting and analyzing SDASs in chemical reaction databases and describe parallels between multi-SDAS networks and biological ecosystems. Applying our algorithms to both an abiotic reaction network and a biochemical one, each driven by a set of simple food chemicals, we detect SDASs that are organized as trophic tiers, of which the higher tier can be seeded by relatively simple chemicals if the lower tier is already activated. This indicates that sequential activation of trophically organized SDASs by seed chemicals that are not much more complex than what already exist could be a mechanism of gradual complexification from relatively simple abiotic reactions to more complex life-like systems. Interestingly, in both reaction networks, higher-tier SDASs include chemicals that might alter emergent features of chemical systems and could serve as early targets of selection. Our analysis provides computational tools for analyzing very large chemical/biochemical reaction networks and suggests new approaches to studying abiogenesis in the lab.

Computational Prediction of Speciation Diagrams and Nucleation Mechanisms: Molecular Vanadium, Niobium, and Tantalum Oxide Nanoclusters in Solution

Understanding the aqueous speciation of molecular metal-oxo-clusters plays a key role in different fields such as catalysis, electrochemistry, nuclear waste recycling, and biochemistry. To describe the speciation accurately, it is essential to elucidate the underlying self-assembly processes. Herein, we apply a computational method to predict the speciation and formation mechanisms of polyoxovanadates, -niobates, and -tantalates. While polyoxovanadates have been widely studied, polyoxoniobates and -tantalates lack the same level of understanding. First, we propose a pentavanadate cluster ([V₅O₁₄]^3-) as a key intermediate for the formation of the decavanadate. Our computed phase speciation diagram is in particularly good agreement with the experiments. Second, we report the formation constants of the heptaniobate, [Nb₇O₂₂]^9-, decaniobate, [Nb₁₀O₂₈]^6-, and tetracosaniobate [H₉Nb₂₄O₇₂]^15-. Additionally, we compute the speciation and phase diagram of niobium, which so far was restricted to Lindqvist derivates. Finally, we predict the formation constant of the decatantalate ([Ta₁₀O₂₆]^6-) in water, even though it had only been synthesized in toluene. Furthermore, we also calculate the corresponding speciation and phase diagrams for polyoxotantalates. Overall, we show that our method can be successfully applied to different families of molecular metal oxides without any need for readjustments; therefore, it can be regarded as a trustworthy tool for exploring polyoxometalates' chemistry.

Fluorescent Microswimmers Based on Cross-β Amyloid Nanotubes and Divergent Cascade Networks

Shaped through millions of years of evolution, the spatial localization of multiple enzymes in living cells employs extensive cascade reactions to enable highly coordinated multimodal functions. Herein, by utilizing a complex divergent cascade, we exploit the catalytic potential as well as templating abilities of streamlined cross-β amyloid nanotubes to yield two orthogonal roles simultaneously. The short peptide based paracrystalline nanotube surfaces demonstrated the generation of fluorescence signals within entangled networks loaded with alcohol dehydrogenase (ADH). The nanotubular morphologies were further used to generate cascade-driven microscopic motility through surface entrapment of sarcosine oxidase (SOX) and catalase (Cat). Moreover, a divergent cascade network was initiated by upstream catalysis of the substrate molecules through the surface mutation of catalytic moieties. Notably, the resultant downstream products led to the generation of motile fluorescent microswimmers by utilizing the two sets of orthogonal properties and, thus, mimicked the complex cascade-mediated functionalities of extant biology.

From the origin of life to pandemics: emergent phenomena in complex systems

When a large number of similar entities interact among each other and with their environment at a low scale, unexpected outcomes at higher spatio-temporal scales might spontaneously arise. This non-trivial phenomenon, known as emergence, characterizes a broad range of distinct complex systems-from physical to biological and social-and is often related to collective behaviour. It is ubiquitous, from non-living entities such as oscillators that under specific conditions synchronize, to living ones, such as birds flocking or fish schooling. Despite the ample phenomenological evidence of the existence of systems' emergent properties, central theoretical questions to the study of emergence remain unanswered, such as the lack of a widely accepted, rigorous definition of the phenomenon or the identification of the essential physical conditions that favour emergence. We offer here a general overview of the phenomenon of emergence and sketch current and future challenges on the topic. Our short review also serves as an introduction to the theme issue Emergent phenomena in complex physical and socio-technical systems: from cells to societies, where we provide a synthesis of the contents tackled in the issue and outline how they relate to these challenges, spanning from current advances in our understanding on the origin of life to the large-scale propagation of infectious diseases. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Small-molecule autocatalytic networks are universal metabolic fossils

Life and the genetic code are self-referential and so are autocatalytic networks made of simpler, small molecules. Several origins of life theories postulate autocatalytic chemical networks preceding the primordial genetic code, yet demonstration with biochemical systems is lacking. Here, small-molecule reflexively autocatalytic food-generated networks (RAFs) ranging in size from 3 to 619 reactions were found in all of 6683 prokaryotic metabolic networks searched. The average maximum RAF size is 275 reactions for a rich organic medium and 93 for a medium with a single organic cofactor, NAD. In the rich medium, all universally essential metabolites are produced with the exception of glycerol-1-p (archaeal lipid precursor), phenylalanine, histidine and arginine. The 300 most common reactions, present in at least 2732 RAFs, are mostly involved in amino acid biosynthesis and the metabolism of carbon, 2-oxocarboxylic acid and purines. ATP and NAD are central in generating network complexity, and because ATP is also one of the monomers of RNA, autocatalytic networks producing redox and energy currencies are a strong candidate niche of the origin of a primordial information-processing system. The wide distribution of small-molecule autocatalytic networks indicates that molecular reproduction may be much more prevalent in the Universe than hitherto predicted. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Environmental conditions drive self-organization of reaction pathways in a prebiotic reaction network

The evolution of life from the prebiotic environment required a gradual process of chemical evolution towards greater molecular complexity. Elaborate prebiotically relevant synthetic routes to the building blocks of life have been established. However, it is still unclear how functional chemical systems evolved with direction using only the interaction between inherent molecular chemical reactivity and the abiotic environment. Here we demonstrate how complex systems of chemical reactions exhibit well-defined self-organization in response to varying environmental conditions. This self-organization allows the compositional complexity of the reaction products to be controlled as a function of factors such as feedstock and catalyst availability. We observe how Breslow's cycle contributes to the reaction composition by feeding C₂ building blocks into the network, alongside reaction pathways dominated by formaldehyde-driven chain growth. The emergence of organized systems of chemical reactions in response to changes in the environment offers a potential mechanism for a chemical evolution process that bridges the gap between prebiotic chemical building blocks and the origin of life.

An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions

A central question in origins of life research is how non-entailed chemical processes, which simply dissipate chemical energy because they can do so due to immediate reaction kinetics and thermodynamics, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamically favorable processes enhanced some processes over others. Some degree of molecular complexity likely had to be supplied by environmental processes to produce entailed self-replicating processes. The origin of entailment, therefore, must connect to fundamental chemistry that builds molecular complexity. We present here an open-source chemoinformatic workflow to model abiological chemistry to discover such entailment. This pipeline automates generation of chemical reaction networks and their analysis to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system by vetting it against experimental data. This workflow can enable rapid identification of products of complex chemistries and their underlying synthetic relationships to help identify autocatalysis, and potentially self-organization, in such systems. The algorithms used in this study are open-source and reconfigurable by other user-developed workflows.

Computer-designed repurposing of chemical wastes into drugs

As the chemical industry continues to produce considerable quantities of waste chemicals^1,2, it is essential to devise 'circular chemistry'^3-8 schemes to productively back-convert at least a portion of these unwanted materials into useful products. Despite substantial progress in the degradation of some classes of harmful chemicals⁹, work on 'closing the circle'-transforming waste substrates into valuable products-remains fragmented and focused on well known areas^10-15. Comprehensive analyses of which valuable products are synthesizable from diverse chemical wastes are difficult because even small sets of waste substrates can, within few steps, generate millions of putative products, each synthesizable by multiple routes forming densely connected networks. Tracing all such syntheses and selecting those that also meet criteria of process and 'green' chemistries is, arguably, beyond the cognition of human chemists. Here we show how computers equipped with broad synthetic knowledge can help address this challenge. Using the forward-synthesis Allchemy platform¹⁶, we generate giant synthetic networks emanating from approximately 200 waste chemicals recycled on commercial scales, retrieve from these networks tens of thousands of routes leading to approximately 300 important drugs and agrochemicals, and algorithmically rank these syntheses according to the accepted metrics of sustainable chemistry^17-19. Several of these routes we validate by experiment, including an industrially realistic demonstration on a 'pharmacy on demand' flow-chemistry platform²⁰. Wide adoption of computerized waste-to-valuable algorithms can accelerate productive reuse of chemicals that would otherwise incur storage or disposal costs, or even pose environmental hazards.

Peptides before and during the nucleotide world: an origins story emphasizing cooperation between proteins and nucleic acids

Recent developments in Origins of Life research have focused on substantiating the narrative of an abiotic emergence of nucleic acids from organic molecules of low molecular weight, a paradigm that typically sidelines the roles of peptides. Nevertheless, the simple synthesis of amino acids, the facile nature of their activation and condensation, their ability to recognize metals and cofactors and their remarkable capacity to self-assemble make peptides (and their analogues) favourable candidates for one of the earliest functional polymers. In this mini-review, we explore the ramifications of this hypothesis. Diverse lines of research in molecular biology, bioinformatics, geochemistry, biophysics and astrobiology provide clues about the progression and early evolution of proteins, and lend credence to the idea that early peptides served many central prebiotic roles before they were encodable by a polynucleotide template, in a putative 'peptide-polynucleotide stage'. For example, early peptides and mini-proteins could have served as catalysts, compartments and structural hubs. In sum, we shed light on the role of early peptides and small proteins before and during the nucleotide world, in which nascent life fully grasped the potential of primordial proteins, and which has left an imprint on the idiosyncratic properties of extant proteins.

Dynamic Exchange of Substituents in a Prebiotic Organocatalyst: Initial Steps towards an Evolutionary System

All evolutionary biological processes lead to a change in heritable traits over successive generations. The responsible genetic information encoded in DNA is altered, selected, and inherited by mutation of the base sequence. While this is well known at the biological level, an evolutionary change at the molecular level of small organic molecules is unknown but represents an important prerequisite for the emergence of life. Here, we present a class of prebiotic imidazolidine-4-thione organocatalysts able to dynamically change their constitution and potentially capable to form an evolutionary system. These catalysts functionalize their building blocks and dynamically adapt to their (self-modified) environment by mutation of their own structure. Depending on the surrounding conditions, they show pronounced and opposing selectivity in their formation. Remarkably, the preferentially formed species can be associated with different catalytic properties, which enable multiple pathways for the transition from abiotic matter to functional biomolecules.

Application of computational approach in plastic pyrolysis kinetic modelling: a review

During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.

The Prebiotic Kitchen: A Guide to Composing Prebiotic Soup Recipes to Test Origins of Life Hypotheses

“Prebiotic soup” often features in discussions of origins of life research, both as a theoretical concept when discussing abiological pathways to modern biochemical building blocks and, more recently, as a feedstock in prebiotic chemistry experiments focused on discovering emergent, systems-level processes such as polymerization, encapsulation, and evolution. However, until now, little systematic analysis has gone into the design of well-justified prebiotic mixtures, which are needed to facilitate experimental replicability and comparison among researchers. This paper explores principles that should be considered in choosing chemical mixtures for prebiotic chemistry experiments by reviewing the natural environmental conditions that might have created such mixtures and then suggests reasonable guidelines for designing recipes. We discuss both “assembled” mixtures, which are made by mixing reagent grade chemicals, and “synthesized” mixtures, which are generated directly from diversity-generating primary prebiotic syntheses. We discuss different practical concerns including how to navigate the tremendous uncertainty in the chemistry of the early Earth and how to balance the desire for using prebiotically realistic mixtures with experimental tractability and replicability. Examples of two assembled mixtures, one based on materials likely delivered by carbonaceous meteorites and one based on spark discharge synthesis, are presented to illustrate these challenges. We explore alternative procedures for making synthesized mixtures using recursive chemical reaction systems whose outputs attempt to mimic atmospheric and geochemical synthesis. Other experimental conditions such as pH and ionic strength are also considered. We argue that developing a handful of standardized prebiotic recipes may facilitate coordination among researchers and enable the identification of the most promising mechanisms by which complex prebiotic mixtures were “tamed” during the origin of life to give rise to key living processes such as self-propagation, information processing, and adaptive evolution. We end by advocating for the development of a public prebiotic chemistry database containing experimental methods (including soup recipes), results, and analytical pipelines for analyzing complex prebiotic mixtures.

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.