Foldamer hypothesis for the growth and sequence differentiation of prebiotic polymers

Prime mass amino acids: A new numbers based classification of significance to mass spectrometry and protein biology

The nominal mass of amino acid residues calculated from their elemental compositions are defined by prime numbers far more often than chance, and such residues appear to play an important role in the formation and biology of proteins. It is proposed therefore that consideration be given to classifying prime mass amino acids as such, beyond the more common, familiar definitions associated with the other physicochemical properties of amino acids including charged or non-charged, hydrophobic or hydrophilic, polar or non-polar, acidic or basic, aliphatic or aromatic. Greater focus could also be given to such residues during peptide and protein sequencing with mass spectrometry and the construction of structural maps, given their predominantly hydrophobic character and thus their role in protein folding and transmembrane domains. The use of prime numbers to define amino acids based on the sum of the atomic masses from their elemental compositions invokes other recent interest and observations whereby prime numbers were organised in a way that mirrors electrons arranged within the orbitals of an atom. The article links number theory with both the physical and biological sciences, and mass spectrometry, for the first time.

On the role of α-alumina in the origin of life: Surface-driven assembly of amino acids

We investigate the hypothesis that mineral/water interfaces played a crucial catalytic role in peptide formation by promoting the self-assembly of amino acids. Using classical molecular dynamics simulations, we demonstrate that the α-alumina(0001) surface exhibits an affinity of 4 kBT for individual glycine or GG dipeptide molecules due to hydrogen bonds. In simulations with multiple glycine molecules, surface-bound glycine enhances further adsorption, leading to the formation of long chains connected by hydrogen bonds between the carboxyl and amine groups of glycine molecules. We find that the likelihood of observing chains longer than 10 glycine units increases by at least five orders of magnitude at the surface compared to the bulk. This surface-driven assembly is primarily due to local high density and alignment with the alumina surface pattern. Together, these results propose a model for how mineral surfaces can induce configuration-specific assembly of amino acids, thereby promoting condensation reactions.

WITHDRAWN: Structural Enzymology, Phylogenetics, Differentiation, and Symbolic Reflexivity at the Dawn of Biology

This manuscript was posted without the final consent of all authors. The authors have therefore withdrawn it. The authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author, carter@med.unc.edu .

A High-Throughput Workflow to Analyze Sequence-Conformation Relationships and Explore Hydrophobic Patterning in Disordered Peptoids

Understanding how a macromolecule's primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using MALDI-MS/MS, and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic N-butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations. Conformational distributions of three compositionally identical library sequences were corroborated through atomistic simulations and ion mobility spectrometry coupled with liquid chromatography. A data-driven strategy was developed using existing sequence variables and data-derived 'motifs' to inform a machine learning algorithm towards conformation prediction. This multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery of materials with conformational control.

Origins of Life: The Protein Folding Problem all over again?

How did specific useful protein sequences arise from simpler molecules at the origin of life? This seemingly needle-in-a-haystack problem has remarkably close resemblance to the old Protein Folding Problem, for which the solution is now known from statistical physics. Based on the logic that Origins must have come only after there was an operative evolution mechanism-which selects on phenotype, not genotype-we give a perspective that proteins and their folding processes are likely to have been the primary driver of the early stages of the origin of life.

Insights into conformational ensembles of compositionally identical disordered peptidomimetics

While the conformational ensembles of disordered peptides and peptidomimetics are complex and challenging to characterize, they are a critical component in the paradigm connecting macromolecule sequence, structure, and function. In molecules that do not adopt a single predominant conformation, the conformational ensemble contains rich structural information that, if accessible, can provide a fundamental understanding related to desirable functions such as cell penetration of a therapeutic or the generation of tunable enzyme-mimetic architecture. To address the fundamental challenge of describing broad conformational ensembles, we developed a model system of peptidomimetics comprised of polar glycine and hydrophobic N-butylglycine to characterize using a suite of analytical techniques. Using replica exchange molecular dynamics atomistic simulations and liquid chromatography coupled to ion mobility spectrometry, we were able to distinguish the conformations of compositionally identical model sequences. However, differences between these model sequences were more challenging to resolve with characterization tools developed for intrinsically disordered proteins and polymers, including double electron-electron resonance (DEER) spectroscopy and diffusion ordered spectroscopy (DOSY) NMR. Finally, we introduce a facile colorimetric assay using immobilized sequences that leverages a solvatochromic probe, Reichardt's dye, to visually reveal conformational trends consistent with the experimental and computational analysis. This rapid colorimetric technique provides a complementary method to characterize the disorder of macromolecules and examine conformational ensembles as an isolated or multiplexed technique.

Enhancement of waterborne pathogen removal by functionalized biochar with ε-polylysine ″dynamic arms″: Potential application in ultrafiltration system

Widespread outbreaks of threatening infections caused by unknown pathogens and water transmission have spawned the development of adsorption methods for pathogen elimination. We proposed a biochar functionalization strategy involving ε-polylysine (PLL), a bio-macromolecular poly(amino acid)s with variable folding conformations, as a "pathogen gripper" on biochar. PLL was successfully bridged onto biochar via polydopamine (PDA) crosslinking. The extension of electropositive side chains within PLL enables the capture of both nanoscale viruses and micrometer-scale bacteria in water, achieving excellent removal performances. This functionalized biochar was tentatively incorporated into ultrafiltration (UF) system, to achieve effective and controllable adsorption and retention of pathogens, and to realize the transfer of pathogens from membrane surface/pore to biochar surface as well as flushing water. The biochar-amended UF systems presents complete retention (∼7 LRV) and hydraulic elution of pathogens into membrane flushing water. Improvements in removal of organics and anti-fouling capability were observed, indicating the broken trade-off in UF pathogen removal dependent on irreversible fouling. Chemical characterizations revealed adsorption mechanisms encompassing electrostatic/hydrophobic interactions, pore filling, electron transfer, chemical bonding and secondary structure transitions. Microscopic and mechanical analyses validated the mechanisms for rapid adsorption and pathogen lysis. Low-concentration alkaline solution for used biochar regeneration, facilitated the deprotonation and transformation of PLL side chain to folded structures (α-helix/β-sheet). Biochar regeneration process also promoted the effective detachment/inactivation of pathogens and protection of functional groups on biochar, corroborated by physicochemical inspection and molecular dynamics simulation. The foldability of poly(amino acid)s acting like dynamic arms, significantly contributed to pathogen capture/desorption/inactivation and biochar regeneration. This study also inspires future investigation for performances of UF systems amended by poly(amino acid)s-functionalized biochar under diverse pressure, temperature, reactive oxygen species of feeds and chemical cleaning solutions, with far-reaching implications for public health, environmental applications of biochar, and UF process improvement.

The prebiotic emergence of biological evolution

The origin of life must have been preceded by Darwin-like evolutionary dynamics that could propagate it. How did that adaptive dynamics arise? And from what prebiotic molecules? Using evolutionary invasion analysis, we develop a universal framework for describing any origin story for evolutionary dynamics. We find that cooperative autocatalysts, i.e. autocatalysts whose per-unit reproductive rate grows as their population increases, have the special property of being able to cross a barrier that separates their initial degradation-dominated state from a growth-dominated state with evolutionary dynamics. For some model parameters, this leap to persistent propagation is likely, not rare. We apply this analysis to the Foldcat Mechanism, wherein peptides fold and help catalyse the elongation of each other. Foldcats are found to have cooperative autocatalysis and be capable of emergent evolutionary dynamics.

Mapping Composition Evolution through Synthesis, Purification, and Depolymerization of Random Heteropolymers

Random heteropolymers (RHPs) consisting of three or more comonomers have been routinely used to synthesize functional materials. While increasing the monomer variety diversifies the side-chain chemistry, this substantially expands the sequence space and leads to ensemble-level sequence heterogeneity. Most studies have relied on monomer composition and simulated sequences to design RHPs, but the questions remain unanswered regarding heterogeneities within each RHP ensemble and how closely these simulated sequences reflect the experimental outcomes. Here, we quantitatively mapped out the evolution of monomer compositions in four-monomer-based RHPs throughout a design-synthesis-purification-depolymerization process. By adopting a Jaacks method, we first determined 12 reactivity ratios directly from quaternary methacrylate RAFT copolymerization experiments to account for the influences of competitive monomer addition and the reversible activation/deactivation equilibria. The reliability of in silico analysis was affirmed by a quantitative agreement (<4% difference) between the simulated RHP compositions and the experimental results. Furthermore, we mapped out the conformation distribution within each ensemble in different solvents as a function of monomer chemistry, composition, and segmental characteristics via high-throughput computation based on self-consistent field theory (SCFT). These comprehensive studies confirmed monomer composition as a viable design parameter to engineer RHP-based functional materials as long as the reactivity ratios are accurately determined and the livingness of RHP synthesis is ensured.

The intrinsically disordered protein glue of the myelin major dense line: Linking AlphaFold2 predictions to experimental data

Numerous human proteins are classified as intrinsically disordered proteins (IDPs). Due to their physicochemical properties, high-resolution structural information about IDPs is generally lacking. On the other hand, IDPs are known to adopt local ordered structures upon interactions with e.g. other proteins or lipid membrane surfaces. While recent developments in protein structure prediction have been revolutionary, their impact on IDP research at high resolution remains limited. We took a specific example of two myelin-specific IDPs, the myelin basic protein (MBP) and the cytoplasmic domain of myelin protein zero (P0ct). Both of these IDPs are crucial for normal nervous system development and function, and while they are disordered in solution, upon membrane binding, they partially fold into helices, being embedded into the lipid membrane. We carried out AlphaFold2 predictions of both proteins and analysed the models in light of experimental data related to protein structure and molecular interactions. We observe that the predicted models have helical segments that closely correspond to the membrane-binding sites on both proteins. We furthermore analyse the fits of the models to synchrotron-based X-ray scattering and circular dichroism data from the same IDPs. The models are likely to represent the membrane-bound state of both MBP and P0ct, rather than the conformation in solution. Artificial intelligence-based models of IDPs appear to provide information on the ligand-bound state of these proteins, instead of the conformers dominating free in solution. We further discuss the implications of the predictions for mammalian nervous system myelination and their relevance to understanding disease aspects of these IDPs.

Light-Fueled Primitive Replication and Selection in Biomimetic Chemical Systems

The concept of chemically evolvable replicators is central to abiogenesis. Chemical evolvability requires three essential components: energy-harvesting mechanisms for nonequilibrium dissipation, kinetically asymmetric replication and decomposition pathways, and structure-dependent selective templating in the autocatalytic cycles. We observed a UVA light-fueled chemical system displaying sequence-dependent replication and replicator decomposition. The system was constructed with primitive peptidic foldamer components. The photocatalytic formation-recombination cycle of thiyl radicals was coupled with the molecular recognition steps in the replication cycles. Thiyl radical-mediated chain reaction was responsible for the replicator death mechanism. The competing and kinetically asymmetric replication and decomposition processes led to light intensity-dependent selection far from equilibrium. Here, we show that this system can dynamically adapt to energy influx and seeding. The results highlight that mimicking chemical evolution is feasible with primitive building blocks and simple chemical reactions.

Coding From Binding? Molecular Interactions at the Heart of Translation

The mechanism and the evolution of DNA replication and transcription, the key elements of the central dogma of biology, are fundamentally well explained by the physicochemical complementarity between strands of nucleic acids. However, the determinants that have shaped the third part of the dogma-the process of biological translation and the universal genetic code-remain unclear. We review and seek parallels between different proposals that view the evolution of translation through the prism of weak, noncovalent interactions between biological macromolecules. In particular, we focus on a recent proposal that there exists a hitherto unrecognized complementarity at the heart of biology, that between messenger RNA coding regions and the proteins that they encode, especially if the two are unstructured. Reflecting the idea that the genetic code evolved from intrinsic binding propensities between nucleotides and amino acids, this proposal promises to forge a link between the distant past and the present of biological systems.

Origins of life: first came evolutionary dynamics

When life arose from prebiotic molecules 3.5 billion years ago, what came first? Informational molecules (RNA, DNA), functional ones (proteins), or something else? We argue here for a different logic: rather than seeking a molecule type, we seek a dynamical process. Biology required an ability to evolve before it could choose and optimise materials. We hypothesise that the evolution process was rooted in the peptide folding process. Modelling shows how short random peptides can collapse in water and catalyse the elongation of others, powering both increased folding stability and emergent autocatalysis through a disorder-to-order process.

The Bootstrap Model of Prebiotic Networks of Proteins and Nucleic Acids

It is not known how life arose from prebiotic physical chemistry. How did fruitful cell-like associations emerge from the two polymer types—informational (nucleic acids, xNAs = DNA or RNA) and functional (proteins)? Our model shows how functional networks could bootstrap from random sequence-independent initial states. For proteins, we adopt the foldamer hypothesis: through persistent nonequilibrium prebiotic syntheses, short random peptides fold and catalyze the elongation of others. The xNAs enter through random binding to the peptides, and all chains can mutate. Chains grow inside colloids that split when they’re large, coupling faster growth speeds to bigger populations. Random and useless at first, these folding and binding events grow protein—xNA networks that resemble today’s protein–protein networks.

Improving coarse-grained models of protein folding through weighting of polar-polar/hydrophobic-hydrophobic interactions into crowded spaces

Herein were tested 7 hydrophobic-polar sequences in two types of 2D-square space lattices, homogeneous and correlated, the latter simulating molecular crowding included as a geometric boundary restriction. Optimization of 2D structures was carried out using a variant of Dill's model, inspired by convex function, taking into account both hydrophobic (Dill's model) and polar interactions, including more structural information to reach better folding solutions. While using correlated networks, degrees of freedom in the folding of sequences were limited; as a result in all cases, more successful structural trials were found in comparison to a homogeneous lattice. The majority of employed sequences were designed by our workgroup, two of them were folded with other approaches, and another is a modified version of a previous sequence, initial forms of the other two have been employed but without taking into account polar-polar contributions. Three of them are newly proposed, intended to test the conjoint hydrophobic-hydrophobic and polar-polar contributions in crowded spaces. One sequence turned out to be the most difficult of the seven folded, this perhaps due to intrinsic (i) degrees of freedom and (ii) motifs of the expected 2D HP structure. Meanwhile two-sequence, although optimal folding was not achieved for neither of the two approaches, folding with correlated network approach not only produced better results than homogeneous space, but for them the best values found with crowding were very close to the expected optimal fitness. In general, five sequences were better folded with medium lattice units for correlated media; instead, another two sequences were better folded with a bit larger degree of lattice unit, revealing that depending on the degrees of freedom and particular folding, motifs in each sequence would require tuned crowding to achieve better folding. Therefore, the main goal herein was to obtain a modified 2D HP lattice model to mimic folding of proteins or secondary structures, like β-sheets, taking into account both hydrophobic-hydrophobic and polar-polar interactions, and fold them in a crowded environment. This simple but enough construction would be conducted to determine the needed information to fold sequences in a sort of a minimal but complete heuristic model. Finally, we claim that all folded sequences into crowded spaces achieve better results than homogeneous ones.

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics-polymeric configuration characterization, feed-forward property prediction, and inverse design-in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications.

The Protein Folding Problem: The Role of Theory

The protein folding problem was first articulated as question of how order arose from disorder in proteins: How did the various native structures of proteins arise from interatomic driving forces encoded within their amino acid sequences, and how did they fold so fast? These matters have now been largely resolved by theory and statistical mechanics combined with experiments. There are general principles. Chain randomness is overcome by solvation-based codes. And in the needle-in-a-haystack metaphor, native states are found efficiently because protein haystacks (conformational ensembles) are funnel-shaped. Order-disorder theory has now grown to encompass a large swath of protein physical science across biology.

Proliferating coacervate droplets as the missing link between chemistry and biology in the origins of life

The hypothesis that prebiotic molecules were transformed into polymers that evolved into proliferating molecular assemblages and eventually a primitive cell was first proposed about 100 years ago. To the best of our knowledge, however, no model of a proliferating prebiotic system has yet been realised because different conditions are required for polymer generation and self-assembly. In this study, we identify conditions suitable for concurrent peptide generation and self-assembly, and we show how a proliferating peptide-based droplet could be created by using synthesised amino acid thioesters as prebiotic monomers. Oligopeptides generated from the monomers spontaneously formed droplets through liquid-liquid phase separation in water. The droplets underwent a steady growth-division cycle by periodic addition of monomers through autocatalytic self-reproduction. Heterogeneous enrichment of RNA and lipids within droplets enabled RNA to protect the droplet from dissolution by lipids. These results provide experimental constructs for origins-of-life research and open up directions in the development of peptide-based materials.

The Way forward for the Origin of Life: Prions and Prion-Like Molecules First Hypothesis

In this paper the hypothesis that prions and prion-like molecules could have initiated the chemical evolutionary process which led to the eventual emergence of life is reappraised. The prions first hypothesis is a specific application of the protein-first hypothesis which asserts that protein-based chemical evolution preceded the evolution of genetic encoding processes. This genetics-first hypothesis asserts that an “RNA-world era” came before protein-based chemical evolution and rests on a singular premise that molecules such as RNA, acetyl-CoA, and NAD are relics of a long line of chemical evolutionary processes preceding the Last Universal Common Ancestor (LUCA). Nevertheless, we assert that prions and prion-like molecules may also be relics of chemical evolutionary processes preceding LUCA. To support this assertion is the observation that prions and prion-like molecules are involved in a plethora of activities in contemporary biology in both complex (eukaryotes) and primitive life forms. Furthermore, a literature survey reveals that small RNA virus genomes harbor information about prions (and amyloids). If, as has been presumed by proponents of the genetics-first hypotheses, small viruses were present during an RNA world era and were involved in some of the earliest evolutionary processes, this places prions and prion-like molecules potentially at the heart of the chemical evolutionary process whose eventual outcome was life. We deliberate on the case for prions and prion-like molecules as the frontier molecules at the dawn of evolution of living systems.

Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure

Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

Nanoscale Catalyst Chemotaxis Can Drive the Assembly of Functional Pathways

Recent experiments demonstrate molecular chemotaxis or altered diffusion rates of enzymes in the presence of their own substrates. We show here an important implication, namely, that if a nanoscale catalyst A produces a small-molecule ligand product L which is the substrate of another catalyst B, the two catalysts will attract each other. We explore this nonequilibrium producer recruitment force (ProRec) in a reaction-diffusion model. The predicted cat-cat association will be the strongest when A is a fast producer of L and B is a tight binder to it. ProRec is a force that could drive a mechanism (the catpath mechanism) by which catalysts could become spatially localized into functional pathways, such as in the biochemical networks in cells, which can achieve complex goals.

The Sequence of a Step-Growth Copolymer Can Be Influenced by Its Own Persistence Length

Synthetic copolymer sequences remain challenging to control, and there are features of even simple one-pot, solution-based copolymerizations that are not yet fully understood. In previous simulations on step-growth copolymerizations in solution, we demonstrated that modest variations in the attractions between type A and B monomers could significantly influence copolymer sequence through an emergent aggregation and phase separation initiated by the lengthening of nascent oligomers. Here we investigate how one aspect of a copolymer's geometry-its flexibility-can modulate those effects. Our simulations show the onset of strand alignment within the polymerization-induced aggregates as chain stiffness increases and demonstrate that this alignment can influence the resulting copolymer sequences. For less flexible copolymers, with persistence lengths ≥10 monomers, modest nonbonded attractions of ∼k_BT between monomers of the same type yield A and B blocks of a characteristic length and result in a polydispersity index that grows rapidly, peaks, and then diminishes as the reaction proceeds. These results demonstrate that for copolymer systems with modest variations in intermonomer attractions and physically realistic flexibilities a nascent copolymer's persistence length can influence its own sequence.

Self-Reproduction and Darwinian Evolution in Autocatalytic Chemical Reaction Systems

Understanding the emergence of life from (primitive) abiotic components has arguably been one of the deepest and yet one of the most elusive scientific questions. Notwithstanding the lack of a clear definition for a living system, it is widely argued that heredity (involving self-reproduction) along with compartmentalization and metabolism are key features that contrast living systems from their non-living counterparts. A minimal living system may be viewed as "a self-sustaining chemical system capable of Darwinian evolution". It has been proposed that autocatalytic sets of chemical reactions (ACSs) could serve as a mechanism to establish chemical compositional identity, heritable self-reproduction, and evolution in a minimal chemical system. Following years of theoretical work, autocatalytic chemical systems have been constructed experimentally using a wide variety of substrates, and most studies, thus far, have focused on the demonstration of chemical self-reproduction under specific conditions. While several recent experimental studies have raised the possibility of carrying out some aspects of experimental evolution using autocatalytic reaction networks, there remain many open challenges. In this review, we start by evaluating theoretical studies of ACSs specifically with a view to establish the conditions required for such chemical systems to exhibit self-reproduction and Darwinian evolution. Then, we follow with an extensive overview of experimental ACS systems and use the theoretically established conditions to critically evaluate these empirical systems for their potential to exhibit Darwinian evolution. We identify various technical and conceptual challenges limiting experimental progress and, finally, conclude with some remarks about open questions.

Driving forces in the origins of life

What were the physico-chemical forces that drove the origins of life? We discuss four major prebiotic 'discoveries': persistent sampling of chemical reaction space; sequence-encodable foldable catalysts; assembly of functional pathways; and encapsulation and heritability. We describe how a 'proteins-first' world gives plausible mechanisms. We note the importance of hydrophobic and polar compositions of matter in these advances.

Incorporating antagonistic pleiotropy into models for molecular replicators

In modern cells, chromosomal genes composed of DNA encode multi-subunit protein/RNA complexes that catalyze the replication of the chromosome and cell. One prevailing theory for the origin of life posits an early stage involving self-replicating macromolecules called replicators, which can be considered genes capable of self-replication. One prevailing theory for the genetics of aging in humans and other organisms is antagonistic pleiotropy, which posits that a gene can be beneficial in one context, and detrimental in another context. We previously reported that the conceptual simplicity of molecular replicators facilitates the generation of two simple models involving antagonistic pleiotropy. Here a third model is proposed, and each of the three models is presented with improved definition of the time variable. Computer simulations were used to calculate the proliferation of a hypothetical two-subunit replicator (AB), when one of the two subunits (B) exhibits antagonistic pleiotropy, leading to an advantage for B to be unstable. In model 1, instability of B yields free A subunits, which in turn stimulate the activity of other AB replicators. In model 2, B is lost and sometimes replaced by a more active mutant form, B'. In model 3, B becomes damaged and loses activity, and its instability allows it to be replaced by a new B. For each model, conditions were identified where instability of B was detrimental, and where instability of B was beneficial. The results are consistent with the hypothesis that antagonistic pleiotropy can promote molecular instability and system complexity, and provide further support for a model linking aging and evolution.

Primordial emergence of a nucleic acid-binding protein via phase separation and statistical ornithine-to-arginine conversion

De novo emergence demands a transition from disordered polypeptides into structured proteins with well-defined functions. However, can polypeptides confer functions of evolutionary relevance, and how might such polypeptides evolve into modern proteins? The earliest proteins present an even greater challenge, as they were likely based on abiotic, spontaneously synthesized amino acids. Here we asked whether a primordial function, such as nucleic acid binding, could emerge with ornithine, a basic amino acid that forms abiotically yet is absent in modern-day proteins. We combined ancestral sequence reconstruction and empiric deconstruction to unravel a gradual evolutionary trajectory leading from a polypeptide to a ubiquitous nucleic acid-binding protein. Intermediates along this trajectory comprise sequence-duplicated functional proteins built from 10 amino acid types, with ornithine as the only basic amino acid. Ornithine side chains were further modified into arginine by an abiotic chemical reaction, improving both structure and function. Along this trajectory, function evolved from phase separation with RNA (coacervates) to avid and specific double-stranded DNA binding. Our results suggest that phase-separating polypeptides may have been an evolutionary resource for the emergence of early proteins, and that ornithine, together with its postsynthesis modification to arginine, could have been the earliest basic amino acids.

On the possible origin of protein homochirality, structure, and biochemical function

Living systems have chiral molecules, e.g., native proteins that almost entirely contain L-amino acids. How protein homochirality emerged from a background of equal numbers of L and D amino acids is among many questions about life's origin. The origin of homochirality and its implications are explored in computer simulations examining the stability and structural and functional properties of an artificial library of compact proteins containing 1:1 (termed demi-chiral), 3:1, and 1:3 ratios of D:L and purely L or D amino acids generated without functional selection. Demi-chiral proteins have shorter secondary structures and fewer internal hydrogen bonds and are less stable than homochiral proteins. Selection for hydrogen bonding yields a preponderance of L or D amino acids. Demi-chiral proteins have native global folds, including similarity to early ribosomal proteins, similar small molecule ligand binding pocket geometries, and many constellations of L-chiral amino acids with a 1.0-Å RMSD to native enzyme active sites. For a representative subset containing 550 active site geometries matching 457 (2) 4-digit (3-digit) enzyme classification (E.C.) numbers, native active site amino acids were generated at random for 472 of 550 cases. This increases to 548 of 550 cases when similar residues are allowed. The most frequently generated sequences correspond to ancient enzymatic functions, e.g., glycolysis, replication, and nucleotide biosynthesis. Surprisingly, even without selection, demi-chiral proteins possess the requisite marginal biochemical function and structure of modern proteins, but were thermodynamically less stable. If demi-chiral proteins were present, they could engage in early metabolism, which created the feedback loop for transcription and cell formation.

The difficult case of an RNA-only origin of life

The RNA world hypothesis is probably the most extensively studied model for the emergence of life on Earth. Despite a large body of evidence supporting the idea that RNA is capable of kick-starting autocatalytic self-replication and thus initiating the emergence of life, seemingly insurmountable weaknesses in the theory have also been highlighted. These problems could be overcome by novel experimental approaches, including out-of-equilibrium environments, and the exploration of an early co-evolution of RNA and other key biomolecules such as peptides and DNA, which might be necessary to mitigate the shortcomings of RNA-only systems.

From prebiotic chemistry to supramolecular oligomers: urea-glyoxal reactions

A fundamental question in origin-of-life studies and astrochemistry concerns the actual processes that initiate the formation of reactive monomers and their oligomerization. Answers lie partly in the accurate description of reaction mechanisms compatible with environments plausible on early Earth as well as cosmological scenarios in planetary factories. Here we show in detail that reactions of urea-as archetypal prebiotic substance-and reactive carbonyls-exemplified by glyoxal-lead to a vast repertoire of oligomers, in which different five- and six-membered non-aromatic heterocycles self-assemble and insert into chains or dendritic-like structures with masses up to 1000 Da. Such regular patterns have been interpreted by experimental and computational methods. A salient conclusion is that such processes most likely occur through SN-type mechanisms on hydrated or protonated species. Remarkably, such supramolecular oligomeric mixtures can be easily isolated from organic solvents, thus opening the door to the generation of novel urea-containing polymers with potential applications in materials chemistry and beyond.

Protein ensembles link genotype to phenotype

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

In silico minimalist approach to study 2D HP protein folding into an inhomogeneous space mimicking osmolyte effect: First trial in the search of foldameric backbones

We have employed our bioinformatics workbench, named Evolution, a Multi-Agent System based architecture with lattice-bead-models, evolutionary-algorithms, and correlated-networks as inhomogeneous spaces, with different correlation lengths, mimicking osmolyte effect (molecular crowding), to in silico survey protein folding. Resolution is with hydrophobic-polar (H-P) sequences in inhomogeneous 2D square lattices, since general biophysicochemical trends consider i) that the backbone is one of the major components responsible for protein folding and ii) osmolyte effect plays an important role to better folding kinetics and reach deeper optima. We have designed foldamers, as square n × n (n = 3, 4, 5, 6) arrays of hydrophobic cores stabilized by H⋯H contacts, attached through short PP (P₂) or long PPPP (P₄) loops, giving rise to 8 sequences (S₁ to S₈) with known optimal scores. Designed sequences were folded into different inhomogeneous spaces and indeed crowded media induced deeper optima, being crowding necessary to best fold, but the space should be enough constrained to induce folding without banning chain movement. The constrained space plays an important role to reach the optimal structure, depending on designed foldamer sequence size, for an optimal correlation length, implying that media affects the folding pathways as happens in real systems. Designed structures were found, moreover, they undergo to degenerated states, both folding states could survey considering i) backbone information and ii) osmolyte effect. In nature, the proteins fold in different structures aiming to reach a global minimum, but a local minimum could be enough to the protein to be functional or dysfunctional.

Prebiotic Oligomer Assembly: What Was the Energy Source?

Polymerization of nucleotides and amino acids to form large, complex, and potentially functional products was an early and essential event on the paths leading to life's origin. The standard Gibbs energies of the condensation reactions are uphill, however, and at equilibrium will yield only declining sequences of small, nonfunctional oligomers. Geochemically produced condensing agents such as carbonyl sulfide, cyanamide, and polyphosphates have been proposed to invert the unfavorable condensation Gibbs energies and thereby activate exergonic condensation. We argue, however, that although activators may provide modest yields of oligomers, the inherently episodic nature of their sources throttles their effectiveness, and the fundamental hydrolytic instabilities of oligonucleotides and peptides ultimately prevail to yield decreasing product sequences. Notably, the Gibbs energy governing oligomer formation is antientropic. Accordingly, we propose that declining progression can be surmounted in evaporating pools in which a favorable entropy change is produced when high surface/volume ratios concentrate reactants at the air/water interface in continuous cycles of wetting and drying. The severely reduced configurational freedom of the solutes then inverts the antientropic nature of the condensation reactions, pivoting them to exergonic states and thus to the production of ascending sequences of complex polymeric products.

Intrinsically Disordered Proteins and the Janus Challenge

To gain a new insight into the role of proteins in the origin of life on Earth, we present the Janus Challenge: identify an intrinsically disordered protein (IDP), naturally occurring or synthetic, that has catalytic activity. For example, such a catalytic IDP may perform condensation reactions to catalyze a peptide bond or a phosphodiester bond formation utilizing natural/un-natural amino acids or nucleotides, respectively. The IDP may also have autocatalytic, de novo synthesis, or self-replicative activity. Meeting this challenge may not only shed new light and provide an alternative to the RNA world hypothesis, but it may also serve as an impetus for technological advances with important biomedical applications.

Kinetic Selection of Template Polymer with Complex Sequences

The emergence and maintenance of polymers with complex sequences pose a major question in the study of the origin of life. To answer this, we study a model polymerization reaction, where polymers are synthesized by stepwise ligation from two types of monomers, catalyzed by a long polymer as a template. Direct stochastic simulation and dynamical systems analysis reveal that the most dominant polymer sequence in a population successively changes, depending on the flow rate of monomers to the system, with more complex sequences selected at a lower flow rate. We discuss the relevance of this kinetic sequence selection through nonequilibrium flow to the origin of complex polymers.

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.