Prebiotically Plausible Autocatalytic Peptide Amyloids

Binding of Precursors to Replicator Assemblies Can Improve Replication Fidelity and Mediate Error Correction

Copying information is vital for life's propagation. Current life forms maintain a low error rate in replication, using complex machinery to prevent and correct errors. However, primitive life had to deal with higher error rates, limiting its ability to evolve. Discovering mechanisms to reduce errors would alleviate this constraint. Here, we introduce a new mechanism that decreases error rates and corrects errors in synthetic self-replicating systems driven by self-assembly. Previous work showed that macrocycle replication occurs through the accumulation of precursor material on the sides of the fibrous replicator assemblies. Stochastic simulations now reveal that selective precursor binding to the fiber surface enhances replication fidelity and error correction. Centrifugation experiments show that replicator fibers can exhibit the necessary selectivity in precursor binding. Our results suggest that synthetic replicator systems are more evolvable than previously thought, encouraging further evolution-focused experiments.

Short Peptide Amyloids Are a Potential Sequence Pool for the Emergence of Proteins

Under prebiotic conditions, peptides are capable of self-replication through a structure-based template-assisted mechanism when they form amyloids. Furthermore, peptide amyloids can spontaneously form inside fatty acid vesicles creating membrane enclosed complex structures of variable morphologies. This is possible because fatty acid vesicle membranes act as filters allowing passage of activated amino acids while some amino acids derived from the activated species become non-permeable and trapped in the vesicles. Similarly, nascent peptides derived from the condensation of the activated amino acids are also trapped in the vesicles. It is hypothesized that such preselected peptide amyloids become a sequence pool for the emergence of proteins in life and that after billions of years of cellular evolution, the sequences in the current proteome have diverged significantly from these original seed peptides. If this hypothesis is correct, it could be possible to detect the traces of these seed sequences in current proteomes. Here, we show for all possible 3, 6, 7, 8 or 9 residue sequence motifs that those motifs that are most amyloidogenic/aggregation prone are over-represented in extant proteomes compared to a sequence-randomized proteome. Furthermore, we find that there is a greater proportion of amyloidogenic sequence motifs in archaea proteomes than in the larger primate proteomes. This suggests that the evolution towards larger proteomes leads to smaller proportion of amyloidogenic sequences.

Mechanisms and pathology of protein misfolding and aggregation

Despite advances in machine learning-based protein structure prediction, we are still far from fully understanding how proteins fold into their native conformation. The conventional notion that polypeptides fold spontaneously to their biologically active states has gradually been replaced by our understanding that cellular protein folding often requires context-dependent guidance from molecular chaperones in order to avoid misfolding. Misfolded proteins can aggregate into larger structures, such as amyloid fibrils, which perpetuate the misfolding process, creating a self-reinforcing cascade. A surge in amyloid fibril structures has deepened our comprehension of how a single polypeptide sequence can exhibit multiple amyloid conformations, known as polymorphism. The assembly of these polymorphs is not a random process but is influenced by the specific conditions and tissues in which they originate. This observation suggests that, similar to the folding of native proteins, the kinetics of pathological amyloid assembly are modulated by interactions specific to cells and tissues. Here, we review the current understanding of how intrinsic protein conformational propensities are modulated by physiological and pathological interactions in the cell to shape protein misfolding and aggregation pathology.

Kinetic Modeling and Parameter Estimation of a Prebiotic Peptide Reaction Network

Although our understanding of how life emerged on Earth from simple organic precursors is speculative, early precursors likely included amino acids. The polymerization of amino acids into peptides and interactions between peptides are of interest because peptides and proteins participate in complex interaction networks in extant biology. However, peptide reaction networks can be challenging to study because of the potential for multiple species and systems-level interactions between species. We developed and employed a computational network model to describe reactions between amino acids to form di-, tri-, and tetra-peptides. Our experiments were initiated with two of the simplest amino acids, glycine and alanine, mediated by trimetaphosphate-activation and drying to promote peptide bond formation. The parameter estimates for bond formation and hydrolysis reactions in the system were found to be poorly constrained due to a network property known as sloppiness. In a sloppy model, the behavior mostly depends on only a subset of parameter combinations, but there is no straightforward way to determine which parameters should be included or excluded. Despite our inability to determine the exact values of specific kinetic parameters, we could make reasonably accurate predictions of model behavior. In short, our modeling has highlighted challenges and opportunities toward understanding the behaviors of complex prebiotic chemical experiments.

Setting the geological scene for the origin of life and continuing open questions about its emergence

The origin of life is one of the most fundamental questions of humanity. It has been and is still being addressed by a wide range of researchers from different fields, with different approaches and ideas as to how it came about. What is still incomplete is constrained information about the environment and the conditions reigning on the Hadean Earth, particularly on the inorganic ingredients available, and the stability and longevity of the various environments suggested as locations for the emergence of life, as well as on the kinetics and rates of the prebiotic steps leading to life. This contribution reviews our current understanding of the geological scene in which life originated on Earth, zooming in specifically on details regarding the environments and timescales available for prebiotic reactions, with the aim of providing experimenters with more specific constraints. Having set the scene, we evoke the still open questions about the origin of life: did life start organically or in mineralogical form? If organically, what was the origin of the organic constituents of life? What came first, metabolism or replication? What was the time-scale for the emergence of life? We conclude that the way forward for prebiotic chemistry is an approach merging geology and chemistry, i.e., far-from-equilibrium, wet-dry cycling (either subaerial exposure or dehydration through chelation to mineral surfaces) of organic reactions occurring repeatedly and iteratively at mineral surfaces under hydrothermal-like conditions.

Out-of-Equilibrium Self-Replication Allows Selection for Dynamic Kinetic Stability in a System of Competing Replicators

Among the key characteristics of living systems are their ability to self‐replicate and the fact that they exist in an open system away from equilibrium. Herein, we show how the outcome of the competition between two self‐replicators, differing in size and building block composition, is different depending on whether the experiments are conducted in a closed vial or in an open and out‐of‐equilibrium replication–destruction regime. In the closed system, the slower replicator eventually prevails over the faster competitor. In a replication‐destruction regime, implemented through a flow system, the outcome of the competition is reversed and the faster replicator dominates. The interpretation of the experimental observations is supported by a mass‐action‐kinetics model. These results represent one of the few experimental manifestations of selection among competing self‐replicators based on dynamic kinetic stability and pave the way towards Darwinian evolution of abiotic systems.