A toy model of prebiotic peptide evolution: the possible role of relative amino acid abundances.

Abstract

This paper presents a mathematical-computational toy model based on the assumed dynamic principles of prebiotic peptide evolution. Starting from a pool of amino acid monomers, the model describes in a generalized manner the generation of peptides and their sequential information. The model integrates the intrinsic and dynamic key elements of the initiation of biopolymerization, such as the relative amino acid abundances and polarities, as well as the oligomer reversibility, i.e. fragmentation and recombination, and peptide self-replication. Our modeling results suggest that the relative amino acid abundances, as indicated by Miller-Urey type electric discharge experiments, played a principal role in the early sequential information of peptide profiles. Moreover, the computed profiles display an astonishing similarity to peptide profiles observed in so-called biological common ancestors found in the following three microorganisms; E. coli, M. jannaschii, and S. cereviasiae. The prebiotic peptide fingerprint was obtained by the so-called polarity index method that was earlier reported as a tool for the identification of cationic amphipathic antibacterial short peptides.

Polar Profile of Antiviral Peptides from AVPpred Database

“…However, the simulation of the three-dimensional structure of the peptide is very complex, without taking into account other factors involved such as: The dynamics of the membrane and toxicity. In this work, we use the Polarity index method [14], already published by our team to identify SCAAP [15][16][17] and which only uses the linear peptide sequence to identify the same group of antiviral peptides that were identified by AVPpred algorithm [2].…”

Polar Characterization of Antifungal Peptides from APD2 Database

“…The method has already been used for the efficient identification of SCAAP [7] and prebiotic analysis [11] due to its robustness; however, we consider the method is not efficient enough identifying the most toxic anti-fungi peptides, which means we have to improve its search profile. With this in mind, we have sub-classified the groups located in APD2 database [12] from 9 to 14 groups ( [7] Table 3), and in this sense we make sure the method remains exclusive.…”

“…The algorithm named Polarity index method [7] was previously published to effectively identify SCAAP and other groups of peptides [7][8][9][10][11] in the APD2 Database (http://aps.unmc.edu/AP/ accessed December 19, 2012) [12]. In this paper, we present the results of the same method identifying anti-fungi peptides.…”

Discrete dynamic system oriented on the formation of prebiotic dipeptides from Rode's experiment.

“…The present work focuses on the possible prebiotic peptide profile formed 4 billion years ago by using the information of the Rode profile through computational simulation and by comparing this profile with our former studies (Polanco et al, 2013;Polanco et al, 2013a) on the Miller-type generation of amino acid monomers (Miller, 1953) as well as with the experiments by Fox & Harada (1960) on the generation of the so-called "proteinoids". In particular, we simulated in three computational scenarios the hypothetical peptide building (i) resulting from the Miller experiments on the lightning-induced amino acid generation by using the experimentally observed monomer abundances, (ii) considering the initial conditions of the Fox & Harada experiments as well as (iii) reproducing the Rode profile taking into account the starting mixtures of the Rode experiments.…”