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Antibacterial peptides are researched mainly for the potential benefit they have in a variety of socially relevant diseases, used by the host to protect itself from different types of pathogenic bacteria. We used the mathematical-computational method known as Hidden Markov models (HMMs) in targeting a subset of antibacterial peptides named Selective Cationic Amphipatic Antibacterial Peptides (SCAAPs). The main difference in the implementation of HMMs was focused on the detection of SCAAP using principally five physical-chemical properties for each candidate SCAAPs, instead of using the statistical information about the amino acids which form a peptide. By this method a cluster of antibacterial peptides was detected and as a result the following were found: 9 SCAAPs, 6 synthetic antibacterial peptides that belong to a subregion of Cecropin A and Magainin 2, and 19 peptides from the Cecropin A family. A scoring function was developed using HMMs as its core, uniquely employing information accessible from the databases.
 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.
 Antimicrobial peptides occupy a prominent place in the production of pharmaceuticals, because of their effective contribution to the protection of the immune system against almost all types of pathogens. These peptides are thoroughly studied by computational methods designed to shed light on their main functions. In this paper, we propose a computational approach, named the Polarity Profile method that represents an improvement to the former Polarity Index method. The Polarity Profile method is very effective in detecting the subgroup of antibacterial peptides called selective cationic amphipathic antibacterial peptides (SCAAP) that show high toxicity towards bacterial membranes and exhibit almost zero toxicity towards mammalian cells. Our study was restricted to the peptides listed in the antimicrobial peptides database (APD2) of December 19, 2012. Performance of the Polarity Profile method is demonstrated through a comparison to the former Polarity Index method by using the same sets of peptides. The efficiency of the Polarity Profile method exceeds 85% taking into account the false positive and/or false negative peptides.
 In accordance with the second law of thermodynamics, the Universe as a whole tends to higher entropy. However, the sequence of far-from-equilibrium events that led to the emergence of life on Earth could have imposed order and complexity during the course of chemical reactions in the so-called primordial soup of life. Hence, we may expect to find characteristic profiles or biases in the prebiotic product mixtures, as for instance among the first amino acids. Seeking to shed light on this hypothesis, we have designed a high performance computer program that simulates the spontaneous formation of the amino acid monomers in closed environments. The program was designed in reference to a prebiotic scenario proposed by Sydney W. Fox. The amino acid abundances and their polarities as the two principal biases were also taken into consideration. We regarded the computational model as exhaustive since 200 000 amino acid dimers were formed by simulation, subsequently expressed in a vector and compared with the corresponding amino acid dimers that were experimentally obtained by Fox. We found a very high similarity between the experimental results and our simulations.
Selective antibacterial peptides containing less than 30 amino acid residues, cationic, with amphipathic properties, have been the subject of several studies due to their active participation and beneficial effects in strengthening the immune system of all living organisms. This manuscript reports the results of a comparison between the group of selective antibacterial peptides and another group called "cell penetrating peptides". An important number of the selective antibacterial peptides are cell penetrating peptides, suggesting that their toxicity is related to their uptake mechanism. The verification of this observation also includes the adaptation of a method previously published, called Polarity index, which reproduces and confirms the action of this new set of peptides. The efficiency of this method was verified based on four different databases, yielding a high score. The verification was based exclusively on the peptides already reported in the databases which have been experimentally verified.
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