Characterization of Selective Antibacterial Peptides by Polarity Index

Polanco, Carlos; Samaniego, José Lino; Buhse, Thomas; Mosqueira, F. G.; Negrón‐Mendoza, A.; Ramos-Bernal, S.; Castañón-González, Jorge Alberto

doi:10.1155/2012/585027

Cited by 18 publications

(54 citation statements)

References 53 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…Since the present work shows the identification of antifungi peptides from an already-published method [7], we indicate only the changes that this method requires for such identification (see Supplementary Material).…”

Section: Methodsmentioning

confidence: 99%

“…Replacing the Q[i,j] matrix in the source program [7] by the Table 1, which represent the incidences of antifungal sequences with a unique pathogenic action. Table 1 considers 88 antifungal peptides extracted from the APD2 database [12].…”

Section: Polarity Index Method Updatesmentioning

confidence: 99%

“…(ii). Replacing the rule in the source program [7] by P[i,j] ? Q[i,j] vector complying with the next rule: the polar interaction 11 is present in any of the first nine positions, polar interaction 16 is not present in the first position, polar interaction 14 is not present in any of the first five positions, polar interaction 12 is not present in the 1st or 4th positions, polar interaction 13 is not present in the 1st, 2nd, 5th, 15th, or 16th positions, polar interaction 6 is not present in the 13th position, and polar interaction 3 is not present from 12th to 16th positions ( …”

Section: Polarity Index Method Updatesmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Polar Characterization of Antifungal Peptides from APD2 Database

Polanco

Samaniego-Mendoza

Buhse

et al. 2014

Cell Biochem Biophys

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The increase in the number of pathogens due to fungi that are tolerant to therapies does not grow at the same speed than the advance on new antifungal drugs. In this sense, it is imperative to find anti-fungi peptides that are not detrimental to mammalian cells and have an effective toxicity to fungi. In this work, we use a method called polarity index, to identify anti-fungi peptides with an efficiency of 70 %. This method already published, initially identified selective antibacterial peptides from APD2 Database, and was characterized by developing a comprehensive analysis of the polar dynamics of a peptide from its linear sequence. Discriminating tests showed that in addition to being efficient in this identification, it was also good at rejecting other classifications of peptides found in that same database.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Polarity Index Method Updatesmentioning

confidence: 99%

Section: Polarity Index Method Updatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Polar Characterization of Antifungal Peptides from APD2 Database

Polanco

Samaniego-Mendoza

Buhse

et al. 2014

Cell Biochem Biophys

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Polar Profile of Antiviral Peptides from AVPpred Database

Polanco

Samaniego

Castañón-González

et al. 2014

Cell Biochem Biophys

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Diseases of viral origin in humans are among the most serious threats to health and the global economy. As recent history has shown the virus has a high pandemic potential, among other reasons, due to its ability to spread by air, hence the identification, investigation, containment, and treatment of viral diseases should be considered of paramount importance. In this sense, the bioinformatics research has focused on finding fast and efficient algorithms that can identify highly toxic antiviral peptides and to serve as a first filter, so that trials in the laboratory are substantially reduced. The work presented here contributes to this effort through the use of an algorithm already published by this team, called polarity index method, which identifies with high efficiency antiviral peptides from the exhaustive analysis of the polar profile, using the linear sequence of the peptide. The test carried out included all peptides in APD2 Database and 60 antiviral peptides identified by Kumar and co-workers (Nucleic Acids Res 40:W199-204, 2012), to build its AVPpred algorithm. The validity of the method was focused on its discriminating capacity so we included the 15 sub-classifications of both Databases.

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A novel designed membrane-active peptide for the control of foodborne Salmonella enterica serovar Typhimurium

Sengkhui

Klubthawee

Aunpad

2023

Sci Rep

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The main cause of non-typhoidal Salmonella (NTS) infection in humans is ingestion of contaminated animal-derived foods such as eggs, poultry and dairy products. These infections highlight the need to develop new preservatives to increase food safety. Antimicrobial peptides (AMPs) have the potential to be further developed as food preservative agents and join nisin, the only AMP currently approved, for use as a preservative in food. Acidocin J1132β, a bacteriocin produced by probiotic Lactobacillus acidophilus, displays no toxicity to humans, however it exhibits only low and narrow-spectrum antimicrobial activity. Accordingly, four peptide derivatives (A5, A6, A9, and A11) were modified from acidocin J1132β by truncation and amino acid substitution. Among them, A11 showed the most antimicrobial activity, especially against S. Typhimurium, as well as a favorable safety profile. It tended to form an α-helix structure upon encountering negatively charged-mimicking environments. A11 caused transient membrane permeabilization and killed bacterial cells through membrane depolarization and/or intracellular interactions with bacterial DNA. A11 maintained most of its inhibitory effects when heated, even when exposed to temperatures up to 100 °C. Notably, it inhibited drug-resistant S. Typhimurium and its monophasic variant strains. Furthermore, the combination of A11 and nisin was synergistic against drug-resistant strains in vitro. Taken together, this study indicated that a novel antimicrobial peptide derivative (A11), modified from acidocin J1132β, has the potential to be a bio-preservative to control S. Typhimurium contamination in the food industry.

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Characterization of Selective Antibacterial Peptides by Polarity Index

Cited by 18 publications

References 53 publications

Polar Characterization of Antifungal Peptides from APD2 Database

Polar Characterization of Antifungal Peptides from APD2 Database

Polar Profile of Antiviral Peptides from AVPpred Database

A novel designed membrane-active peptide for the control of foodborne Salmonella enterica serovar Typhimurium

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