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Key Physicochemical Determinants in the Antimicrobial Peptide RiLK1 Promote Amphipathic Structures.

ABSTRACT: Antimicrobial peptides (AMPs) represent a skilled class of new antibiotics, due to their broad range of activity, rapid killing, and low bacterial resistance. Many efforts have been made to discover AMPs with improved performances, i.e., high antimicrobial activity, low cytotoxicity against human cells, stability against proteolytic degradation, and low costs of production. In the design of new AMPs, several physicochemical features, such as hydrophobicity, net positive charge, propensity to assume amphipathic conformation, and self-assembling properties, must be considered. Starting from the sequence of the dodecapeptide 1018-K6, we designed a new 10-aminoacid peptide, namely RiLK1, which is highly effective against both fungi and Gram-positive and -negative bacteria at low micromolar concentrations without causing human cell cytotoxicity. In order to find the structural reasons explaining the improved performance of RiLK1 versus 1018-K6, a comparative analysis of the two peptides was carried out with a combination of CD, NMR, and fluorescence spectroscopies, while their self-assembling properties were analyzed by optical and atomic force microscopies. Interestingly, the different spectroscopic and microscopic profiles exhibited by the two peptides, including the propensity of RiLK1 to adopt helix arrangements in contrast to 1018-K6, could explain the improved bactericidal, antifungal, and anti-biofilm activities shown by the new peptide against a panel of food pathogens.

SUBMITTER: Falcigno L

PROVIDER: S-EPMC8472000 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Key Physicochemical Determinants in the Antimicrobial Peptide RiLK1 Promote Amphipathic Structures.

Falcigno Lucia L D'Auria Gabriella G Palmieri Gianna G Gogliettino Marta M Agrillo Bruna B Tatè Rosarita R Dardano Principia P Nicolais Luigi L Balestrieri Marco M

International journal of molecular sciences 20210916 18

Antimicrobial peptides (AMPs) represent a skilled class of new antibiotics, due to their broad range of activity, rapid killing, and low bacterial resistance. Many efforts have been made to discover AMPs with improved performances, i.e., high antimicrobial activity, low cytotoxicity against human cells, stability against proteolytic degradation, and low costs of production. In the design of new AMPs, several physicochemical features, such as hydrophobicity, net positive charge, propensity to ass ...[more]

PMID: 34576174

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Project description:The evolution of bonded restorations has undergone great progress over several decades. Nonetheless, life spans of bonded restorations are limited mainly because of the eventual incidence of recurrent caries. Over time, water and waterborne agents (acids, enzymes) degrade the components of the dentin/restoration interface, allowing bacterial colonization and dentin reinfection at the margins of the restoration. We developed a 2-tier protective technology consisting of priming/coating dentin with amphipathic and antimicrobial peptides (AAMPs) to obtain hydrophobic/water-repellent and antibiofilm dentin-resisting recurrent caries around bonded restorations. We tested a series of AAMPs to assess their structure-function relationships as well as the effects of different dentin-conditioning methods on the structural features of AAMP-coated dentin. We found relation between the secondary structure of AAMPs (high portion of β-sheet), the antimicrobial potency of AAMPs, and the AAMPs' ability to form hydrophobic coatings on dentin. We also determined that AAMPs had preferential adsorption on the mineral phase of dentin, which suggested that peptides arrange their cationic and hydrophilic motifs in direct contact with the negatively charged minerals in the hydrophilic dentin. These results led us to explore different dentin-conditioning methods that would increase the mineral/collagen ratio and their effect on AAMP immobilization. We innovatively imaged the spatial distribution of the AAMPs in relation to the dentinal tubules and collagen network using a minimally invasive multimodal imaging technique: multiphoton-second harmonic generation. Using multiphoton-second harmonic generation imaging, we determined that partial deproteinization of dentin increased the amount of immobilized AAMPs as compared with the total etched dentin at the dentin surface and extended deeply around dentinal tubules. Last, we analyzed the release rate of AAMPs from dentin coatings in artificial saliva to predict their stability in the clinical setting. In conclusion, priming dentin with AAMPs is a versatile new approach with potential to fortify the otherwise vulnerable adhesive-based interfaces.

Project description:BackgroundAntimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.ResultsBy generating explicit boundaries, our method defines new categories of active and inactive peptides based on their physicochemical properties. Consequently, it describes physicochemical characteristics of similarity among active peptides and the physicochemical boundaries between active and inactive peptides in a single process. To build the similarity boundaries, we used the rough set theory approach; to our knowledge, this is the first time that this approach has been used to classify peptides. The modified rough set theory method limits the number of values describing a boundary to a user-defined limit. Our method is optimized for specificity over selectivity. Noting that false positives increase activity assays while false negatives only increase computational search time, our method provided a low false discovery rate. Published datasets were used to compare our rough set theory method to other published classification methods and based on this comparison, we achieved high selectivity and comparable sensitivity to currently available methods.ConclusionsWe developed rule sets that define physicochemical boundaries which allow us to directly classify the active sequences from inactive peptides. Existing classification methods are either sequence-order insensitive or length-dependent, whereas our method generates the rule sets that combine order-sensitive descriptors with length-independent descriptors. The method provides comparable or improved performance to currently available methods. Discovering the boundaries of physicochemical properties may lead to a new understanding of peptide similarity.

Dataset Information

Key Physicochemical Determinants in the Antimicrobial Peptide RiLK1 Promote Amphipathic Structures.

Publications

Key Physicochemical Determinants in the Antimicrobial Peptide RiLK1 Promote Amphipathic Structures.

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