Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries

Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.

Degradation and Failure Phenomena at the Dentin Bonding Interface

Damage in the bonding interface is a significant factor that leads to premature failure of dental bonded restorations. The imperfectly bonded dentin-adhesive interface is susceptible to hydrolytic degradation and bacterial and enzyme attack, severely jeopardizing restorations' longevity. Developing caries around previously made restorations, also called "recurrent or secondary caries," is a significant health problem. The replacement of restorations is the most prevailing treatment in dental clinics, leading to the so-called "tooth death spiral". In other words, every time a restoration is replaced, more tooth tissue is removed, increasing the size of the restorations until the tooth is eventually lost. This process leads to high financial costs and detriment to patients' quality of life. Since the complexity of the oral cavity makes prevention a challenging task, novel strategies in Dental Materials and Operative fields are required. This article briefly overviews the physiological dentin substrate, features of dentin bonding, challenges and clinical relevance. We discussed the anatomy of the dental bonding interface, aspects of the degradation at the resin-dentin interface, extrinsic and intrinsic factors affecting dental bonding longevity, perspectives on resin and collagen degradation and how these subjects are connected. In this narrative review, we also outlined the recent progress in overcoming dental bonding challenges through bioinspiration, nanotechnology and advanced techniques to reduce degradation and improve dental bonding longevity.

Antimicrobial Peptides Therapy: An Emerging Alternative for Treating Drug-Resistant Bacteria

Microbial resistance to antibiotics is an ancient and dynamic issue that has brought a situation reminiscent of the pre-antibiotic era to the limelight. Currently, antibiotic resistance and the associated infections are widespread and pose significant global health and economic burden. Thus, the misuse of antibiotics, which has increased resistance, has necessitated the search for alternative therapeutic agents for combating resistant pathogens. Antimicrobial peptides (AMPs) hold promise as a viable therapeutic approach against drug-resistant pathogens. AMPs are oligopeptides with low molecular weight. They have broad-spectrum antimicrobial activities against pathogenic microorganisms. AMPs are nonspecific and target components of microbes that facilitate immune response by acting as the first-line defense mechanisms against invading pathogenic microbes. The diversity and potency of AMPs make them good candidates for alternative use. They could be used alone or in combination with several other biomaterials for improved therapeutic activity. They can also be employed in vaccine production targeting drug-resistant pathogens. This review covers the opportunities and advances in AMP discovery and development targeting antimicrobial resistance (AMR) bacteria. Briefly, it presents an overview of the global burden of the antimicrobial resistance crisis, portraying the global magnitude, challenges, and consequences. After that, it critically and comprehensively evaluates the potential roles of AMPs in addressing the AMR crisis, highlighting the major potentials and prospects.

Engineering peptide-polymer hybrids for targeted repair and protection of cervical lesions

By 2060, nearly 100 million people in the U.S. will be over age 65 years. One-third of these older adults will have root caries, and nearly 80% will have dental erosion. These conditions can cause pain and loss of tooth structure that interfere with eating, speaking, sleeping, and quality of life. Current treatments for root caries and dental erosion have produced unreliable results. For example, the glass-ionomer-cement or composite-resin restorations used to treat these lesions have annual failure rates of 44% and 17%, respectively. These limitations and the pressing need to treat these conditions in the aging population are driving a focus on microinvasive strategies, such as sealants and varnishes. Sealants can inhibit caries on coronal surfaces, but they are ineffective for root caries. For healthy, functionally independent elders, chlorhexidine varnish applied every 3 months inhibits root caries, but this bitter-tasting varnish stains the teeth. Fluoride gel inhibits root caries, but requires prescriptions and daily use, which may not be feasible for some older patients. Silver diamine fluoride can both arrest and inhibit root caries but stains the treated tooth surface black. The limitations of current approaches and high prevalence of root caries and dental erosion in the aging population create an urgent need for microinvasive therapies that can: (a) remineralize damaged dentin; (b) inhibit bacterial activity; and (c) provide durable protection for the root surface. Since cavitated and non-cavitated root lesions are difficult to distinguish, optimal approaches will treat both. This review will explore the multi-factorial elements that contribute to root surface lesions and discuss a multi-pronged strategy to both repair and protect root surfaces. The strategy integrates engineered peptides, novel polymer chemistry, multi-scale structure/property characterization and predictive modeling to develop a durable, microinvasive treatment for root surface lesions.

Emerging Computational Approaches for Antimicrobial Peptide Discovery

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

Toward insights on antimicrobial selectivity of host defense peptides via machine learning model interpretation

Host defense peptides are promising candidates for the development of novel antibiotics. To realize their therapeutic potential, high levels of target selectivity is essential. This study aims to identify factors governing selectivity via the use of the random forest algorithm for correlating peptide sequence information with their bioactivity data. Satisfactory predictive models were achieved from out-of-bag prediction that yielded accuracies and Matthew's correlation coefficients in excess of 0.80 and 0.57, respectively. Model interpretation through the use of variable importance metrics and partial dependence plots indicated that the selectivity was heavily influenced by the composition and distribution patterns of molecular charge and solubility related parameters. Furthermore, the three investigated bacterial target species (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) likely had a significant influence on how selectivity was realized as there appears to be a similar underlying selectivity mechanism on the basis of charge-solubility properties (i.e. but which is tailored according to the target in question).

AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides

Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid sequence and its anti-inflammatory property is of great importance for the discovery of new AIEs. To address this issue, we propose a novel prediction tool for accurate identification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. Most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. At the same time, we exploit several feature extraction models and utilize one feature selection model, in order to construct many base classifiers from various feature representations. More specifically, we develop an effective classification model, with which we can extract and learn a set of informative features from the ensemble classifier chain model with different group of base classifiers. Furthermore, in order to test the predictive power of our model, we conduct the comparative experiments on the leave-one-out cross-validation and the independent test. It shows that our novel predictor performs great accurate for identification of AIEs as well as existing outstanding prediction tools. Source codes are available at https://github.com/guofei-tju/Ensemble-classifier-chain-model.

Reconfigurable Dual Peptide Tethered Polymer System Offers a Synergistic Solution for Next Generation Dental Adhesives

Resin-based composite materials have been widely used in restorative dental materials due to their aesthetic, mechanical, and physical properties. However, they still encounter clinical shortcomings mainly due to recurrent decay that develops at the composite-tooth interface. The low-viscosity adhesive that bonds the composite to the tooth is intended to seal this interface, but the adhesive seal is inherently defective and readily damaged by acids, enzymes, and oral fluids. Bacteria infiltrate the resulting gaps at the composite-tooth interface and bacterial by-products demineralize the tooth and erode the adhesive. These activities lead to wider and deeper gaps that provide an ideal environment for bacteria to proliferate. This complex degradation process mediated by several biological and environmental factors damages the tooth, destroys the adhesive seal, and ultimately, leads to failure of the composite restoration. This paper describes a co-tethered dual peptide-polymer system to address composite-tooth interface vulnerability. The adhesive system incorporates an antimicrobial peptide to inhibit bacterial attack and a hydroxyapatite-binding peptide to promote remineralization of damaged tooth structure. A designer spacer sequence was incorporated into each peptide sequence to not only provide a conjugation site for methacrylate (MA) monomer but also to retain active peptide conformations and enhance the display of the peptides in the material. The resulting MA-antimicrobial peptides and MA-remineralization peptides were copolymerized into dental adhesives formulations. The results on the adhesive system composed of co-tethered peptides demonstrated both strong metabolic inhibition of S. mutans and localized calcium phosphate remineralization. Overall, the result offers a reconfigurable and tunable peptide-polymer hybrid system as next-generation adhesives to address composite-tooth interface vulnerability.

Antimicrobial Peptides Derived From Insects Offer a Novel Therapeutic Option to Combat Biofilm: A Review

Biofilms form a complex layer with defined structures, that attach on biotic or abiotic surfaces, are tough to eradicate and tend to cause some resistance against most antibiotics. Several studies confirmed that biofilm-producing bacteria exhibit higher resistance compared to the planktonic form of the same species. Antibiotic resistance factors are well understood in planktonic bacteria which is not so in case of biofilm producing forms. This may be due to the lack of available drugs with known resistance mechanisms for biofilms. Existing antibiotics cannot eradicate most biofilms, especially of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species). Insects produce complex and diverse set of chemicals for survival and defense. Antimicrobial peptides (AMPs), produced by most insects, generally have a broad spectrum of activity and the potential to bypass the resistance mechanisms of classical antibiotics. Besides, AMPs may well act synergistically with classical antibiotics for a double-pronged attack on infections. Thus, AMPs could be promising alternatives to overcome medically important biofilms, decrease the possibility of acquired resistance and treatment of multidrug-resistant pathogens including ESKAPE. The present review focuses on insect-derived AMPs with special reference to anti-biofilm-based strategies. It covers the AMP composition, pathways and mechanisms of action, the formation of biofilms, impact of biofilms on human diseases, current strategies as well as therapeutic options to combat biofilm with antimicrobial peptides from insects. In addition, the review also illustrates the importance of bioinformatics tools and molecular docking studies to boost the importance of select bioactive peptides those can be developed as drugs, as well as suggestions for further basic and clinical research.

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

BACKGROUND

Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space.

METHODS

Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process.

RESULTS

We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis.

CONCLUSIONS

Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences.

Macrel: antimicrobial peptide screening in genomes and metagenomes

Motivation

Antimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall.

Results

Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data.

Availability

Macrel is implemented in Python 3. It is available as open source at https://github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: https://big-data-biology.org/software/macrel.

Harnessing biomolecules for bioinspired dental biomaterials

Dental clinicians have relied for centuries on traditional dental materials (polymers, ceramics, metals, and composites) to restore oral health and function to patients. Clinical outcomes for many crucial dental therapies remain poor despite many decades of intense research on these materials. Recent attention has been paid to biomolecules as a chassis for engineered preventive, restorative, and regenerative approaches in dentistry. Indeed, biomolecules represent a uniquely versatile and precise tool to enable the design and development of bioinspired multifunctional dental materials to spur advancements in dentistry. In this review, we survey the range of biomolecules that have been used across dental biomaterials. Our particular focus is on the key biological activity imparted by each biomolecule toward prevention of dental and oral diseases as well as restoration of oral health. Additional emphasis is placed on the structure-function relationships between biomolecules and their biological activity, the unique challenges of each clinical condition, limitations of conventional therapies, and the advantages of each class of biomolecule for said challenge. Biomaterials for bone regeneration are not reviewed as numerous existing reviews on the topic have been recently published. We conclude our narrative review with an outlook on the future of biomolecules in dental biomaterials and potential avenues of innovation for biomaterial-based patient oral care.

To What Extent Do Fluorophores Bias the Biological Activity of Peptides? A Practical Approach Using Membrane-Active Peptides as Models

The characterization of biologically active peptides relies heavily on the study of their efficacy, toxicity, mechanism of action, cellular uptake, or intracellular location, using both in vitro and in vivo studies. These studies frequently depend on the use of fluorescence-based techniques. Since most peptides are not intrinsically fluorescent, they are conjugated to a fluorophore. The conjugation may interfere with peptide properties, thus biasing the results. The selection of the most suitable fluorophore is highly relevant. Here, a comprehensive study with blood-brain barrier (BBB) peptide shuttles (PepH3 and PepNeg) and antimicrobial peptides (AMPs) (vCPP2319 and Ctn[15-34]), tested as anticancer peptides (ACPs), having different fluorophores, namely 5(6)-carboxyfluorescein (CF), rhodamine B (RhB), quasar 570 (Q570), or tide fluor 3 (TF3) attached is presented. The goal is the evaluation of the impact of the selected fluorophores on peptide performance, applying routinely used techniques to assess cytotoxicity/toxicity, secondary structure, BBB translocation, and cellular internalization. Our results show that some fluorophores significantly modulate peptide activity when compared with unlabeled peptides, being more noticeable in hydrophobic and charged fluorophores. This study highlights the need for a careful experimental design for fluorescently labeled molecules, such as peptides.

Overview of Global Trends in Classification, Methods of Preparation and Application of Bacteriocins

This paper summarizes information about the division of bacteriocins into classes (Gram-negative bacteria, Gram-positive bacteria, and archaea). Methods for producing bacteriocins have been studied. It is known that bacteriocins, most successfully used today are products of secondary metabolism of lactic acid bacteria. It is established that the main method of bacteriocin research is PCR analysis, which makes it possible to quickly and easily identify the presence of bacteriocin encoding genes. The mechanism of cytotoxic action of bacteriocins has been studied. It is proved that the study of cytotoxic (antitumor) activity in laboratory conditions will lead to the clinical use of bacteriocins for cancer treatment in the near future. It is established that the incorporation of bacteriocins into nanoparticles and targeted delivery to areas of infection may soon become an effective treatment method. The delivery of bacteriocins in a concentrated form, such as encapsulated in nanoparticles, will increase their effectiveness and minimize potential toxic side effects. The analysis of publications on this topic confirmed that diverse research on bacteriocins is relevant.

Threats to adhesive/dentin interfacial integrity and next generation bio-enabled multifunctional adhesives

Nearly 100 million of the 170 million composite and amalgam restorations placed annually in the United States are replacements for failed restorations. The primary reason both composite and amalgam restorations fail is recurrent decay, for which composite restorations experience a 2.0-3.5-fold increase compared to amalgam. Recurrent decay is a pernicious problem-the standard treatment is replacement of defective composites with larger restorations that will also fail, initiating a cycle of ever-larger restorations that can lead to root canals, and eventually, to tooth loss. Unlike amalgam, composite lacks the inherent capability to seal discrepancies at the restorative material/tooth interface. The low-viscosity adhesive that bonds the composite to the tooth is intended to seal the interface, but the adhesive degrades, which can breach the composite/tooth margin. Bacteria and bacterial by-products such as acids and enzymes infiltrate the marginal gaps and the composite's inability to increase the interfacial pH facilitates cariogenic and aciduric bacterial outgrowth. Together, these characteristics encourage recurrent decay, pulpal damage, and composite failure. This review article examines key biological and physicochemical interactions involved in the failure of composite restorations and discusses innovative strategies to mitigate the negative effects of pathogens at the adhesive/dentin interface. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B:2466-2475, 2019.

Mitigation of peri-implantitis by rational design of bifunctional peptides with antimicrobial properties

The integration of molecular and cell biology with materials science has led to strategies to improve the interface between dental implants with the surrounding soft and hard tissues in order to replace missing teeth and restore mastication. More than 3 million implants have been placed in the US alone and this number is rising by 500,000/year. Peri-implantitis, an inflammatory response to oral pathogens growing on the implant surface threatens to reduce service life leading to eventual implant failure, and such an outcome will have adverse impact on public health and create significant health care costs. Here we report a predictive approach to peptide design, which enabled us to engineer a bifunctional peptide to combat bacterial colonization and biofilm formation, reducing the adverse host inflammatory immune response that destroys the tissue surrounding implants and shortens their lifespans. This bifunctional peptide contains a titanium-binding domain that recognizes and binds with high affinity to titanium implant surfaces, fused through a rigid spacer domain with an antimicrobial domain. By varying the antimicrobial peptide domain, we were able to predict the properties of the resulting bifunctional peptides in their entirety by analyzing the sequence-structure-function relationship. These bifunctional peptides achieve: 1) nearly 100% surface coverage within minutes, a timeframe suitable for their clinical application to existing implants; 2) nearly 100% binding to a titanium surface even in the presence of contaminating serum protein; 3) durability to brushing with a commercially available electric toothbrush; and 4) retention of antimicrobial activity on the implant surface following bacterial challenge. A bifunctional peptide film can be applied to both new implants and/or repeatedly applied to previously placed implants to control bacterial colonization mitigating peri-implant disease that threatens dental implant longevity.

Repeatedly Applied Peptide Film Kills Bacteria on Dental Implants

The rising use of titanium dental implants has increased the prevalence of peri-implant disease that shortens their useful life. A growing view of peri-implant disease suggests that plaque accumulation and microbiome dysbiogenesis trigger a host immune inflammatory response that destroys soft and hard tissues supporting the implant. The incidence of peri-implant disease is difficult to estimate, but with over 3 million implants placed in the USA alone, and the market growing by 500,000 implants/year, such extensive use demands additional interceptive approaches. We report a water-based, nonsur-gical approach to address peri-implant disease using a bifunctional peptide film, which can be applied during initial implant placement and later reapplied to existing implants to reduce bacterial growth. Bifunctional peptides are based upon a titanium binding peptide (TiBP) optimally linked by a spacer peptide to an antimicrobial peptide (AMP). We show herein that dental implant surfaces covered with a bifunctional peptide film kill bacteria. Further, using a simple protocol for cleaning implant surfaces fouled by bacteria, the surface can be effectively recoated with TiBP-AMP to regain an antimicrobial state. Fouling, cleansing, and rebinding was confirmed for up to four cycles with minimal loss of binding efficacy. After fouling, rebinding with a water-based peptide film extends control over the oral microbiome composition, providing a novel nonsurgical treatment for dental implants.

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

Antimicrobial peptides (AMPs) are part of the inherent immune system. In fact, they occur in almost all organisms including, e.g., plants, animals, and humans. Remarkably, they show effectivity also against multi-resistant pathogens with a high selectivity. This is especially crucial in times, where society is faced with the major threat of an ever-increasing amount of antibiotic resistant microbes. In addition, AMPs can also exhibit antitumor and antiviral effects, thus a variety of scientific studies dealt with the prediction of active peptides in recent years. Due to their potential, even the pharmaceutical industry is keen on discovering and developing novel AMPs. However, AMPs are difficult to verify in vitro, hence researchers conduct sequence similarity experiments against known, active peptides. Unfortunately, this approach is very time-consuming and limits potential candidates to sequences with a high similarity to known AMPs. Machine learning methods offer the opportunity to explore the huge space of sequence variations in a timely manner. These algorithms have, in principal, paved the way for an automated discovery of AMPs. However, machine learning models require a numerical input, thus an informative encoding is very important. Unfortunately, developing an appropriate encoding is a major challenge, which has not been entirely solved so far. For this reason, the development of novel amino acid encodings is established as a stand-alone research branch. The present review introduces state-of-the-art encodings of amino acids as well as their properties in sequence and structure based aggregation. Moreover, albeit a well-chosen encoding is essential, performant classifiers are required, which is reflected by a tendency towards specifically designed models in the literature. Furthermore, we introduce these models with a particular focus on encodings derived from support vector machines and deep learning approaches. Albeit a strong focus has been set on AMP predictions, not all of the mentioned encodings have been elaborated as part of antimicrobial research studies, but rather as general protein or peptide representations.

Peptide Mediated Antimicrobial Dental Adhesive System

The most common cause for dental composite failures is secondary caries due to invasive bacterial colonization of the adhesive/dentin (a/d) interface. Innate material weakness often lead to an insufficient seal between the adhesive and dentin. Consequently, bacterial by-products invade the porous a/d interface leading to material degradation and dental caries. Current approaches to achieve antibacterial properties in these materials continue to raise concerns regarding hypersensitivity and antibiotic resistance. Herein, we have developed a multi-faceted, bio-functionalized approach to overcome the vulnerability of such interfaces. An antimicrobial adhesive formulation was designed using a combination of antimicrobial peptide and a ε-polylysine resin system. Effector molecules boasting innate immunity are brought together with a biopolymer offering a two-fold biomimetic design approach. The selection of ε-polylysine was inspired due to its non-toxic nature and common use as food preservative. Biomolecular characterization and functional activity of our engineered dental adhesive formulation were assessed and the combinatorial formulation demonstrated significant antimicrobial activity against Streptococcus mutans. Our antimicrobial peptide-hydrophilic adhesive hybrid system design offers advanced, biofunctional properties at the critical a/d interface.

Peptide Mediated Antimicrobial Dental Adhesive System

The most common cause for dental composite failures is secondary caries due to invasive bacterial colonization of the adhesive/dentin (a/d) interface. Innate material weakness often lead to an insufficient seal between the adhesive and dentin. Consequently, bacterial by-products invade the porous a/d interface leading to material degradation and dental caries. Current approaches to achieve antibacterial properties in these materials continue to raise concerns regarding hypersensitivity and antibiotic resistance. Herein, we have developed a multi-faceted, bio-functionalized approach to overcome the vulnerability of such interfaces. An antimicrobial adhesive formulation was designed using a combination of antimicrobial peptide and a ε-polylysine resin system. Effector molecules boasting innate immunity are brought together with a biopolymer offering a two-fold biomimetic design approach. The selection of ε-polylysine was inspired due to its non-toxic nature and common use as food preservative. Biomolecular characterization and functional activity of our engineered dental adhesive formulation were assessed and the combinatorial formulation demonstrated significant antimicrobial activity against Streptococcus mutans. Our antimicrobial peptide-hydrophilic adhesive hybrid system design offers advanced, biofunctional properties at the critical a/d interface.