Implant-associated infections can have severe effects on the longevity of implant devices and they also represent a major cause of implant failures. Treating these infections associated with implants by antibiotics is not always an effective strategy due to poor penetration rates of antibiotics into biofilms. Additionally, emerging antibiotic resistance poses serious concerns. There is an urge to develop effective antibacterial surfaces that prevent bacterial adhesion and proliferation. A novel class of bacterial therapeutic agents, known as antimicrobial peptides (AMP’s), are receiving increasing attention as an unconventional option to treat septic infection, partly due to their capacity to stimulate innate immune responses and for the difficulty of microorganisms to develop resistance towards them. While host- and bacterial- cells compete in determining the ultimate fate of the implant, functionalization of implant surfaces with antimicrobial peptides can shift the balance and prevent implant infections. In the present study, we developed a novel chimeric peptide to functionalize the implant material surface. The chimeric peptide simultaneously presents two functionalities, with one domain binding to a titanium alloy implant surface through a titanium-binding domain while the other domain displays an antimicrobial property. This approach gains strength through control over the bio-material interfaces, a property built upon molecular recognition and self-assembly through a titanium alloy binding domain in the chimeric peptide. The efficiency of chimeric peptide both in-solution and absorbed onto titanium alloy surface was evaluated in vitro against three common human host infectious bacteria, S. mutans, S. epidermidis, and E. coli. In biological interactions such as occurs on implants, it is the surface and the interface that dictate the ultimate outcome. Controlling the implant surface by creating an interface composed chimeric peptides may therefore open up new possibilities to cover the implant site and tailor it to a desirable bioactivity.

Bacterial adhesion and growth at the composite/adhesive/tooth interface remain the primary cause of dental composite restoration failure. Early colonizers, including Streptococcus mutans, play a critical role in the formation of dental caries by creating an environment that reduces the adhesive's integrity. Subsequently, other bacterial species, biofilm formation, and lactic acid from S. mutans demineralize the adjoining tooth. Because of their broad spectrum of antibacterial activity and low risk for antibiotic resistance, antimicrobial peptides (AMPs) have received

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.

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.

Surgical site infection is a common cause of post-operative morbidity, often leading to implant loosening, ultimately requiring revision surgery, increased costs and worse surgical outcomes. Since implant failure starts at the implant surface, creating and controlling the bio-material interface will play a critical role in reducing infection while improving host cell-to-implant interaction. Here, we engineered a biomimetic interface based upon a chimeric peptide that incorporates a titanium binding peptide (TiBP) with an antimicrobial peptide (AMP) into a single molecule to direct binding to the implant surface and deliver an antimicrobial activity against S. mutans and S. epidermidis, two bacteria which are linked with clinical implant infections. To optimize antimicrobial activity, we investigated the design of the spacer domain separating the two functional domains of the chimeric peptide. Lengthening and changing the amino acid composition of the spacer resulted in an improvement of minimum inhibitory concentration by a three-fold against S. mutans. Surfaces coated with the chimeric peptide reduced dramatically the number of bacteria, with up to a nine-fold reduction for S. mutans and a 48-fold reduction for S. epidermidis. Ab initio predictions of antimicrobial activity based on structural features were confirmed. Host cell attachment and viability at the biomimetic interface were also improved compared to the untreated implant surface. Biomimetic interfaces formed with this chimeric peptide offer interminable potential by coupling antimicrobial and improved host cell responses to implantable titanium materials, and this peptide based approach can be extended to various biomaterials surfaces.

Bacterial infection is a serious medical problem leading to implant failure. The current antibiotic based therapies rise concerns due to bacterial resistance. The family of antimicrobial peptides (AMP) is one of the promising candidates as local therapy agents due to their broad-spectrum activity. Despite AMPs receive increasing attention to treat infection, their effective delivery to the implantation site has been limited. Here, we developed an engineered dual functional peptide which delivers AMP as a biomolecular therapeutic agent onto calcium phosphate deposited nanotubular titanium surfaces. Dual functionality of the peptide was achieved by combining a hydroxyapatite binding peptide-1 (HABP1) with an AMP using a flexible linker. HABP functionality of the peptide provided a self-coating property onto the nano-topographies that are designed to improve osteointegration capability, while AMP offered an antimicrobial protection onto the implant surface. We successfully deposited calcium phosphate minerals on nanotubular titanium oxide surface using pulse electrochemical deposition (PECD) and characterized the minerals by XRD, FT-IR, FE-SEM. Antimicrobial activity of the engineered peptide was tested *

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.

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.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2514-6) contains supplementary material, which is available to authorized users.