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.
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