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.

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.

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.

Aligned poly(l‐lactide)/poly(methyl methacrylate) binary blend fibers and mats loaded with a chimeric green fluorescence protein having a bioactive peptide with hydroxyapatite binding and mineralization property are prepared by pressurized gyration. The effect of processing parameters on the product morphologies, and the shape memory properties of these samples are investigated. Integration of hydroxyapatite nanoparticles into the fiber assembly is self‐directed using the hydroxyapatite‐binding property of the peptide genetically engineered to green fluorescence protein. Fluorescence microscopy analysis corroborated with Fourier transform infrared spectroscopy (FTIR) data confirms the integration of the chimeric protein with the fibers. An enzyme based remineralization assay is conducted to study the effects of peptide‐mediated mineralization within the fiber mats. Raman and FTIR spectral changes observed following the peptide‐mediated mineralization provides an initial step toward a soft‐hard material transition. These results show that programmable shape memory properties can be obtained by incorporating genetically engineered bioactive peptide domains into polymer fibers.

The skin is the most exposed organ and, therefore, vulnerable to injury and wounds (Nguyen & Soulika, 2019). Wound healing is a complex tissue repair process, and failing to manage it could result in the formation of scars (Landén et al., 2016; Takeo et al., 2015). Tissue repair involves the partial tissue regeneration involving restitution of tissue components during the wound healing process (Atkin et al., 2019; Gonzalez et al.., 2016). Wound healing is a dynamic process consisting of four phases: inflammation, proliferation,

Front Cover: Shape memory fibers and fibrous mats were produced for the first time by gyrating a polymer solution under pressure. The process allowed the integration of green fluorescence protein genetically engineered to carry a bioactive peptide domain into these fibers. The peptide interacted with hydroxyapatite nanoparticles added to the polymer enabling biomineralisation in the fibers. This work paves the way for manufacturing programmable shape memory products specifically tailored for desired healthcare applications. This is reported by Xiaowen Wu, Suntharavathanan Mahalingam, Sarah Kay VanOosten, Cate Wisdom, Candan Tamerler, and Mohan Edirisinghe, article number 1600270.