Engineered Peptides Enable Biomimetic Route for Collagen Intrafibrillar Mineralization.

Overcoming the short lifespan of current dental adhesives remains a significant clinical need. Adhesives rely on formation of the hybrid layer to adhere to dentin and penetrate within collagen fibrils. However, the ability of adhesives to achieve complete enclosure of demineralized collagen fibrils is recognized as currently unattainable. We developed a peptide-based approach enabling collagen intrafibrillar mineralization and tested our hypothesis on a type-I collagen-based platform. Peptide design incorporated collagen-binding and remineralization-mediating properties using the domain structure conservation approach. The structural changes from representative members of different peptide clusters were generated for each functional domain. Common signatures associated with secondary structure features and the related changes in the functional domain were investigated by attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and circular dichroism (CD) spectroscopy, respectively. Assembly and remineralization properties of the peptides on the collagen platforms were studied using atomic force microscopy (AFM). Mechanical properties of the collagen fibrils remineralized by the peptide assemblies was studied using PeakForce-Quantitative Nanomechanics (PF-QNM)-AFM. The engineered peptide was demonstrated to offer a promising route for collagen intrafibrillar remineralization. This approach offers a collagen platform to develop multifunctional strategies that combine different bioactive peptides, polymerizable peptide monomers, and adhesive formulations as steps towards improving the long-term prospects of composite resins.

Spatiotemporal Imaging of Zinc Ions in Zebrafish Live Brain Tissue Enabled by Fluorescent Bionanoprobes.

The zebrafish is a powerful model organism to study the mechanisms governing transition metal ions within whole brain tissue. Zinc is one of the most abundant metal ions in the brain, playing a critical pathophysiological role in neurodegenerative diseases. The homeostasis of free, ionic zinc (Zn2+) is a key intersection point in many of these diseases, including Alzheimer's disease and Parkinson's disease. A Zn2+ imbalance can eventuate several disturbances that may lead to the development of neurodegenerative changes. Therefore, compact, reliable approaches that allow the optical detection of Zn2+ across the whole brain would contribute to our current understanding of the mechanisms that underlie neurological disease pathology. We developed an engineered fluorescence protein-based nanoprobe that can spatially and temporally resolve Zn2+ in living zebrafish brain tissue. The self-assembled engineered fluorescence protein on gold nanoparticles was shown to be confined to defined locations within the brain tissue, enabling site specific studies, compared to fluorescent protein-based molecular tools, which diffuse throughout the brain tissue. Two-photon excitation microscopy confirmed the physical and photometrical stability of these nanoprobes in living zebrafish (Danio rerio) brain tissue, while the addition of Zn2+ quenched the nanoprobe fluorescence. Combining orthogonal sensing methods with our engineered nanoprobes will enable the study of imbalances in homeostatic Zn2+ regulation. The proposed bionanoprobe system offers a versatile platform to couple metal ion specific linkers and contribute to the understanding of neurological diseases.

Autonomous-Strengthening Adhesive Provides Hydrolysis-Resistance and Enhanced Mechanical Properties in Wet Conditions.

The low-viscosity adhesive that is used to bond composite restorative materials to the tooth is readily damaged by acids, enzymes, and oral fluids. Bacteria infiltrate the resulting gaps at the composite/tooth interface, demineralize the tooth, and further erode the adhesive. This paper presents the preparation and characterization of a low-crosslink-density hydrophilic adhesive that capitalizes on sol-gel reactions and free-radical polymerization to resist hydrolysis and provide enhanced mechanical properties in wet environments. Polymerization behavior, water sorption, and leachates were investigated. Dynamic mechanical analyses (DMA) were conducted using water-saturated adhesives to mimic load transfer in wet conditions. Data from all tests were analyzed using appropriate statistical tests (α = 0.05). The degree of conversion was comparable for experimental and control adhesives at 88.3 and 84.3%, respectively. HEMA leachate was significantly lower for the experimental (2.9 wt%) compared to control (7.2 wt%). After 3 days of aqueous aging, the storage and rubbery moduli and the glass transition temperature of the experimental adhesive (57.5MPa, 12.8MPa, and 38.7 °C, respectively) were significantly higher than control (7.4MPa, 4.3 MPa, and 25.9 °C, respectively). The results indicated that the autonomic sol-gel reaction continues in the wet environment, leading to intrinsic reinforcement of the polymer network, improved hydrolytic stability, and enhanced mechanical properties.

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.

Probing Selective Self-Assembly of Putrescine Oxidase with Controlled Orientation Using a Genetically Engineered Peptide Tag.

Controlling enzyme orientation and location on surfaces is a critical step for their successful deployment in diverse applications from biosensors to lab-on-a-chip devices. Functional activity of the enzymes on the surface will largely depend on the spatial arrangement and orientation. Solid binding peptides have been proven to offer versatility for immobilization of biomolecules on inorganic materials including metals, oxides, and minerals. Previously, we demonstrated the utility of a gold binding peptide genetically incorporated into the enzyme putrescine oxidase (PutOx-AuBP), enabling self-enzyme assembly on gold substrates. PutOx is an attractive biocatalyst among flavin oxidases, using molecular oxygen as an electron acceptor without requiring a dissociable coenzyme. Here, we explore the selective self-assembly of this enzyme on a range of surfaces using atomic force microscopy (AFM) along with the assessment of functional activity. This work probes the differences in surface coverage, distribution, size, shape, and activity of PutOx-AuBP in comparison to those of native putrescine oxidase (PutOx) on multiple surfaces to provide insight for material-selective enzymatic assembly. Surfaces investigated include metal (templated-stripped gold (TSG)), oxide (native SiO2 on Si(111)), minerals (mica and graphite), and self-assembled monolayers (SAMs) with a range of hydrophobicity and charge. Supported by both the coverage and the dimensions of immobilized enzymes, our results indicate that of the surfaces investigated, material-selective binding takes place with orientation control only for PutOx-AuBP onto the TSG substrate. These differences are consistent with the measurements of surface-bound enzymatic activities. Substrate-dependent differences observed indicate significant variations in enzyme-surface interactions ranging from peptide-directed self-assembly to enzyme aggregation. The implications of this study provide insight for the fabrication of enzymatic patterns directed by self-assembling peptide tags onto localized surface regions. Enabling functional enzyme-based nanoscale materials offers a fascinating path for utilization of sustainable biocatalysts integrated into multiscale devices.

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.

Self-Immobilized Putrescine Oxidase Biocatalyst System Engineered with a Metal Binding Peptide.

Flavin oxidases are valuable biocatalysts for the oxidative synthesis of a wide range of compounds, while at the same time reduce oxygen to hydrogen peroxide. Compared to other redox enzymes, their ability to use molecular oxygen as an electron acceptor offers a relatively simple system that does not require a dissociable coenzyme. As such, they are attractive targets for adaptation as cost-effective biosensor elements. Their functional immobilization on surfaces offers unique opportunities to expand their utilization for a wide range of applications. Genetically engineered peptides have been demonstrated as enablers of the functional assembly of biomolecules at solid material interfaces. Once identified as having a high affinity for the material of interest, these peptides can provide a single step bioassembly process with orientation control, a critical parameter for functional immobilization of the enzymes. In this study, for the first time, we explored the bioassembly of a putrescine oxidase enzyme using a gold binding peptide tag. The enzyme was genetically engineered to incorporate a gold binding peptide with an expectation of an effective display of the peptide tag to interact with the gold surface. In this work, the functional activity and expression were investigated, along with the selectivity of the binding of the peptide-tagged enzyme. The fusion enzyme was characterized using multiple techniques, including protein electrophoresis, enzyme activity, and microscopy and spectroscopic methods, to verify the functional expression of the tagged protein with near-native activity. Binding studies using quartz crystal microbalance (QCM), nanoparticle binding studies, and atomic force microscopy studies were used to address the selectivity of the binding through the peptide tag. Surface binding AFM studies show that the binding was selective for gold. Quartz crystal microbalance studies show a strong increase in the affinity of the peptide-tagged protein over the native enzyme, while activity assays of protein bound to nanoparticles provide evidence that the enzyme retained catalytic activity when immobilized. In addition to showing selectivity, AFM images show significant differences in the height of the molecules when immobilized through the peptide tag compared to immobilization of the native enzyme, indicating differences in orientation of the bound enzyme when attached via the affinity tag. Controlling the orientation of surface-immobilized enzymes would further improve their enzymatic activity and impact diverse applications, including oxidative biocatalysis, biosensors, biochips, and biofuel production.

Direct interaction of ligand-receptor pairs specifying stomatal patterning.

Valves on the plant epidermis called stomata develop according to positional cues, which likely involve putative ligands (EPIDERMAL PATTERNING FACTORS [EPFs]) and putative receptors (ERECTA family receptor kinases and TOO MANY MOUTHS [TMM]) in Arabidopsis. Here we report the direct, robust, and saturable binding of bioactive EPF peptides to the ERECTA family. In contrast, TMM exhibits negligible binding to EPF1 but binding to EPF2. The ERECTA family forms receptor homomers in vivo. On the other hand, TMM associates with the ERECTA family but not with itself. While ERECTA family receptor kinases exhibit complex redundancy, blocking ERECTA and ERECTA-LIKE1 (ERL1) signaling confers specific insensitivity to EPF2 and EPF1, respectively. Our results place the ERECTA family as the primary receptors for EPFs with TMM as a signal modulator and establish EPF2-ERECTA and EPF1-ERL1 as ligand-receptor pairs specifying two steps of stomatal development: initiation and spacing divisions.

Self Assembled Recombinant Proteins on Metallic Nanoparticles As Bimodal Imaging Probes.

Combining multiple modalities is at the center of developing new methods for sensing and imaging that are required for comprehensive understanding of events at the molecular level. Various imaging modalities have been developed using metallic nanoparticles owning to their exceptional physical and chemical properties. Due to their localized surface plasmon resonance characteristics, gold and silver nanoparticles exhibit unique optoelectronic properties commonly used in biomedical sciences and engineering. Self assembled monolayers or physical adsorption have previously been adapted to functionalize the surfaces of nanoparticles with biomolecules for targeted imaging. However, depending on differences among the functional groups used on the nanoparticle surface, wide variation in the displayed biomolecular property to recognize its target may result. In the last decade, the properties of inorganic binding peptides have been proven advantageous to assemble selective functional nano-entities or proteins onto nanoparticles surfaces. Herein we explored formation of self-assembled hybrid metallic nano-architectures that are composed of gold and silver nanoparticles with fluorescent proteins, for use as bimodal imaging probes. We employed metal binding peptide-based assembly to self assemble green fluorescence protein onto metallic substrates of various geometries. Assembly of the green fluorescent proteins, genetically engineered to incorporate gold- or silver-binding peptides onto metallic nanoparticles, resulted in the generation of hybrid-, biomodal-imaging probes in a single step. Green fluorescent activity on gold and silver surfaces can be been monitored using both plasmonic and fluorescent signatures. Our results demonstrate a novel bimodal imaging system that can be finely tuned with respect to nanoparticle size and protein concentration. Resulting hybrid probes may mitigate the limitation of depth penetration into biological tissues as well as providing high signal-to-noise ratio and sensitivity.

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.

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

BACKGROUND: Antimicrobial 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. RESULTS: By 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. CONCLUSIONS: We 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.

Coupling Infusion and Gyration for the Nanoscale Assembly of Functional Polymer Nanofibers Integrated with Genetically Engineered Proteins.

Nanofibers featuring functional nanoassemblies show great promise as enabling constituents for a diverse range of applications in areas such as tissue engineering, sensing, optoelectronics, and nanophotonics due to their controlled organization and architecture. An infusion gyration method is reported that enables the production of nanofibers with inherent biological functions by simply adjusting the flow rate of a polymer solution. Sufficient polymer chain entanglement is obtained at Berry number > 1.6 to make bead-free fibers integrated with gold nanoparticles and proteins, in the diameter range of 117-216 nm. Integration of gold nanoparticles into the nanofiber assembly is followed using a gold-binding peptide tag genetically conjugated to red fluorescence protein (DsRed). Fluorescence microscopy analysis corroborated with Fourier transform infrared spectroscopy (FTIR) data confirms the integration of the engineered red fluorescence protein with the nanofibers. The gold nanoparticle decorated nanofibers having red fluorescence protein as an integral part keep their biological functionality including copper-induced fluorescence quenching of the DsRed protein due to its selective Cu(+2) binding. Thus, coupling the infusion gyration method in this way offers a simple nanoscale assembly approach to integrate a diverse repertoire of protein functionalities into nanofibers to generate biohybrid materials for imaging, sensing, and biomaterial applications.

Bio-inspired hard-to-soft interface for implant integration to bone.

Accomplishing full, functional integration at the host-to-biomaterial interface has been a critical roadblock in engineering implants with performance similar to biological materials. Molecular recognition-based self-assembly, coupled with biochemical signaling, may lead to controllable and predictable cellular differentiation at the implant interface. Here, we engineer a bio-inspired interface built upon a chimeric peptide. Binding to the biomaterial interface is achieved using a molecular recognition domain specific for the titanium/titanium alloy implant surface and a biochemical signal guiding stem cells to differentiate by activating the Wnt signaling pathway for bone formation. During a critical period of host cell growth and determination, the bioactive implant interface signals mouse, as well as human, stem cells to differentiate along osteogenic lineages. The Wnt-induced cells show enhanced mineral deposition in an extracellular matrix of their creation and an enhanced gene expression profile consistent with osteogenesis, thereby providing a bone-to-implant interface that promotes bone regeneration. FROM THE CLINICAL EDITOR: This team of authors studied methods for enhanced hard-to-soft interface for implant integration to bone, and demonstrate how a bio-inspired surface built upon a chimeric peptide may be utilized for this purpose.

Chimeric peptides as implant functionalization agents for titanium alloy implants with antimicrobial properties.

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.

Thermodynamics of engineered gold binding peptides: establishing the structure-activity relationships.

Adsorption behavior of a gold binding peptide was experimentally studied to achieve kinetics and thermodynamics parameters toward understanding of the binding of an engineered peptide onto a solid metal surface. The gold-binding peptide, GBP1, was originally selected using a cell surface display library and contains 14 amino acid residues. In this work, single- and three-repeats of GBP1 were used to assess the effects of two parameters: molecular architecture versus secondary structure on adsorption on to gold substrate. The adsorption measurements were carried out using surface plasmon resonance (SPR) spectroscopy at temperatures ranging from 10 to 55 °C. At all temperatures, two different regimes of peptide adsorption were observed, which, based on the model, correspond to two sets of thermodynamics values. The values of enthalpy, ΔH(ads), and entropy, ΔS(ads), in these two regimes were determined using the van't Hoff approach and Gibbs-Helmholtz relationship. In general, the values of enthalpy for both peptides are negative indicating GBP1 binding to gold is an exothermic phenomenon and that the binding of three repeat gold binding peptide (3l-GBP1) is almost 5 times tighter than that for the single repeat (l-GBP1). More intriguing result is that the entropy of adsorption for the 3l-GBP1 is negative (-43.4 ± 8.5 cal/(mol K)), while that for the l-GBP1 is positive (10.90 ± 1.3 cal/(mol K)). Among a number of factors that synergistically contribute to the decrease of entropy, long-range ordered self-assembly of the 3l-GBP1 on gold surface is the most effective, probably through both peptide-solid and peptide-peptide intermolecular interactions. Additional adsorption experiments were conducted in the presence of 2,2,2-trifluoroethanol (TFE) to determine how the conformational structures of the biomolecules responded to the environmental perturbation. We found that the peptides differ in their conformational responses to the change in solution conditions; while l-GBP does not fold in the presence of TFE, 3l-GBP1 adopted two types of secondary structure (ß-strand, α-helix) and that peptide's binding to the solid is enhanced by the presence of low percentages of TFE solvent. Not only do these kinetics and thermodynamics results provide adsorption behavior and binding of genetically engineered peptides for inorganics (GEPI), but they could also provide considerable insights into fundamental understanding peptide molecular recognition and their selective specificity for the solids. Moreover, comprehensive work described herein suggests that multiple repeat forms of the solid binding peptides possess a conformational component that can be exploited to further tailor affinity and binding of a given sequence to a solid material followed by ordered assembly as a convenient tool in future practical applications.

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.

Designing Collagen-Binding Peptide with Enhanced Properties Using Hydropathic Free Energy Predictions.

Collagen is fundamental to a vast diversity of health functions and potential therapeutics. Short peptides targeting collagen are attractive for designing modular systems for site-specific delivery of bioactive agents. Characterization of peptide-protein binding involves a larger number of potential interactions that require screening methods to target physiological conditions. We build a hydropathy-based free energy estimation tool which allows quick evaluation of peptides binding to collagen. Previous studies showed that pH plays a significant role in collagen structure and stability. Our design tool enables probing peptides for their collagen-binding property across multiple pH conditions. We explored binding features of currently known collagen-binding peptides, collagen type I alpha chain 2 sense peptide (TKKTLRT) and decorin LRR-10 (LRELHLNNN). Based on these analyzes, we engineered a collagen-binding peptide with enhanced properties across a large pH range in contrast to LRR-10 pH dependence. To validate our predictions, we used a quantum-dots-based binding assay to compare the coverage of the peptides on type I collagen. The predicted peptide resulted in improved collagen binding. Hydropathy of the peptide-protein pair is a promising approach to finding compatible pairings with minimal use of computational resources, and our method allows for quick evaluation of peptides for binding to other proteins. Overall, the free-energy-based tool provides an alternative computational screening approach that impacts protein interaction search methods.

Engineered Escherichia coli silver-binding periplasmic protein that promotes silver tolerance.

Silver toxicity is a problem that microorganisms face in medical and environmental settings. Through exposure to silver compounds, some bacteria have adapted to growth in high concentrations of silver ions. Such adapted microbes may be dangerous as pathogens but, alternatively, could be potentially useful in nanomaterial-manufacturing applications. While naturally adapted isolates typically utilize efflux pumps to achieve metal resistance, we have engineered a silver-tolerant Escherichia coli strain by the use of a simple silver-binding peptide motif. A silver-binding peptide, AgBP2, was identified from a combinatorial display library and fused to the C terminus of the E. coli maltose-binding protein (MBP) to yield a silver-binding protein exhibiting nanomolar affinity for the metal. Growth experiments performed in the presence of silver nitrate showed that cells secreting MBP-AgBP2 into the periplasm exhibited silver tolerance in a batch culture, while those expressing a cytoplasmic version of the fusion protein or MBP alone did not. Transmission electron microscopy analysis of silver-tolerant cells revealed the presence of electron-dense silver nanoparticles. This is the first report of a specifically engineered metal-binding peptide exhibiting a strong in vivo phenotype, pointing toward a novel ability to manipulate bacterial interactions with heavy metals by the use of short and simple peptide motifs. Engineered metal-ion-tolerant microorganisms such as this E. coli strain could potentially be used in applications ranging from remediation to interrogation of biomolecule-metal interactions in vivo.

Fabrication of hierarchical hybrid structures using bio-enabled layer-by-layer self-assembly.

Development of versatile and flexible assembly systems for fabrication of functional hybrid nanomaterials with well-defined hierarchical and spatial organization is of a significant importance in practical nanobiotechnology applications. Here we demonstrate a bio-enabled self-assembly technique for fabrication of multi-layered protein and nanometallic assemblies utilizing a modular gold-binding (AuBP1) fusion tag. To accomplish the bottom-up assembly we first genetically fused the AuBP1 peptide sequence to the C'-terminus of maltose-binding protein (MBP) using two different linkers to produce MBP-AuBP1 hetero-functional constructs. Using various spectroscopic techniques, surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR), we verified the exceptional binding and self-assembly characteristics of AuBP1 peptide. The AuBP1 peptide tag can direct the organization of recombinant MBP protein on various gold surfaces through an efficient control of the organic-inorganic interface at the molecular level. Furthermore using a combination of soft-lithography, self-assembly techniques and advanced AuBP1 peptide tag technology, we produced spatially and hierarchically controlled protein multi-layered assemblies on gold nanoparticle arrays with high molecular packing density and pattering efficiency in simple, reproducible steps. This model system offers layer-by-layer assembly capability based on specific AuBP1 peptide tag and constitutes novel biological routes for biofabrication of various protein arrays, plasmon-active nanometallic assemblies and devices with controlled organization, packing density and architecture.

Improved Candida methylica formate dehydrogenase fermentation through statistical optimization of low-cost culture media.

NAD⁺-dependent formate dehydrogenase (FDH, EC 1.2.1.2) is of use in the regeneration of NAD(P)H coenzymes, and therefore has strong potential for practical application in chemical and medical industries. A low-cost production of recombinant Escherichia coli (E. coli) containing FDH from Candida methylica (cmFDH) was optimized in molasses-based medium by using response surface methodology (RSM) based on central composite design (CCD). The beet molasses as a sole carbon source, (NH4)2HPO4 as a nitrogen and phosphorus source, KH2PO4 as a buffer agent, and Mg2SO4 · 7H2O as a magnesium and sulfur source were used as variables in the medium. The optimum medium composition was found to be 34.694 g L⁻¹ of reducing sugar (equivalent to molasses solution), 8.536 g L⁻¹ of (NH4)2HPO4, 3.073 g L⁻¹ of KH2PO4, and 1.707 g L⁻¹ of Mg2SO4 · 7H2O. Molasses-based culture medium increased the yield of cmFDH about three times compared to LB medium. The currently developed media has the potential to be used in industrial bioprocesses with low-cost production.