Design Of Peptide Sequences Research Articles

Colorimetric detection of furin based on enhanced catalytic activity of G-quadruplex/hemin DNAzyme

BackgroundRapid and sensitive colorimetric detection methods are crucial for diseases diagnosis, particularly those involving proteases like furin, which are implicated in various conditions, including cancer. Traditional detection methods for furin suffer from limitations in sensitivity and practicality for on-site detection, motivating the development of novel detection strategies. Therefore, developing a simple, enzyme-free, and rapid colorimetric analysis method with high sensitivity for furin detection is imperative. ResultsHerein, we have proposed a colorimetric method in this work for the first time to detect furin, leveraging the assembly of G-quadruplex/hemin DNAzyme with enhanced catalytic activity. Specifically, a peptide-DNA conjugate (PDC) comprising a furin-recognition peptide and flanking DNA sequences for signal amplification is designed to facilitate the DNAzyme assembly. Upon furin treatment, PDC cleavage triggers a cyclic catalytic hairpin assembly reaction to form the complementary double-stranded structures by hairpin 1 (HP1) and hairpin 2 (HP2), bringing the G-quadruplex sequence in HP1 closer to hemin on HP2. Moreover, the resulting G-quadruplex/hemin DNAzymes exhibit robust peroxidase-like activity, enabling the catalysis of the colorimetric reaction of ABTS2− for furin detection. Our method demonstrates high sensitivity, rapid response, and compatibility with complex sample matrices, achieving a detection limit as low as 1.1 pM. SignificanceThe DNAzyme reported in this work exhibits robust catalytic activity, enabling high sensitivity and good efficiency for the detection. By eliminating the requirement for exogenous enzymes, our approach enables visual furin detection without expensive instrumentation and reagents, promising significant utility in biomedical and clinical diagnostic applications. Given the various design of peptide sequence and the programmability of DNA, it can be readily applied to analyzing other useful tumor biomarkers.

Ultrashort Peptide Grafting on Mesoporous Films and Its Impact on Ionic Mesopore Accessibility.

An approach for direct in-pore solid-phase ultrashort peptide synthesis on mesoporous films using the amino acids arginine, leucine, and glycine is presented. Although the number of grafted amino acids remains low, the ionic mesopore accessibility can be gradually adjusted. The addition of arginine in up to five reaction cycles leads to a progressive increase in positive mesopore charge density, which gradually increases the anionic mesopore accessibility at acidic pH. At basic pH, the remaining silanol groups at the pore wall still dominate counter-charged cation mesopore accessibility. Thus, specific peptide sequence design is demonstrated to be a sensitive tool for molecular transport control in nanoscale pores. Overall, the direct in-pore solid-phase ultrashort peptide synthesis on mesoporous films using the sequences of different amino acids opens up exciting opportunities for the development of innovative materials with precisely tailored properties and functions based on specific peptide sequence design.

Generative β-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

Applications of self-assembled peptide hydrogels in anti-tumor therapy.

Peptides are a class of active substances composed of a variety of amino acids with special physiological functions. The rational design of peptide sequences at the molecular level enables their folding into diverse secondary structures. This property has garnered significant attention in the biomedical sphere owing to their favorable biocompatibility, adaptable mechanical traits, and exceptional loading capabilities. Concurrently with advancements in modern medicine, the diagnosis and treatment of tumors have increasingly embraced targeted and personalized approaches. This review explores recent applications of self-assembled peptides derived from natural amino acids in chemical therapy, immunotherapy, and other adjunctive treatments. We highlighted the utilization of peptide hydrogels as delivery systems for chemotherapeutic drugs and other bioactive molecules and then discussed the challenges and prospects for their future application.

Exploiting the Features of Short Peptides to Recognize Specific Cell Surface Markers.

Antibodies are the macromolecules of choice to ensure specific recognition of biomarkers in biological assays. However, they present a range of shortfalls including a relatively high production cost and limited tissue penetration. Peptides are relatively small molecules able to reproduce sequences of highly specific paratopes and, although they have less biospecificity than antibodies, they offer advantages like ease of synthesis, modifications of their amino acid sequences and tagging with fluorophores and other molecules required for detection. This work presents a strategy to design peptide sequences able to recognize the CD44 hyaluronic acid receptor present in the plasmalemma of a range of cells including human bone marrow stromal mesenchymal cells. The protocol of identification of the optimal amino acid sequence was based on the combination of rational design and in silico methodologies. This protocol led to the identification of two peptide sequences which were synthesized and tested on human bone marrow mesenchymal stromal cells (hBM-MSCs) for their ability to ensure specific binding to the CD44 receptor. Of the two peptides, one binds CD44 with sensitivity and selectivity, thus proving its potential to be used as a suitable alternative to this antibody in conventional immunostaining. In the context of regenerative medicine, the availability of this peptide could be harnessed to functionalize tissue engineering scaffolds to anchor stem cells as well as to be integrated into systems such as cell sorters to efficiently isolate MSCs from biological samples including various cell subpopulations. The data here reported can represent a model for developing peptide sequences able to recognize hBM-MSCs and other types of cells and for their integration in a range of biomedical applications.

Arginine Methylation Regulates Self-Assembly of Peptides.

Bio-inspired design of peptides represents one facile strategy for development of supramolecular monomers for self-assembly into well-defined nanostructures. Inspired by methylation of arginine during post-translational modification for manipulating protein functions, herein, the controllable self-assembly of peptides via rational incorporation of methylated arginine residues into bola-amphiphilic peptides is reported. A series of bola-amphiphilic peptides are designed and synthesized either containing natural arginine or methylated arginine and investigate the influence of arginine methylation on peptide assembly. This study finds that incorporation of symmetrically di-methylated arginine into oppositely charged hexapeptide hex-SDMAE leads to distinct assembling performance compare to natural peptide hex-RE. The findings demonstrate that the methylation of rationally designed peptide sequences allows for regulation of self-assembly of peptides, thus implying the great potential of arginine methylation in establishing controllable peptide assembling systems and creating in situ formulation of biomedical materials in the future.

Insight into the Binding Mechanisms of Quartz-Selective Peptides: Toward Greener Flotation Processes.

Mining practices, chiefly froth flotation, are being critically reassessed to replace their use of biohazardous chemical reagents in favor of biofriendly alternatives as a path toward green processes. In this regard, this study aimed at evaluating the interactions of peptides, as potential floatation collectors, with quartz using phage display and molecular dynamics (MD) simulations. Quartz-selective peptide sequences were initially identified by phage display at pH = 9 and further modeled by a robust simulation scheme combining classical MD, replica exchange MD, and steered MD calculations. Our residue-specific analyses of the peptides revealed that positively charged arginine and lysine residues were favorably attracted by the quartz surface at basic pH. The negatively charged residues at pH 9 (i.e., aspartic acid and glutamic acid) further showed affinity toward the quartz surface through electrostatic interactions with the positively charged surface-bound Na+ ions. The best-binding heptapeptide combinations, however, contained both positively and negatively charged residues in their composition. The flexibility of peptide chains was also shown to directly affect the adsorption behavior of the peptide. While attractive intrapeptide interactions were dominated by a weak peptide-quartz binding, the repulsive self-interactions in the peptides improved the binding propensity to the quartz surface. Our results showed that MD simulations are fully capable of revealing mechanistic details of peptide adsorption to inorganic surfaces and are an invaluable tool to accelerate the rational design of peptide sequences for mineral processing applications.

Reconfigurable Peptide Analogs of Apolipoprotein A-I Reveal Tunable Features of Nanodisc Assembly.

Nanodisc (ND)-forming membrane scaffold proteins or peptides developed from apolipoprotein A-I (apoA-I) have led to considerable promise in structural biology and therapeutic applications. However, the rationale and regularity characteristics in peptide sequence design remain inconclusive. Here, we proposed a consensus-based normalization approach through the reversed engineering of apoA-IΔ1-45 to design reconfigurable apoA-I peptide analogs (APAs) for tunable ND assembly. We present extensive morphological validations and computational simulation analyses on divergent APA-NDs that are generated by our method. Fifteen divergent APAs were generated accordingly to study the assembly machinery of NDs. We show that APA designs exhibit multifactorial influence in terms of varying APA tandem repeats, sequence composition, and lipid-to-APA ratio to form tunable diameters of NDs. There is a strong positive correlation between DMPC-to-APA ratios and ND diameters. Longer APA with more tandem repeats tends to yield higher particle size homogeneity. Our results also suggest proline is a dispensable residue for the APA-ND formation. Interestingly, proline-rich substitution not only provides an inward-bending effect in forming smaller NDs but also induces the cumulative chain flexibility that enables larger ND formation at higher lipid ratios. Additionally, proline-tryptophan residues in APAs play a dominant role in forming larger NDs. Molecular simulation shows that enriched basic and acidic residues in APAs evoke abundant hydrogen-bond and salt bridge networks to reinforce the structural stability of APA-NDs. Together, our findings provide a rational basis for understanding APA design. The proposed model could be extended to other apolipoproteins for desired ND engineering.

Probing the Interactions of LRP1 Ectodomain-Derived Peptides with Fibrillar Tau Protein and Its Impact on Cellular Internalization

Cellular internalization and the spreading of misfolded tau have become increasingly important for elucidating the mechanism of Tau pathology involved in Alzheimer’s disease (AD). The low-density lipoprotein-related receptor 1 (LRP1) has been implicated in the internalization of fibrillar tau. In this work, we utilized homology modeling to model the Cluster 2 domain of LRP1 and determined that a 23-amino-acid sequence is involved in binding to paired helical filaments (PHF) of Tau. Fourteen short peptide segments derived from this ectodomain region were then designed and docked with PHF Tau. Molecular dynamics studies of the optimal peptides bound to PHF Tau demonstrated that the peptides formed critical contacts through Lys and Gln residues with Tau. Based on the computational results, flow cytometry, AFM, SPR analysis and CD studies were conducted to examine binding and cellular internalization. The results showed that the peptide sequence TauRP (1–14) (DNSDEENCES) was not only associated with fibrillar Tau but was also able to mitigate its cellular internalization in LRP1-expressed HEK-293 cells. Preliminary docking studies with Aβ (1–42) revealed that the peptides also bound to Aβ (1–42). While this study focused on the CCR2 domain of LRP1 to design peptide sequences to mitigate Tau internalization, the work can be extended to other domains of the LRP1 receptor or other receptors to examine if the cellular internalization of fibrillar Tau can be deterred. These findings show that short peptides derived from the LRP1 receptor can alter the internalization of its ligands.

Solubility-Aware Protein Binding Peptide Design Using AlphaFold.

New protein–protein interactions (PPIs) are identified, but PPIs have different physicochemical properties compared with conventional targets, making it difficult to use small molecules. Peptides offer a new modality to target PPIs, but designing appropriate peptide sequences by computation is challenging. Recently, AlphaFold and RoseTTAFold have made it possible to predict protein structures from amino acid sequences with ultra-high accuracy, enabling de novo protein design. We designed peptides likely to have PPI as the target protein using the “binder hallucination” protocol of AfDesign, a de novo protein design method using AlphaFold. However, the solubility of the peptides tended to be low. Therefore, we designed a solubility loss function using solubility indices for amino acids and developed a solubility-aware AfDesign binder hallucination protocol. The peptide solubility in sequences designed using the new protocol increased with the weight of the solubility loss function; moreover, they captured the characteristics of the solubility indices. Moreover, the new protocol sequences tended to have higher affinity than random or single residue substitution sequences when evaluated by docking binding affinity. Our approach shows that it is possible to design peptide sequences that can bind to the interface of PPI while controlling solubility.

Sequence-Dependent Tyrosine-Containing Peptide Nanoassemblies for Sensing Tyrosinase and Melanoma.

Tyrosine-containing peptide nanoassemblies have received tremendous attention because of their potential applications in biomedicine and nanomaterial fields. However, a current outstanding challenge is to direct the equilibrium between oxidative polymerization of precursors and the noncovalent assembly to precisely tune their specific nanostructures and functionalities through the rational design of peptide sequences. With a simple library of tripeptides containing tyrosine, glycine, and lysine, here we demonstrate how amino acid sequence encodes the property of tripeptide nanoassemblies by modulating the enzymatic oxidation of tyrosinase with the accompanied self-assembly, and thus select the pathways toward fluorescent or melanin-like nanoassemblies. The fluorescence of tripeptide nanoassemblies has been demonstrated in sensing both tyrosinase and melanoma. Our findings will provide inspiration of peptide sequence design for generating the complex bioactive peptide nanomaterials for biomedical applications.

PepFun: Open Source Protocols for Peptide-Related Computational Analysis.

Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.

A novel long-acting oxyntomodulin analogue eliminates diabetes and obesity in mice

Oxyntomodulin (OXM) was identified as a glucagon (GCG) receptor (GCGR) and glucagon-like peptide 1 (GLP-1) receptor (GLP-1R) dual agonist to suppress appetite, increase energy expenditure, and induce body weight loss in obese humans. However, the activities of native OXM to activate GCGR and GLP-1R in vitro were much weaker than the natural ligands. To address this gap, structural modifications were adopted and novel OXM analogues were obtained through chimeric peptide sequence design. One specific analogue with enhanced and balanced GCGR/GLP-1R activations was chemically conjugated with polyethylene glycol (PEG) to achieve sustained release in vivo. This PEGylated analogue was further explored pharmacologically in db/db and diet-induced obese (DIO) mice models. Chronic weekly administration significantly induced hypoglycemic effects and body weight loss with dose dependency, along with normalized adiposity, lipid metabolism, and liver steatosis. Based on its profiles in vitro and in vivo, the analogue has the great potential to develop as a novel anti-diabetic and/or anti-obese candidate. As observed more insulin stimulation and improved insulin resistance, it may be also explored for the treatment of nonalcoholic steatohepatitis (NASH) in the future.

Machine learning for synthetic biology: Methods and applications

Traditional synthetic biology takes a trial-and-error approach, suffering from inefficiency and local optima. Recent advances in high-throughput experimental techniques generate a huge amount of biological data, which enables the use of machine learning to close the “design-build-test-learn” loop. Machine learning, especially deep learning, is a data-driven modeling method, which extracts useful patterns from big data and then leverages learned knowledge to tackle specific tasks. In this review, we aim to provide a brief primer of machine learning to synthetic biologists. Starting with common taxonomy, we introduce representative methods, pipelines, and underlying principles of machine learning that can be applied in synthetic biology. We include typical methods such as support vector machine, deep neural networks, generative adversarial nets, transfer learning and reinforcement learning. In particular, discriminative models, including convolutional neural networks and support vector machine, are appropriate for predicting sequence-function relationship. Generative models, including generative adversarial nets (GANs) and deep generative models for graph generation, are suitable for sequence or network design. Next, we review the recent applications of machine learning in studying synthetic biology parts and modules, including promoters, bioactive peptides, enzymes, metabolic pathways, and genetic circuits. For example, DeePromoter combined a convolutional neural network and a long-short term memory to achieve an accuracy as high as 90% when predicting promoter sequences. For enzyme design, a Gauss Process model was proposed with Bayesian optimization by upper confidence bound method, which resulted in the engineering of thermostable P450 enzymes. For antimicrobial peptides, a generative GAN model enhanced with a feedback mechanism was trained to design peptide sequences with new functions. Finally, we conclude with future challenges and directions. Particularly, interpretable machine learning models are desirable to guide mechanistic investigation. Moreover, it is necessary to develop new machine learning methods that are more compatible with biological data, which are heterogeneous, multi-modal (such as sequence, network, image, and structure), and lack of proper labels. With the increasing availability of big biological data and development of machine learning methods tailored for synthetic biology, we envision a paradigm shift towards a closed cycle of “design-build-test-learn” in creating artificial life with predictable functions.

Peptide Capping Agent Design for Gold (111) Facet by Molecular Simulation and Experimental Approaches

The stochastic tunneling-basin hopping method (STUN-BH) was utilized to obtain the most stable peptide S7 configuration (Ac-Ser-Ser-Phe-Pro-Gln-Pro-Asn-CONH2) adsorbed on Au(111) facet. After the most stable S7 configuration was found, molecular dynamics (MD) simulation was conducted to investigate the thermal stability between S7 and Au facet at 300 K in both vacuum and water environment. Moreover, further design sets of peptide sequences on Au(111) facet were used to compare with S7. All molecular simulations were carried out by the large-scale atomic/molecular massively parallel simulator (LAMMPS). The Amber99sb-ILDN force field was employed for modeling the interatomic interaction of peptides, and the TIP3P water was used for the water environment. The CHARMM-METAL force field was introduced to model the S7, PF8 (Ac-Pro-Phe-Ser-Pro-Phe-Ser-Pro-Phe-CONH2) and FS8 (Ac-Phe-Ser-Phe-Ser-Phe-Ser-Phe-Ser-CONH2) interactions with Au(111). The MD simulation results demonstrate that the morphology of Pro affects the adsorption stability of Phe. Therefore, we designed two sequences, PF8 and FS8, to confirm our simulation result through experiment. The present study also develops a novel low-temperature plasma synthesis method to evaluate the facet selecting performance of the designed peptide sequences of S7, PF8, and FS8. The experimental results suggest that the reduced Au atom seed is captured with the designed peptide sequences and slowing growing under room temperature for 72 hours. The experimental results are in the excellent agreement with the simulation finding that the Pro in the designed peptide sequences plays a critical role in the facet selection for Au atom stacking.

Chemical functionality of multidomain peptide hydrogels governs early host immune response

Multidomain Peptide (MDP) hydrogels are nanofibrous materials with many potential biomedical applications. The peptide sequence design of these materials offers high versatility and allows for the incorporation of various chemical functionalities into the nanofibrous scaffold. It is known that host response to biomaterials is strongly affected by factors such as size, shape, stiffness, and chemistry. However, there is a lack of fundamental understanding of the host response to different MDP hydrogels. In particular, it is unknown what effect the chemical functionality displayed on the nanofiber has on biological activity. Here we evaluated the early inflammatory host response to four MDP hydrogels displaying amines, guanidinium ions, and carboxylates in a subcutaneous injection model. While all the studied peptide materials possess similar nanostructure and physical properties, they trigger markedly different inflammatory responses. These were characterized by immunophenotyping of the cellular infiltrate using multi-color flow cytometry. The negatively-charged peptides elicit minimal inflammation characterized by tissue-resident macrophage infiltration, fast remodeling, and no collagen deposition or blood vessel formation within the implants. In contrast, the positively-charged peptides are highly infiltrated by immune cells, are remodeled at a slower rate, promote angiogenesis, and result in a high degree of collagen deposition. The presence of dynamic cell phenotypes characterizes the inflammation caused by the lysine-based peptide, including inflammatory monocytes, macrophages, and lymphoid cells, which is seen to be resolving over time. The arginine-based hydrogel shows higher inflammatory response with a persistent and significant infiltration of polymorphonuclear myeloid-derived cells, even ten days after implantation. This understanding of the immune response to peptide biomaterials improves our ability to design effective materials and to tailor their use for specific biomedical applications.

Molecular dynamics simulation of the interaction of HLL peptide and 2D materials with individual residue resolution

Two dimensional (2D) transition metal dichalcogenide (TMD) materials, such as MoS2, show high sensitivity as transducers in bioelectronic molecular sensors. Functionalization for the detection of specific molecules in mixtures of many is, however, still a challenge. Peptides are effective for the functionalization of transducers for molecular sensing and to increase materials’ biocompatibility. In addition, peptides can be utilized as biomolecular scaffolding between molybdenum disulfide (MoS2) and potential substrate materials to enable a new and novel large-area printing technique as demonstrated in an earlier work and studied here. In this study, classical molecular dynamics is used to characterize the binding of a model peptide sequence (HLLQPTQNPFRN), referred to hereafter as HLL, to 2D surfaces. Particularly, the experimental observation of model peptides acting as bridging moieties between two surfaces (MoS2 and silicon oxide in the case of HLL) is confirmed and explained by the selective binding of individual residues to each surface. The HLL strand was simulated in an aqueous environment in the vicinity of MoS2, graphene, and amorphous silica and determined to selectively bind to the MoS2 surface. This preferential binding to MoS2 was quantified by computing the enthalpy of HLL binding to the three surfaces, which resulted in a larger value for MoS2 relative to the other surfaces. The energy of interaction of individual amino-acid residues with each surface was evaluated by summing up the electrostatic interaction energy of each atom in the residue and the surface. This key study shows that some of the residues that strongly bind to graphene or MoS2 are found within the bulk water, away from the silica surface. Conversely, some residues that were found to bind to silica do not show equal affinity to graphene and/or MoS2. A set of calculations featuring MoS2/HLL/silica were also conducted to demonstrate the dual binding feature of this peptide. The ability to characterize the relative strength of individual residues, rather than just entire peptide chains, will reduce trial and error when designing peptides to interact with particular surfaces as residues can be selected one by one based on their binding strength to the surface. The simulations reported here are validated by comparing with recent experiments, for which this work is able to provide additional insights. Thus the approach utilized here can be adopted to identify and characterize the interaction strength of each residue in order to design peptide sequences for optimal binding.

Synthesis of Human Milk Oligosaccharides: Protein Engineering Strategies for Improved Enzymatic Transglycosylation.

Human milk oligosaccharides (HMOs) signify a unique group of oligosaccharides in breast milk, which is of major importance for infant health and development. The functional benefits of HMOs create an enormous impetus for biosynthetic production of HMOs for use as additives in infant formula and other products. HMO molecules can be synthesized chemically, via fermentation, and by enzymatic synthesis. This treatise discusses these different techniques, with particular focus on harnessing enzymes for controlled enzymatic synthesis of HMO molecules. In order to foster precise and high-yield enzymatic synthesis, several novel protein engineering approaches have been reported, mainly concerning changing glycoside hydrolases to catalyze relevant transglycosylations. The protein engineering strategies for these enzymes range from rationally modifying specific catalytic residues, over targeted subsite −1 mutations, to unique and novel transplantations of designed peptide sequences near the active site, so-called loop engineering. These strategies have proven useful to foster enhanced transglycosylation to promote different types of HMO synthesis reactions. The rationale of subsite −1 modification, acceptor binding site matching, and loop engineering, including changes that may alter the spatial arrangement of water in the enzyme active site region, may prove useful for novel enzyme-catalyzed carbohydrate design in general.

Biomimetic Peptide Nanowires Designed for Conductivity

The filamentous peptide-based nanowires produced by some dissimilatory metal-reducing bacteria, such as Geobacter sulfurreducens, display excellent natural conductivity. Their mechanism of conduction is assumed to be a combination of delocalized electrons through closely aligned aromatic amino acids and hopping/charge transfer. The proteins that form these microbial nanowires are structured from a coiled-coil, for which the design rules have been reported in the literature. Furthermore, at least one biomimetic system using related synthetic peptides has shown that the incorporation of aromatic residues can be used to enhance conductivity of peptide fibers. Herein, the de novo design of peptide sequences is used to enhance the conductivity of peptide gels, as inspired by microbial nanowires. A critical factor hampering investigations in both microbiology and materials development is inconsistent reporting of biomaterial conductivity measurements, with consistent methodologies needed for such investigations. We have reported a method herein to analyze non-Ohmic behavior using existing parameters, which is a statistically insightful approach for detecting small changes in biologically based samples. Aromatic residues were found to contribute to peptide gel conductivity, with the importance of the peptide confirmation and fibril assembly demonstrated both experimentally and computationally. This is a small step (in combination with parallel research under way by other researchers) toward developing effective peptide-based conducting nanowires, opening the door to the use of electronics in water and physiological environments for bioelectronic and bioenergy applications.

Living Bacteria-Nanoparticle Hybrids Mediated through Surface-Displayed Peptides.

In this study, we investigated the preparation of living bacteria-nanoparticle hybrids mediated by surface-displayed peptides. The assembly of metallic nanoparticles on living bacteria has been achieved under mild conditions utilizing metal-peptide interactions, whereas the viability of the bacterial cells was greatly preserved. Escherichia coli was engineered with inducible gene circuits to control the display of peptides with desired sequences. Several designed peptide sequences as well as known gold-binding peptides were expressed on the cell surface using enhanced circularly permuted outer membrane protein X (eCPX) scaffolds. Driven by metal-peptide affinity, "biofriendly" citrate-stabilized gold nanoparticles were self-assembled onto the surface of bacteria with displayed peptides, which required overcoming the repulsive force between negatively charged nanoparticles and negatively charged cells. The bacteria/Au nanoparticle hybrids were highly viable and maintained the ability to grow and divide, which is a crucial step toward the creation of living material systems. Further activity and preservation of the bacterial hybrid assembly was demonstrated. The method described herein enables the conjugation of bacterial surfaces with diverse metal-rich nanoparticles in an inducible, and therefore easily controlled, manner. The expressed peptide sequences can be easily modified to alter the binding affinity and specificity for a wide variety of materials to form on-demand, high-density living biohybrids.