Optical bioelectronic nose of outstanding sensitivity and selectivity toward volatile organic compounds implemented with genetically engineered bacteriophage: Integrated study of multi-scale computational prediction and experimental validation

Specific interaction between the DSPHTELP peptide and various functional groups

M13 bacteriophages serve as a versatile foundation for nanobiotechnology due to their unique biological and chemical properties. The polypeptides that comprise their coat proteins, specifically pVIII, can be precisely tailored through genetic engineering. This enables the customized integration of various functional elements through specific interactions, leading to the development of innovative hybrid materials for applications such as energy storage, biosensing, and catalysis. Notably, a certain genetically engineered M13 bacteriophage variant, referred to as DSPH, features a pVIII with a repeating DSPHTELP peptide sequence. This sequence facilitates specific adhesion to single-walled carbon nanotubes (SWCNTs), primarily through π-π and hydrophobic interactions, though the exact mechanism remains unconfirmed. In this study, we synthesized the DSPHTELP peptide (an 8-mer peptide) and analyzed its interaction forces with different functional groups across various pH levels using surface forces apparatus (SFA). Our findings indicate that the 8-mer peptide binds most strongly to CH3 groups (Wad = 13.74 ± 1.04 mJ m-2 at pH 3.0), suggesting that hydrophobic interactions are indeed the predominant mechanism. These insights offer both quantitative and qualitative understanding of the molecular interaction mechanisms of the 8-mer peptide and clarify the basis of its specific interaction with SWCNTs through the DSPHTELP M13 bacteriophage.

Sensor development for multiple simultaneous classifications using genetically engineered M13 bacteriophages

Sensors for detecting infinitesimal amounts of chemicals in air have been widely developed because they can identify the origin of chemicals. These sensing technologies are also used to determine the variety and freshness of fresh food and detect explosives, hazardous chemicals, environmental hormones, and diseases using exhaled gases. However, there is still a need to rapidly develop portable and highly sensitive sensors that respond to complex environments. Here, we show an efficient method for optimising an M13 bacteriophage-based multi-array colourimetric sensor for multiple simultaneous classifications. Apples, which are difficult to classify due to many varieties in distribution, were selected for classifying targets. M13 was adopted to fabricate a multi-array colourimetric sensor using the self-templating process since a chemical property of major coat protein p8 consisting of the M13 body can be manipulated by genetic engineering to respond to various target substances. The twenty sensor units, which consisted of different types of manipulated M13, exhibited colour changes because of the change of photonic crystal-like nanostructure when they were exposed to target substances associated with apples. The classification success rate of the optimal sensor combinations was achieved with high accuracy for the apple variety (100%), four standard fragrances (100%), and aging (84.5%) simultaneously. We expect that this optimisation technique can be used for rapid sensor development capable of multiple simultaneous classifications in various fields, such as medical diagnosis, hazardous environment monitoring, and the food industry, where sensors need to be developed in response to complex environments consisting of various targets.

Development of a palm-sized bioelectronic sensing device for protein detection in milk samples

The present study relates a portable optical sensing device supported by a small single-board (SBC) computer. The electronic architectural avenue connects the SBC with a camera, LED lights and a monitor. A 'sensor integration unit' has been linked with the device where the biological reactions were performed and assessed based on the concentration-dependent optical signal outputs. This setup can detect the generation of colors and distinguish their changes in the RGB intensity scale with an accuracy of a single pixel unit. A predefined range of values was obtained and fed to the device that can quantitatively sense the molecule of interest on the sensing matrix. The device has a touchscreen interactive panel that allows users to manually set experimental conditions and connect the entire measurement process to the cloud storage for backup information. We have considered detecting Alkaline Phosphatase (ALP) quantitatively from standard solutions as well as in milk samples as a proof-of-concept protein molecule. The device has shown exceptional analytical performance for lower and higher concentration ranges (0-100 U/mL and 100-1000 U/mL) with correlation coefficient values of 0.99. The detection limit of ALP was determined to be 0.1 U/mL, and the average time of a sample assessment was recorded to be 15 s. The device has also been tested against ALP-spiked milk samples to check its effectiveness and commercial viability. The outcome of the real-time assessment was sensitive and efficient, indicating its direct commercial and clinical importance towards colorimetric detection for diverse macromolecules.

Artificial Olfactory Biohybrid System: An Evolving Sense of Smell

The olfactory system can detect and recognize tens of thousands of volatile organic compounds (VOCs) at low concentrations in complex environments. Bioelectronic nose (B-EN), which mimics olfactory systems, is becoming an emerging sensing technology for identifying VOCs with sensitivity and specificity. B-ENs integrate electronic sensors with bioreceptors and pattern recognition technologies to enable medical diagnosis, public security, environmental monitoring, and food safety. However, there is currently no commercially available B-EN on the market. Apart from the high selectivity and sensitivity necessary for volatile organic compound analysis, commercial B-ENs must overcome issues impacting sensor operation and other problems associated with odor localization. The emergence of nanotechnology has provided a novel research concept for addressing these problems. In this work, the structure and operational mechanisms of biomimetic olfactory systems are discussed, with an emphasis on the development and immobilization of materials. Various biosensor applications and current developments are reviewed. Challenges and opportunities for fulfilling the potential of artificial olfactory biohybrid systems in fundamental and practical research are investigated in greater depth.

The development progress of multi-array colourimetric sensors based on the M13 bacteriophage

Techniques for detecting chemicals dispersed at low concentrations in air continue to evolve. These techniques can be applied not only to manage the quality of agricultural products using a post-ripening process but also to establish a safety prevention system by detecting harmful gases and diagnosing diseases. Recently, techniques for rapid response to various chemicals and detection in complex and noisy environments have been developed using M13 bacteriophage-based sensors. In this review, M13 bacteriophage-based multi-array colourimetric sensors for the development of an electronic nose is discussed. The self-templating process was adapted to fabricate a colour band structure consisting of an M13 bacteriophage. To detect diverse target chemicals, the colour band was utilised with wild and genetically engineered M13 bacteriophages to enhance their sensing abilities. Multi-array colourimetric sensors were optimised for application in complex and noisy environments based on simulation and deep learning analysis. The development of a multi-array colourimetric sensor platform based on the M13 bacteriophage is likely to result in significant advances in the detection of various harmful gases and the diagnosis of various diseases based on exhaled gas in the future.

Rational Design of Peptides Derived from Odorant-Binding Proteins for SARS-CoV-2-Related Volatile Organic Compounds Recognition

Peptides are promising molecular-binding elements and have attracted great interest in novel biosensor development. In this study, a series of peptides derived from odorant-binding proteins (OBPs) were rationally designed for recognition of SARS-CoV-2-related volatile organic compounds (VOCs). Ethanol, nonanal, benzaldehyde, acetic acid, and acetone were selected as representative VOCs in the exhaled breath during the COVID-19 infection. Computational docking and prediction tools were utilized for OBPs peptide characterization and analysis. Multiple parameters, including the docking model, binding affinity, sequence specification, and structural folding, were investigated. The results demonstrated a rational, rapid, and efficient approach for designing breath-borne VOC-recognition peptides, which could further improve the biosensor performance for pioneering COVID-19 screening and many other applications.

A DNA-derived phage nose using machine learning and artificial neural processing for diagnosing lung cancer

There is a growing interest in electronic nose-based diagnostic systems that are fast and portable. However, existing technologies are suitable only for operation in the laboratory, making them difficult to apply in a rapid, non-face-to-face, and field-suitable manner. Here, we demonstrate a DNA-derived phage nose (D²pNose) as a portable respiratory disease diagnosis system requiring no pretreatment. D²pNose was produced based on phage colour films implanted with DNA sequences from mammalian olfactory receptor cells, and as a result, it possesses the comprehensive reactivity of these cells. The manipulated surface chemistry of the genetically engineered phages was verified through a correlation analysis between the calculated and the experimentally measured reactivity. Breaths from 31 healthy subjects and 31 lung cancer patients were collected and exposed to D²pNose without pretreatment. With the help of deep learning and neural pattern separation, D²pNose has achieved a diagnostic success rate of over 75% and a classification success rate of over 86% for lung cancer based on raw human breath. Based on these results, D²pNose can be expected to be directly applicable to other respiratory diseases.

Neural mechanism mimetic selective electronic nose based on programmed M13 bacteriophage

The electronic nose is a reliable practical sensor device that mimics olfactory organs. Although numerous studies have demonstrated excellence in detecting various target substances with the help of ideal models, biomimetic approaches still suffer in practical realization because of the inability to mimic the signal processing performed by olfactory neural systems. Herein, we propose an electronic nose based on the programable surface chemistry of M13 bacteriophage, inspired by the neural mechanism of the mammalian olfactory system. The neural pattern separation (NPS) was devised to apply the pattern separation that operates in the memory and learning process of the brain to the electronic nose. We demonstrate an electronic nose in a portable device form, distinguishing polycyclic aromatic compounds (harmful in living environment) in an atomic-level resolution (97.5% selectivity rate) for the first time. Our results provide practical methodology and inspiration for the second-generation electronic nose development toward the performance of detection dogs (K9).

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

First-Principles-Based Machine-Learning Molecular Dynamics for Crystalline Polymers with van der Waals Interactions

Machine-learning (ML) techniques have drawn an ever-increasing focus as they enable high-throughput screening and multiscale prediction of material properties. Especially, ML force fields (FFs) of quantum mechanical accuracy are expected to play a central role for the purpose. The construction of ML-FFs for polymers is, however, still in its infancy due to the formidable configurational space of its composing atoms. Here, we demonstrate the effective development of ML-FFs using kernel functions and a Gaussian process for an organic polymer, polytetrafluoroethylene (PTFE), with a data set acquired by first-principles calculations and ab initio molecular dynamics (AIMD) simulations. Even though the training data set is sampled only with short PTFE chains, structures of longer chains optimized by our ML-FF show an excellent consistency with density functional theory calculations. Furthermore, when integrated with molecular dynamics simulations, the ML-FF successfully describes various physical properties of a PTFE bundle, such as a density, melting temperature, coefficient of thermal expansion, and Young's modulus.

Investigation of colorimetric biosensor array based on programable surface chemistry of M13 bacteriophage towards artificial nose for volatile organic compound detection: From basic properties of the biosensor to practical application

Various threats such as explosives, drugs, environmental hormones, and spoiled food manifest themselves with the presence of volatile organic compounds (VOCs) in our environment. In order to recognize and respond to these threats early, the demand for highly sensitive and selective electronic noses is increasing. The M13 bacteriophage-based optoelectronic nose is an excellent candidate to meet all these requirements. However, the phage-based electronic nose is still in its infancy, and strategies that include a systematic approach and development are still essential. Here, we have integrated theoretical and experimental approaches to analyze the correlation between the surface chemistry of genetically engineered phage and the phage-based optoelectronic nose properties. The reactivity of the genetically engineered phage color film to some VOCs were quantitatively analyzed, and the correlation with the binding affinity value calculated by Density-functional theory (DFT) was compared. This demonstrates that phage color films have controllable reactivity through a genetic engineering. We have selected phages that are advantageous in distinguishing each VOCs in this work through hierarchical cluster analysis (HCA). The reason for this difference was verified through the optimized geometry calculated by DFT. Through this, it was confirmed that the tryptophan-based and the Histidine-based of genetically engineered phage film are important in distinguishing the VOCs (Y-hexanolactone, 2-isopropyl-4-methylthiazole, ethanol, acetone, ethyl acetate, and acetaldehyde) used in this work to evaluate the peach freshness quality. This was applied to the design of a field-applied phage-based optoelectronic nose and verified by measuring the freshness of the actual fruit.

Use of Multiple Bacteriophage-Based Structural Color Sensors to Improve Accuracy for Discrimination of Geographical Origins of Agricultural Products

A single M13 bacteriophage color sensor was previously utilized for discriminating the geographical origins of agricultural products (garlic, onion, and perilla). The resulting discrimination accuracy was acceptable, ranging from 88.6% to 94.0%. To improve the accuracy further, the use of three separate M13 bacteriophage color sensors containing different amino acid residues providing unique individual color changes (Wild sensor: glutamic acid (E)-glycine (G)-aspartic acid (D), WHW sensor: tryptophan (W)-histidine (H)-tryptophan (W), 4E sensor: four repeating glutamic acids (E)) was proposed. This study was driven by the possibility of enhancing sample discrimination by combining mutually characteristic and complimentary RGB signals obtained from each color sensor, which resulted from dissimilar interactions of sample odors with the employed color sensors. When each color sensor was used individually, the discrimination accuracy based on support vector machine (SVM) ranged from 91.8–94.0%, 88.6–90.3%, and 89.8–92.1% for garlic, onion, and perilla samples, respectively. Accuracy improved to 98.0%, 97.5%, and 97.1%, respectively, by integrating all of the RGB signals acquired from the three color sensors. Therefore, the proposed strategy was effective for improving sample discriminability. To further examine the dissimilar responses of each color sensor to odor molecules, typical odor components in the samples (allyl disulfide, allyl methyl disulfide, and perillaldehyde) were measured using each color sensor, and differences in RGB signals were analyzed.