Optical discrimination of terpenes in citrus peels with a host:guest sensing array

A simple aqueous host:guest sensing array can selectively discriminate between different types of citrus varietal from peel extract samples. It can also distinguish between identical citrus samples at varying stages of ripening. The discrimination effects stem from detection of changes in the terpenoid composition of the peel extracts by the host:guest array, despite the overwhelming excess of a single component, limonene, in each sample. The hosts are insensitive to limonene but bind other monoterpenes strongly, even though they are similar in structure to the major limonene component. This work demonstrates the capability of host:guest arrays in sensing target molecules in environments with the competing agents present at high abundances in the sample matrix.

Authenticity identification of high - Temperature Daqu Baijiu through multi-channel visual array sensor of organic dyes combined with smart phone App

High - temperature Daqu Baijiu faces a challenge from illegal adulteration of high-grade Baijiu bottles with low-grade Baijiu, affecting its quality and value. This study developed a rapid identification method for high temperature Daqu Baijiu with the same aroma type using a four-channel visual array sensor and detection of color changes caused by competition coordination with Zn2+ and color-changing organic dyes. The array sensor demonstrated high stability and repeatability in targeting flavor components and achieved 97.78 % or more accuracy combined with DD-SIMCA model in detecting adulteration across the Baijiu with same aroma type. The results of GC-MS and Quantum Chemical Calculation showed that esters, acids, and pyrazines played a crucial role. The smart phone App could quickly identify the authenticity of Baijiu with accuracy achieved 93 %. This research provides a foundation for rapid and reliable assessment of Baijiu quality and authenticity, enabling the industry to combat fraudulent practices effectively.

Visual Gustation via Regulable Elastic Photonic Crystals

The unique structural sensitivity of photonic crystals (PCs) endows them with stretchable or elastic tunability for light propagation and spontaneous emission modulation. Hydrogel PCs have been demonstrated to have biocompatibility and flexibility for potential human health detection and environmental security monitoring. However, current elastic PCs still possess a fixed elastic modulus and uncontrollable structural colors based on a tunable elastic modulus, posing considerable challenges for in situ detection, particularly in wearable or portable sensing devices. In this work, we introduced a novel chemo-mechanical transduction mechanism embedded within a photonic crystal nanomatrix, leading to the creation of structural colors and giving rise to a visual gustation sensing experience. By utilizing the captivating structural colors generated by the hydrogel PC, we employ abundant optical information to identify various analytes. The finite element analysis proved the electric field distribution in the PC matrix during stretch operations. The elastic-optical behaviors with various chemical cosolvents, including cations, anions, saccharides, or organic acids, were investigated. The mechanism of the Hofmeister effect regulating the elasticity of hydrogels was demonstrated with the network nanostructure of the hydrogels. The hydrogel PC matrix demonstrates remarkable capability in efficiently distinguishing a wide range of cations, anions, saccharides, and organic acids across various concentrations, mixtures, and even real food samples, such as tastes and soups. Through comprehensive research, a precise relationship between the structural colors and the elastic modulus of hydrogel PCs has been established, contributing to the biomatching elastic-optics platform for wearable devices, a dynamic environment, and clinical or health monitoring auxiliary.

Optical sensor arrays for the detection and discrimination of natural products

Covering: up to the end of 2022Natural products (NPs) have found uses in medicine, food, cosmetics, materials science, environmental protection, and other fields related to our life. Their beneficial properties along with potential toxicities make the detection and discrimination of NPs crucial for their applications. Owing to the merits of low cost and simple operation, optical sensor arrays, including colorimetric and fluorometric sensor arrays, have been widely applied in the detection of small molecule NPs and discrimination of structurally similar small molecule NPs or complex mixtures of NPs. This review provides a brief introduction to the optical sensor array and focuses on its progress toward the detection and discrimination of NPs. We summarized the design principle of sensor arrays toward various NPs (i.e., saccharides and polyhydroxy compounds, organic acids, flavonoids, organic sulfur compounds, amines, amino acids, and saponins) based on their functional groups and characteristic chemical properties, along with representative examples. Moreover, the challenges and potential directions for further research of optical sensor arrays for NPs are proposed.

Data-driven design of a multiplexed, peptide-sensitized transistor to detect breath VOC markers of COVID-19

Exhaled human breath contains a rich mixture of volatile organic compounds (VOCs) whose concentration can vary in response to disease or other stressors. Using simulated odorant-binding proteins (OBPs) and machine learning methods, we designed a multiplex of short VOC- and carbon-binding peptide probes that detect a characteristic "VOC fingerprint". Specifically, we target VOCs associated with COVID-19 in a compact, molecular sensor array that directly transduces vapor composition into multi-channel electrical signals. Rapidly synthesizable, chimeric VOC- and solid-binding peptides were derived from selected OBPs using multi-sequence alignment with protein database structures. Selective peptide binding to targeted VOCs and sensor surfaces was validated using surface plasmon resonance spectroscopy and quartz crystal microbalance. VOC sensing was demonstrated by peptide-sensitized, exposed-channel carbon nanotube transistors. The data-to-device pipeline enables the development of novel devices for non-invasive monitoring, diagnostics of diseases, and environmental exposure assessment.

A simple array integrating machine learning for identification of flavonoids in red wines

Bioactive flavonoids, the major ingredients of red wines, have been proven to prevent atherosclerosis and cardiovascular disease due to their anti-inflammatory and anti-oxidant activity. However, flavonoids have proven challenging to identify, even when multiple approaches are combined. Hereby, a simple array was constructed to detect flavonoids by employing phenylboronic acid modified perylene diimide derivatives (PDIs). Through multiple non-specific interactions (hydrophilic, hydrophobic, charged, aromatic, hydrogen-bonded and reversible covalent interactions) with flavonoids, the fluorescence of PDIs can be modulated, and variations in intensity can be used to create fingerprints of flavonoids. This array successfully discriminated 14 flavonoids of diverse structures and concentrations with 100% accuracy, based on patterns in fluorescence intensity modulation, via optimized machine learning algorithms. As a result, this array demonstrated the parallel detection of 8 different types and origins of red wines with a high accuracy, revealing the excellent potential of the sensor array in food mixtures detection.

A Rainbow Structural Color by Stretchable Photonic Crystal for Saccharide Identification

Photonic crystals (PCs) with fascinating structural color nanomaterials present effectively spontaneous emission modulation and selectively optical signal amplification. Stretchability or elasticity could enable the feasible tunability for structural colors. Aimed at the regulation of structural colors, we endeavored to achieve the PC nanomatrix evolution and optical property during stretching. In this work, a rainbow structural color by stretchable PCs was exploited to provide abundant optical information for multianalyte recognition. The finite element analysis proved the electric field distribution in the PC matrix, which completely matched with the phenomenon of the measured PC spectra. By simply employing analysis of the multistate PC during stretching, the mono PC matrix chip can differentially enhance fluorescence signals in broad spectral regions, resulting in diverse sensing information for high-efficiency multianalysis. The stretchable PC chip can facilely discriminate 14 similar structured saccharides with a minimum concentration of 10^-7 M using only one fluorescence complex. Furthermore, saccharides in different concentrations, mixtures, and real samples (beverages and sweets) also can be successfully distinguished. The exploration on fluorescent stretch dependence behavior of the photonic crystal contributes the biomatching optical platform for wearable devices, dynamic environment, clinical, or health monitoring auxiliary.

A facile way to construct sensor array library via supramolecular chemistry for discriminating complex systems

Differential sensing, which discriminates analytes via pattern recognition by sensor arrays, plays an important role in our understanding of many chemical and biological systems. However, it remains challenging to develop new methods to build a sensor unit library without incurring a high workload of synthesis. Herein, we propose a supramolecular approach to construct a sensor unit library by taking full advantage of recognition and assembly. Ten sensor arrays are developed by replacing the building block combinations, adjusting the ratio between system components, and changing the environment. Using proteins as model analytes, we examine the discriminative abilities of these supramolecular sensor arrays. Then the practical applicability for discriminating complex analytes is further demonstrated using honey as an example. This sensor array construction strategy is simple, tunable, and capable of developing many sensor units with as few syntheses as possible.

Multiplexing the Quantitation of MAP Kinase Activities Using Differential Sensing

Protein kinases are therapeutic targets for many human diseases, but the lack of user-friendly quantitative assays limits the ability to follow the activities of numerous kinases at once (multiplexing). To develop such an assay, we report an array of sulfonamido-oxine (SOX)-labeled peptides showing cross-reactivity to different mitogen-activated protein kinases (MAPKs) for use in a differential sensing scheme. We first verified using linear discriminant analysis that the array could differentiate MAPK isoforms. Then, using principal component analysis, the array was optimized based on the discrimination imparted by each SOX-peptide. Next, the activity of individual MAPK families in ternary mixtures was quantified by support vector machine regression. Finally, we multiplexed the quantification of three MAPK families using partial least squares regression in A549 cell lysates, which has possible interference from other kinase classes. Thus, our method simultaneously quantifies the activity of multiple kinases. The technique could be applied to other protein kinase families and the monitoring of diseases.

Self-healable and anti-freezing ion conducting hydrogel-based artificial bioelectronic tongue sensing toward astringent and bitter tastes

Numerous efforts have been attempted to mimic human tongue since years. However, they still have limitations because of damages, temperature effects, detection ranges etc. Herein, a self-healable hydrogel-based artificial bioelectronic tongue (E-tongue) containing mucin as a secreted protein, sodium chloride as an ion transporting electrolyte, and chitosan/poly(acrylamide-co-acrylic acid) as the main 3D structure holding hydrogel network is synthesized. This E-tongue is introduced to mimic astringent and bitter mouth feel based on cyclic voltammetry (CV) measurements subjected to target substances, which permits astringent tannic acid (TA) and bitter quinine sulfate (QS) to be detected over wide corresponding ranges of 29.3 mM-0.59 μM and 63.8 mM-6.38 μM with remarkable respective sensitivities of 0.2 and 0.12 wt%^-1. Besides, the taste selectivity of this E-tongue is performed in the presence of various mixed-taste chemicals to show its high selective behavior toward bitter and astringent chemicals. The electrical self-healability is shown via CV responses to illustrate electrical recovery within a short time span. In addition, cytotoxicity tests using HeLa cells are performed, where a clear viability of ≥95% verified its biocompatibility. The anti-freezing sensing of E-tongue tastes at -5 °C also makes this work to be useful at sub-zero environments. Real time degrees of tastes are detected using beverages and fruits to confirm future potential applications in food taste detections and humanoid robots.

Student Perceptions of Authoring a Publication Stemming from a Course-Based Undergraduate Research Experience (CURE)

Course-based undergraduate research experiences (CUREs) engage students in authentic research experiences in a course format and can sometimes result in the publication of that research. However, little is known about student-author perceptions of CURE publications. In this study, we examined how students perceive they benefit from authoring a CURE publication and what they believe is required for authorship of a manuscript in a peer-reviewed journal. All 16 students who were enrolled in a molecular genetics CURE during their first year of college participated in semistructured interviews during their fourth year. At the time of the interviews, students had been authors of a CURE publication for a year and a half. Students reported that they benefited personally and professionally from the publication. Students had varying perceptions of what is required for authorship, but every student thought that writing the manuscript was needed, and only two mentioned needing to approve the final draft. Additionally, we identified incomplete conceptions that students had about CURE publications. This work establishes student-perceived benefits from CURE publications and highlights the need for authorship requirements to be explicitly addressed in CUREs.

Discrimination and Quantitation of Biologically Relevant Carboxylate Anions Using A [Dye•PAMAM] Complex

Carboxylate anions are analytical targets with environmental and biological relevance, whose detection is often challenging in aqueous solutions. We describe a method for discrimination and quantitation of carboxylates in water buffered to pH 7.4 based on their differential interaction with a supramolecular fluorescent sensor, self-assembled from readily available building blocks. A fifth-generation poly(amidoamine) dendrimer (PAMAM G5), bound to organic fluorophores (calcein or pyranine) through noncovalent interactions, forms a [dye•PAMAM] complex responsive to interaction with carboxylates. The observed changes in absorbance, and in fluorescence emission and anisotropy, were interpreted through linear discriminant analysis (LDA) and principal component analysis (PCA) to differentiate 10 structurally similar carboxylates with a limit of discrimination around 100 μM. The relationship between the analytes’ chemical structures and the system’s response was also elucidated. This insight allowed us to extend the system’s capabilities to the simultaneous identification of the nature and concentration of unknown analytes, with excellent structural identification results and good concentration recovery, an uncommon feat for a pattern-based sensing system.

Network analysis on Fourier-transform infrared (FTIR) spectroscopic data sets in an Eigen space layout: Introducing a novel approach for analysing wine samples

In the present work, Eigen-directed network analysis for Fourier-transform infrared (FTIR) spectroscopic data sets of wine samples was introduced. A network can generally be viewed as a collection of nodes connected to each other through links, often also called edges. Herein, each node in the network represents a sample and the dissimilarity weight associated with the difference between the two connected nodes is described by the edge. The utility of the approach was tested by analysing a collection of 148 wine samples. The networking on FTIR data sets of these samples in the Eigen space layout was found to impart required aesthetic values as well as the chemical significance to the nodes positioning. The proposed approach successfully captured the compositional differences among the analysed wine samples and classified them in two groups. The Eigen-directed network analysis also allowed a swift assessment regarding inter- and intra-group homogeneity. Homogeneous groups were found to contain nodes with high degree of adjacency and edges with smaller lengths. In comparative study, the proposed approach was found to outperform the network analysis in force-directed layout and principal component analysis. In summary, the proposed Eigen-directed network analysis provided a simplified illustration of highly correlated spectral data sets enabling a swift and intuitive interpretation.

Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage

Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. 'Pinot noir' wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D ¹H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.

Supramolecular Sensor for Astringent Procyanidin C1: Fluorescent Artificial Tongue for Wine Components

An artificial tongue that detects astringent components for a comprehensive evaluation of taste has not been established to date. Herein, we first propose fluorescent polythiophene (PT) derivatives (S1-S3) modified with 3-pyridinium boronic acid as supramolecular chemosensors for wine components including astringent procyanidin C1. After numerous attempts for the synthetic conditions, more than 95 mol % of the PT unit was modified with the pyridinium boronic acid moiety. To evaluate the PT derivatives as chemosensors of the artificial tongue, qualitative and quantitative analyses were performed with four types of wine components (i.e., sweet, sour, bitter, and astringent tastes) in combination with pattern recognition models. Notably, procyanidin C1 in the actual wine sample was successfully detected in a quantitative manner. In other words, we have established an authentic artificial tongue using PT based supramolecular chemosensors.

Disposable paper strips for carboxylate discrimination

A fluorescent macromolecular chemosensor, built from readily available components, performs chemical fingerprinting of carboxylate anions on low-cost, disposable paper supports.

Multichannel DNA Sensor Array Fingerprints Cell States and Identifies Pharmacological Effectors of Catabolic Processes

Cells at disease onset are often associated with subtle changes in the expression level of a single or few molecular components, making traditionally used biomarker-driven clinical diagnosis a challenging task. We demonstrate here the design of a DNA nanosensor array with multichannel output that identifies the normal or pathological state of a cell based on the alteration of its global proteomic signature. Fluorophore-encoded single-stranded DNA (ssDNA) strands were coupled via supramolecular interaction with a surface-functionalized gold nanoparticle quencher to generate this integrated sensor array. In this design, ssDNA sequences exhibit dual roles, where they provide differential affinities with the receptor gold nanoparticle as well as act as transducer elements. The unique interaction mode of the analyte molecules disrupts the noncovalent supramolecular complexation, generating simultaneous multichannel fluorescence output to enable signature-based analyte identification via a linear discriminant analysis-based machine learning algorithm. Different cell types, particularly normal and cancerous cells, were effectively distinguished using their fluorescent fingerprints. Additionally, this DNA sensor array displayed excellent sensitivity to identify cellular alterations associated with chemical modulation of catabolic processes. Importantly, pharmacological effectors, which could modulate autophagic flux, have been effectively distinguished by generating responses from their global protein signatures. Taken together, these studies demonstrate that our multichannel DNA nanosensor is well suited for rapid identification of subtle changes in a complex mixture and thus can be readily expanded for point-of-care clinical diagnosis, high-throughput drug screening, or predicting the therapeutic outcome from a limited sample volume.

A Photochromic Sensor Microchip for High-Performance Multiplex Metal Ion Detection

Photochromic molecules can respond to external stimulations and undergo reversible conversion between different chemical structures, providing one photochromic molecule with multiple recognition states for targeting compounds. Here we design a facile sensor microchip with only one photochromic molecule (spirooxazine) to discriminate multiplex metal ions. The sensor chip performs in dark, ultraviolet, or visual stimulation, resulting in different molecular states of spirooxazine-metallic coordination and patterned fluorescent signals for analysis. By using this sensor microchip, 11 metal ions are discriminated. Furthermore, mineral water of 16 different brands and metal ions in human serum are distinguished.

Differentiation and classification of RNA motifs using small molecule-based pattern recognition

Understanding how to design small molecules that target coding and non-coding RNA has the potential to exponentially increase the number of therapeutically-relevant druggable targets, which are currently mostly proteins. However, there is limited information on the principles at the basis of RNA recognition. In this chapter, we describe a pattern-based technique that can be used for the simultaneous elucidation of RNA motifs and small molecule features for RNA selective recognition, termed Pattern Recognition of RNA by Small Molecules (PRRSM). We provide protocols for the computational design and synthetic preparation of an RNA training set as well as how to perform the assay in plate reader format. Furthermore, we provide details on how to perform and interpret the statistical analysis and indicate possible future extensions of the technique. By combining insights into characteristics of the small molecules and of the RNA that leads to differentiation, PRRSM promises to accelerate the elucidation of the determinants at the basis of RNA recognition.

Chemometric assisted Fourier Transform Infrared (FTIR) Spectroscopic analysis of fruit wine samples: Optimizing the initialization and convergence criteria in the non-negative factor analysis algorithm for developing a robust classification model

The present work proposes certain optimization in the non-negative factor analysis (NNFA) algorithm to ensure an efficient analysis of the Fourier transformation infrared (FTIR) spectral data sets of the fruit wine samples. The first optimization deals with initialization of the variables in a controlled fashion that would ensure a reasonably good quality initial estimate to implement NNFA algorithm. It prevents NNFA algorithm from itinerating with random numbers that essentially have no chemical relevance. The second implemented optimization involves eliminating the alternate least square of convergence and allowing the algorithm to iterate until the iteration limit is reached. This criterion avoids the algorithm to have premature convergence and ensures that model provide the solutions which corresponds to the global minima. The application of NNFA with suggested optimizations are found to capture the subtle differences in the spectral profiles and classify the fruit wine samples that are essentially complex mixtures of several chemicals in unknown proportions. The proposed approach is also found to perform better than principal component analysis on practical grounds. In summary, the current work provides a simple, sensitive and cost-effective approach using optimized NNFA and FTIR spectroscopy for classifying the fruit wine samples.

Upconversion fluorescent nanoparticles based-sensor array for discrimination of the same variety red grape wines

A fluorescent sensor array composed of upconversion nanomaterials to distinguish the same variety of red grape wines was constructed.

RNA Structural Differentiation: Opportunities with Pattern Recognition

Our awareness and appreciation of the many regulatory roles of RNA have dramatically increased in the past decade. This understanding, in addition to the impact of RNA in many disease states, has renewed interest in developing selective RNA-targeted small molecule probes. However, the fundamental guiding principles in RNA molecular recognition that could accelerate these efforts remain elusive. While high-resolution structural characterization can provide invaluable insight, examples of well-characterized RNA structures, not to mention small molecule:RNA complexes, remain limited. This Perspective provides an overview of the current techniques used to understand RNA molecular recognition when high-resolution structural information is unavailable. We will place particular emphasis on a new method, pattern recognition of RNA with small molecules (PRRSM), that provides rapid insight into critical components of RNA recognition and differentiation by small molecules as well as into RNA structural features.

Poly(p-phenyleneethynylene)-based tongues discriminate fruit juices

We describe a simple optoelectronic tongue, consisting of a positively charged, fluorescent poly(para-phenyleneethynylene), P2, that reacts to fruit juices, when employed at three different pH-values (pH 3, 7, 13). This minimal tongue identifies and discriminates 14 different commercially available apple juices, 6 different grape juices and 5 different black currant juices from each other. A similar, negatively charged fluorescent polymer, P1, also achieved discrimination, but the analyte concentration had to be increased by a factor of 50. A mixture of black currant juice and red grape juice is identified as red grape juice, for specific combinations of grape and black currant juices. A mixture of red and green grape juice passes as red grape juice in our sensing system when it contains more than 70% of red grape juice. The data were obtained by fluorescence quenching of the conjugated polymers and processed by linear discriminant analysis of the collected data.

Array-Based Protein Sensing Using an Aggregation-Induced Emission (AIE) Light-Up Probe

Protein detection and identification are important for the diagnosis of diseases; however, the development of facile sensing probes still remains challenging. Here, we present an array-based "turn on" protein-sensing platform capable of detecting and identifying proteins using aggregation-induced emission luminogens (AIEgens). The water-soluble AIEgens in which fluorescence was initially turned off showed strong fluorescence in the presence of nanomolar concentrations of proteins via restriction of the intramolecular rotation of the AIEgens. The binding affinities between the AIEgens and proteins were associated with various chemical functional groups on AIEgens, resulting in distinct fluorescent-signal outcomes for each protein. The combined fluorescence outputs provided sufficient information to detect and discriminate proteins of interest by linear discriminant analysis. Furthermore, the array-based sensor enabled classification of different concentrations of specific proteins. These results provide novel insight into the use of the AIEgens as a new type of sensing probe in array-based systems.

Supramolecular functional assemblies: dynamic membrane transporters and peptide nanotubular composites

The fabrication of functional molecular devices constitutes one of the most important current challenges for chemical sciences. The complex processes accomplished by living systems continuously demand the assistance of non-covalent interactions between molecular building blocks. Additionally, these building blocks (proteins, membranes, nucleotides) are also constituted by self-assembled structures. Therefore, supramolecular chemistry is the discipline required to understand the properties of the minimal self-assembled building blocks of living systems and to develop new functional smart materials. In the first part of this feature article, we highlight selected examples of the preparation of supramolecular membrane transporters with special emphasis on the application of dynamic covalent bonds. In the second section of the paper we review recent breakthroughs in the preparation of peptide nanotube hybrids with functional applications. The development of these devices constitutes an exciting process from where we can learn how to understand and manipulate supramolecular functional assemblies.

Sensing the impact of environment on small molecule differentiation of RNA sequences

Using pattern recognition of RNA with small molecules (PRRSM) with fluorescent RNA chemosensors and aminoglycosides, we reveal the impact of changing environmental conditions on the differentiation of a range of RNA structures as well as the ability to predict different sequence/size compositions of five canonical RNA motifs.

D-A type sensor array for differentiation and identification of white wine varieties based on specific solvent effect activated by CT-LE transition

In this work, we synthesize a series of compounds with electron donor (D) and acceptor (A) units. They show general solvent effect in aprotic solvents, suggesting a charge transfer (CT) process. While in protic solvents including water, ethanol and methanol, the spectra exert no polarity-dependence but a remarkable hypochromatic shift together with the fading of CT band. Dynamic analysis implies that intermolecular hydrogen bond will be formed between carboxylic acid and protic solvent, boosting another deactivation pathway that jumps off a bigger energy gap, in other words, favoring the locally excited (LE) state emission. The CT-LE transition involves variations in both absorption and emission spectra, and further poses competition with other mechanisms including activated/restricted intramolecular rotation (IR/RIR). Inspired by the cross-reactivity, we turn our attention to the development of sensor array, in order to identify white wine varieties. The differential spectral responses are recorded, generating multiple factors including absorption wavelength (λ_ab), emission wavelength (λ_em), absorbance (Abs.) and emission intensity (Int.). These factors are processed with principal component analysis (PCA), creating a three-dimensional fingerprint data base for white wines. The data points in the coordinate system are clustered into 10 different groups, demonstrating a clear differentiation of all the white wines. More importantly, as our final test for whether the sensor array can identify the counterfeits, an adulterated liquor sample, which is provided by police officers, is fingerprinted on the three-dimensional diagram. Its canonical factors fall into an area distinct from the adulterated wine, indicating a clear identification.

Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass Spectrometry

Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal-to-noise ratios (S/N). These complex effects can lead to potentially unrepresentative signal intensities and can bias subsequent analyses. Within mass spectrometry-based peptide technologies, the relation between a peptide's amino acid sequence and S/N remains largely nonquantitative. To address this challenge, we present a method to quantify and analyze mass spectrometry S/N of two peptide arrays, and we use this analysis to portray quality of data and to design future arrays for SAMDI mass spectrometry. Our study demonstrates that S/N varies significantly across peptides within peptide arrays, and variation in S/N is attributable to differences of single amino acids. We apply supervised machine learning to predict peptide S/N based on amino acid sequence, and identify specific physical properties of the amino acids that govern variation of this metric. We find low peptide-S/N concordance between arrays, demonstrating that different arrays require individual characterization and that global peptide-S/N relationships are difficult to identify. However, with proper peptide sampling, this study illustrates how machine learning can accurately predict the S/N of a peptide in an array, allowing for the efficient design of arrays through selection of high S/N peptides.

Differentiation and Identification of Cachaça Wood Extracts Using Peptide-Based Receptors and Multivariate Data Analysis

It is becoming increasingly important to differentiate complex mixtures, especially in forensics. Cachaça, the most popular alcoholic beverage in Brazil, is made from distilled and fermented sugar cane juice. It contains a mixture of naturally occurring polyphenols known as tannins, whose composition is dictated by the type of wood used to age the beverage. These tannins can be differentiated in an Indicator Displacement Assay (IDA) using peptide-based ternary sensing ensembles. This investigation demonstrates a technique for fingerprinting the identity of the woods used to age cachaças. Unknown cachaça samples were tested against a training set of Brazilian woods in addition to oaks from different countries. Results obtained from the analysis showed that 62.5% of the samples were correctly identified. Furthermore, four samples anonymously added to the pool of unknowns from the training set were identified with 100% accuracy, emphasizing both the promising results obtained from this method of differentiation and the importance of analyzing same-age samples.

Fingerprinting antibiotics with PAE-based fluorescent sensor arrays

We outline an evolution process for tongue elements composed of poly(p-aryleneethynylene)s (PAE) and detergents, resulting in a chemical tongue (24 elements) that discerns antibiotics. Cross-breeding of this new tongue with tongue elements that consist of simple poly(p-phenyleneethynylene)s (PPE) at different pH-values leads to an enlarged sensor array, composed of 30 elements. This tongue was pruned, employing principal component analysis. We find that a filial tongue featuring three elements from each original array (i.e. a six element tongue) is superior to either of the prior tongues and the composite tongue in the discrimination of structurally different antibiotics. Such a selection process should be general and give an idea how to successfully generate powerful low-selectivity sensor elements and configure them into discriminative chemical tongues.

Small Molecule-Based Pattern Recognition To Classify RNA Structure

Three-dimensional RNA structures are notoriously difficult to determine, and the link between secondary structure and RNA conformation is only beginning to be understood. These challenges have hindered the identification of guiding principles for small molecule:RNA recognition. We herein demonstrate that the strong and differential binding ability of aminoglycosides to RNA structures can be used to classify five canonical RNA secondary structure motifs through principal component analysis (PCA). In these analyses, the aminoglycosides act as receptors, while RNA structures labeled with a benzofuranyluridine fluorophore act as analytes. Complete (100%) predictive ability for this RNA training set was achieved by incorporating two exhaustively guanidinylated aminoglycosides into the receptor library. The PCA was then externally validated using biologically relevant RNA constructs. In bulge-stem-loop constructs of HIV-1 transactivation response element (TAR) RNA, we achieved nucleotide-specific classification of two independent secondary structure motifs. Furthermore, examination of cheminformatic parameters and PCA loading factors revealed trends in aminoglycoside:RNA recognition, including the importance of shape-based discrimination, and suggested the potential for size and sequence discrimination within RNA structural motifs. These studies present a new approach to classifying RNA structure and provide direct evidence that RNA topology, in addition to sequence, is critical for the molecular recognition of RNA.

Fluorogenic sensor platform for the histone code using receptors from dynamic combinatorial libraries

A sensor array has been developed that can differentiate multiple post-translational modifications in the same peptide and their relative positions in the sequence, including multiple methylations, providing a promising new tool for deciphering the histone code.

Post-translational modifications (PTMs) on histone tails act in diverse combinations in the ‘histone code’ to control gene expression, with dysregulation observed in a variety of diseases. However, detection and sensing methods are limited, expensive, and/or low-throughput, including MS and antibody based detection. We found that by combining four synthetic receptors developed by dynamic combinatorial chemistry (DCC) in an indicator displacement system, we are able to create a pattern-based sensor platform that can discriminate single PTMs such as methylation and acetylation on a representative histone peptide with 100% accuracy as well as peptides bearing both dimethyl and trimethyl lysine in the presence of arginine methylation, which has not previously been demonstrated, and can even correctly distinguish the position of lysine methylation individually or in the presence of other PTMs. To extend this approach, a full panel of thirteen analytes containing different combinations of PTMs were classified with 96 ± 1% overall accuracy in a 50% left-out analysis, demonstrating the robustness and versatility of the sensor array. Finally, the sensor platform was also used to demonstrate proof of concept for enzymatic assays by analysing the mock reaction of a threonine kinase, successfully identifying analytes representative of substrate conversion both with and without neighboring PTMs. This work provides a rapid platform for the analysis of peptides bearing complex modifications and highlights the utility of receptors discovered though DCC that display variations in binding affinity and selectivity.

Art, auto-mechanics, and supramolecular chemistry. A merging of hobbies and career

While the strict definition of supramolecular chemistry is "chemistry beyond the molecule", meaning having a focus on non-covalent interactions, the field is primarily associated with the creation of synthetic receptors and self-assembly. For synthetic ease, the receptors and assemblies routinely possess a high degree of symmetry, which lends them an aspect of aesthetic beauty. Pictures of electron orbitals similarly can be seen as akin to works of art. This similarity was an early draw for me to the fields of supramolecular chemistry and molecular orbital theory, because I grew up in a household filled with art. In addition to art, my childhood was filled with repairing and constructing mechanical entities, such as internal combustion motors, where many components work together to achieve a function. Analogously, the field of supramolecular chemistry creates systems of high complexity that achieve functions or perform tasks. Therefore, in retrospect a career in supramolecular chemistry appears to be simply an extension of childhood hobbies involving art and auto-mechanics.

Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors

Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

Next-generation sequencing as input for chemometrics in differential sensing routines

Differential sensing (DS) methods traditionally use spatially arrayed receptors and optical signals to create score plots from multivariate data which classify individual analytes or complex mixtures. Herein, a new approach is described, in which nucleic acid sequences and sequence counts are used as the multivariate data without the necessity of a spatial array. To demonstrate this approach to DS, previously selected aptamers, identified from the literature, were used as semi-specific receptors, Next-Gen DNA sequencing was used to generate data, and cell line differentiation was the test-bed application. The study of a principal component analysis loading plot revealed cross-reactivity between the aptamers. The technique generates high-dimensionality score plots, and should be applicable to any mixture of complex and subtly different analytes for which nucleic acid-based receptors exist.