Hot Spot Mutagenesis Improves the Functional Expression of Unique Mammalian Odorant Receptors

Vertebrate animals detect odors through olfactory receptors (ORs), members of the G protein-coupled receptor (GPCR) family. Due to the difficulty in the heterologous expression of ORs, studies of their odor molecule recognition mechanisms have progressed poorly. Functional expression of most ORs in heterologous cells requires the co-expression of their chaperone proteins, receptor transporting proteins (RTPs). Yet, some ORs were found to be functionally expressed without the support of RTP (RTP-independent ORs). In this study, we investigated whether amino acid residues highly conserved among RTP-independent ORs improve the functional expression of ORs in heterologous cells. We found that a single amino acid substitution at one of two sites (NBW3.39 and 3.43) in their conserved residues (E and L, respectively) significantly improved the functional expression of ORs in heterologous cells. E3.39 and L3.43 also enhanced the membrane expression of RTP-dependent ORs in the absence of RTP. These changes did not alter the odorant responsiveness of the tested ORs. Our results showed that specific sites within transmembrane domains regulate the membrane expression of some ORs.

Fluorescent Chemosensors for Ion and Molecule Recognition: The Next Chapter

Over the past 30 years fluorescent chemosensors have evolved to incorporate many optical-based modalities and strategies. In this perspective we seek to highlight the current state of the art as well as provide our viewpoint on the most significant future challenges remaining in the area. To underscore current trends in the field and to facilitate understanding of the area, we provide the reader with appropriate contemporary examples. We then conclude with our thoughts on the most probable directions that chemosensor development will take in the not-too-distant future.

Energetics of a protein disorder-order transition in small molecule recognition

Many proteins recognise other proteins via mechanisms that involve the folding of intrinsically disordered regions upon complex formation. Here we investigate how the selectivity of a drug-like small molecule arises from its modulation of a protein disorder-to-order transition. Binding of the compound AM-7209 has been reported to confer order upon an intrinsically disordered lid region of the oncoprotein MDM2. Calorimetric measurements revealed that truncation of the lid region of MDM2 increases the dissociation constant of AM-7209 250-fold. By contrast, lid truncation has little effect on the binding of the ligand Nutlin-3a. Insights into these differential binding energetics were obtained via a complete thermodynamic analysis that featured adaptive absolute alchemical free energy of binding calculations with enhanced-sampling molecular dynamics simulations. The simulations reveal that in apo MDM2 the ordered lid state is energetically disfavoured. AM-7209, but not Nutlin-3a, shows a significant energetic preference for ordered lid conformations, thus shifting the balance towards ordering of the lid in the AM-7209/MDM2 complex. The methodology reported herein should facilitate broader targeting of intrinsically disordered regions in medicinal chemistry.

Recent advances of using personal glucose meter as a biosensor readout for non-glucose targets

Background: Personal glucose meter (PGM) has become the most successful biosensor in past decades due to its advantages of small size, convenient operation, and low cost. To take advantage of many years of research and development of PGMs, new signal transduction methods has been developed to expand the PGM from simple monitoring blood glucose to detection of numerous non-glucose targets. Objectives: This review summarizes recent advance of PGM-based biosensors for non-glucose targets including signal transduction, signal amplification and target molecule recognition and analysis. Current challenges and future directions are also discussed. Conclusion: PGM can be used as biosensor readout to detect various non-glucose targets from metal ion, small molecule to protein and even living organisms such as bacteria and other pathogens by using different signal transduction elements such as invertase and amylase, and different signal amplification methods such as nanomaterials, nucleic acid reaction, liposome encapsulation, hydrogel trapping, DNAzyme amplification and biotin-streptavidin reaction.

ChemPix: Automated Recognition of Hand-drawn Hydrocarbon Structures Using Deep Learning

<p>Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The top ensemble model achieved a hand-drawn hydrocarbon recognition accuracy of 77% for the first prediction and 86% if the top 3 predictions were considered; in over 50% of cases, the model was at least 97% confident in the prediction, making it a promising tool for real-world use cases.</p>

ChemPix: Automated Recognition of Hand-drawn Hydrocarbon Structures Using Deep Learning

<p>Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The top ensemble model achieved a hand-drawn hydrocarbon recognition accuracy of 77% for the first prediction and 86% if the top 3 predictions were considered; in over 50% of cases, the model was at least 97% confident in the prediction, making it a promising tool for real-world use cases.</p>

Graphene Oxide/Perylene-Aniline Electrochemiluminescence Platform for Protein Detection Based on Molecule Recognition

Most biosensors for protein folate receptor(FR) detection based on small molecule folic acid(FA) recognition usually introduced FA linked single strand DNA(FA-ssDNA) and nuclease to promote sensitivity, which increased expenses and...

Solid-State Membrane Sensors Based on Man-Tailored Biomimetic Receptors for Selective Recognition of Isoproturon and Diuron Herbicides

Solid-contact ion-selective electrodes (SC-ISEs) have shown great potential for routine and portable ion detection. The introduction of nanomaterials as ion-to-electron transducers and the adoption of different performance-enhancement strategies have significantly promoted the development of SC-ISEs. Herein, new solid-contact ion-selective electrodes, along with the implementation of multiwalled carbon nanotubes (MWCNTs) as ion-to-electron transducers and potassium tetrakis (p-chlorophenyl) borate (KTpClB) as lipophilic ionic additives, were presented for the detection of isoproturon (IPU) and diuron (DU) herbicides. Molecularly imprinted polymers (MIPs), with special molecule recognition properties for isoproturon (IPU) and diuron (DU), were prepared, characterized, and introduced as sensory recognition materials in the presented electrodes. Sensors revealed a near-Nernstian response for both isoproturon (IPU) and diuron (DU) with slopes of 53.1 ± 1.2 (r2 = 0.997) and 57.2 ± 0.3 (r2 = 0.998) over the linear ranges of 2.2 × 10−6–1.0 × 10−3 M and 3.2 × 10−6–1.0 × 10−3 M with detection limits of 8.3 × 10−7 and 1.4 × 10−6 M, respectively. The response time of the presented sensors was found to be <5 s and the lifetime was at least eight weeks. The sensors exhibited good selectivity towards isoproturon (IPU) and diuron (DU) in comparison with some other herbicides, alkali, alkaline earth, and heavy metal ions. The presented sensors were successfully applied for the direct determination of isoproturon (IPU) and diuron (DU) in real water samples.