Human μ Opioid Receptor Models with Evaluation of the Accuracy Using the Crystal Structure of the Murine μ Opioid Receptor

Models of the human μ opioid receptor were constructed using available G-protein-coupled receptor (GPCR) structures using homology (comparative) modeling techniques. The recent publication of a high-resolution crystal structure of a construct based on the murine μ opioid receptor offers a unique opportunity to evaluate the reliability of the homology models and test the relevance of introducing more templates (known structures) to increase the accuracy of the comparative models. In the first model two templates were used: the β2 adrenergic and bovine rhodopsin receptors. For the second model, four templates were utilized: the β2adrenergic, bovine rhodopsin, β1 adrenergic, and A2A adenosine receptors. Including additional templates improved the accuracy of structural motifs and other features of the model when the same sequence alignment was used. The predicted structures were especially relevant in the case of important receptor regions such as the DRY motif, which has been associated with receptor activation. Additionally, this study showed that receptor sequence similarity is crucial in homology modeling, as indicated in the case of the highly diverse EC2 loop. This study demonstrates the reliability of the homology modeling technique in the case of the μ opioid receptor, a member of the rhodopsin-like family class of GPCRs. The addition of more templates improved the accuracy of the model. The findings regarding the modeling has significant implication to other GPCRs where the crystal structure is still unknown and suggest that homology modeling techniques can provide high quality structural models for interpreting experimental findings and formulating structurally based hypotheses regarding the activity of these important receptors

Binding Site and Affinity Prediction of General Anesthetics to Protein Targets Using Docking

BACKGROUND: The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explored whether a computational method, AutoDock, could serve as such a tool. METHODS: High-resolution crystal data of water-soluble proteins (cytochrome C, apoferritin, and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus [GLIC]) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (http://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants were compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent cocrystallization data. Docking calculations for 6 general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known 50% effective concentration (EC50) values were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC50 values and octanol/water partition coefficients for the 6 general anesthetics. RESULTS: All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (P = 0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the extracellular domain of GLIC. The predicted affinities correlated significantly with the known EC50 values for the 6 frequently used anesthetics in GLIC for the site identified in the experimental crystal data (P = 0.006). However, predicted affinities in apoferritin, human serum albumin, and cytochrome C did not correlate with these 6 anesthetics\u27 known experimental EC50values. A weak correlation between the predicted affinities and the octanol/water partition coefficients was observed for the sites in GLIC. CONCLUSION: We demonstrated that anesthetic binding sites and relative affinities can be predicted using docking calculations in an automatic docking server (AutoDock) for both water-soluble and membrane proteins. Correlation of predicted affinity and EC50 for 6 frequently used general anesthetics was only observed in GLIC, a member of a protein family relevant to anesthetic mechanism

Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

We investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise. To better distinguish these noise types and utilize their semantics, we propose a novel sample selection-based approach for noisy label learning, called Proto-semi. Proto-semi initially divides all samples into the confident and unconfident datasets via warm-up. By leveraging the confident dataset, prototype vectors are constructed to capture class characteristics. Subsequently, the distances between the unconfident samples and the prototype vectors are calculated to facilitate noise classification. Based on these distances, the labels are either corrected or retained, resulting in the refinement of the confident and unconfident datasets. Finally, we introduce a semi-supervised learning method to enhance training. Empirical evaluations on a real-world annotated dataset substantiate the robustness of Proto-semi in handling the problem of learning from noisy labels. Meanwhile, the prototype-based repartitioning strategy is shown to be effective in mitigating the adverse impact of label noise. Our code and data are available at https://github.com/fuxiAIlab/ProtoSemi

Scalable Production of Highly-Sensitive Nanosensors Based on Graphene Functionalized with a Designed G Protein-Coupled Receptor

We have developed a novel, all-electronic biosensor for opioids that consists of an engineered mu opioid receptor protein, with high binding affinity for opioids, chemically bonded to a graphene field-effect transistor to read out ligand binding. A variant of the receptor protein that provided chemical recognition was computationally redesigned to enhance its solubility and stability in an aqueous environment. A shadow mask process was developed to fabricate arrays of hundreds of graphene transistors with average mobility of ~1500 cm2 V-1 s-1 and yield exceeding 98%. The biosensor exhibits high sensitivity and selectivity for the target naltrexone, an opioid receptor antagonist, with a detection limit of 10 pg/mL.Comment: Nano Letters 201

A Computationally Designed Water-Soluble Variant of a G-Protein-Coupled Receptor: The Human Mu Opioid Receptor

G-protein-coupled receptors (GPCRs) play essential roles in various physiological processes, and are widely targeted by pharmaceutical drugs. Despite their importance, studying GPCRs has been problematic due to difficulties in isolating large quantities of these membrane proteins in forms that retain their ligand binding capabilities. Creating water-soluble variants of GPCRs by mutating the exterior, transmembrane residues provides a potential method to overcome these difficulties. Here we present the first study involving the computational design, expression and characterization of water-soluble variant of a human GPCR, the human mu opioid receptor (MUR), which is involved in pain and addiction. An atomistic structure of the transmembrane domain was built using comparative (homology) modeling and known GPCR structures. This structure was highly similar to the subsequently determined structure of the murine receptor and was used to computationally design 53 mutations of exterior residues in the transmembrane region, yielding a variant intended to be soluble in aqueous media. The designed variant expressed in high yield in Escherichia coli and was water soluble. The variant shared structural and functionally related features with the native human MUR, including helical secondary structure and comparable affinity for the antagonist naltrexone (Kd  = 65 nM). The roles of cholesterol and disulfide bonds on the stability of the receptor variant were also investigated. This study exemplifies the potential of the computational approach to produce water-soluble variants of GPCRs amenable for structural and functionally related characterization in aqueous solution

Towards Long-term Annotators: A Supervised Label Aggregation Baseline

Relying on crowdsourced workers, data crowdsourcing platforms are able to efficiently provide vast amounts of labeled data. Due to the variability in the annotation quality of crowd workers, modern techniques resort to redundant annotations and subsequent label aggregation to infer true labels. However, these methods require model updating during the inference, posing challenges in real-world implementation. Meanwhile, in recent years, many data labeling tasks have begun to require skilled and experienced annotators, leading to an increasing demand for long-term annotators. These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works. Hereby, in this paper, we propose a novel label aggregation technique, which does not need any model updating during inference and can extensively explore the historical annotation records. We call it SuperLA, a Supervised Label Aggregation method. Inside this model, we design three types of input features and a straightforward neural network structure to merge all the information together and subsequently produce aggregated labels. Based on comparison experiments conducted on 22 public datasets and 11 baseline methods, we find that SuperLA not only outperforms all those baselines in inference performance but also offers significant advantages in terms of efficiency

Evaluation of Exhaled Nitric Oxide in Thoracic Surgery Patients under One Lung Ventilation Using a Newly Designed Online Exhaled Nitric Oxide Measuring System

Measurement of exhaled nitric oxide (NO) has been gaining much interest lately. However, an ideal measuring system is not yet available in the clinical setting. The aims of the present study were to construct an exhaled NO measuring system and to investigate the effects of one lung ventilation (OLV) on exhaled NO output in patients who underwent thoracic surgery. At first, the NO measuring system was constructed with an NO analyzer, a respiratory flowmeter and a data processing computer system in which the algorithm was indwelled for correcting the distorted NO output wave form. Then, accuracy of this system was tested by using a simulator. This simulator was reworked in order to simulate NO production from the lung under both spontaneous respiration and mechanical ventilation. The data of peak NO concentration (pNO) and NO output (VNO) obtained with the NO monitoring system were significantly correlated with "alveolar" NO concentration (aNO) and exhaled NO volume from the simulator. Then, exhaled NO was measured in 12 thoracic surgery patients who underwent OLV using this system. pNO and VNO were significantly decreased by about half during OLV, and returned to baseline 25 min after releasing OLV. These data suggest that the newly designed online exhaled NO measuring system accurately detected aNO and exhaled NO volume in a breath-by-breath manner, and OLV for about 3 h did not influence the NO output from the lung after releasing OLV in thoracic surgery patients

Novel Molecular Targets of Dezocine and Their Clinical Implications

While dezocine is a partial mu opioid receptor agonist, it is not a controlled substance. Thus, the characterization of the molecular targets of dezocine is critical for scientific and clinical implications. The goal of this study is to characterize molecular targets for dezocine and their implications

Characterization of a Computationally Designed Water-Soluble Human μ Opioid Receptor Variant Using X-ray Structural Information

Background The recent X-ray crystal structure of the murine μ opioid receptor (MUR) allowed us to reengineer a previously designed water-soluble variant of the transmembrane portion of the human MUR (wsMUR-TM). Methods The new variant of water soluble MUR (wsMUR-TM_v2) was engineered based upon the murine MUR crystal structure. This novel variant was expressed in E. coliand purified. The properties of the receptor were characterized and compared with those of wsMUR-TM. Results Seven residues originally included for mutation in the design of the wsMUR-TM, were reverted to their native identities. wsMUR-TM_v2 contains 16% mutations of the total sequence. It was overexpressed and purified with high yield. Although dimers and higher oligomers were observed to form over time, the wsMUR-TM_v2 stayed predominantly monomeric at concentrations as high as 7.5 mg/ml in buffer within a 2-month period. Its secondary structure was predominantly helical and comparable with those of both the original wsMUR-TM variant and the native MUR. The binding affinity of wsMUR-TM_v2 for naltrexone (Kd ~ 70 nM) was in close agreement with that for wsMUR-TM. The helical content of wsMUR-TM_v2 decreased cooperatively with increasing temperature, and the introduction of sucrose was able to stabilize the protein. Conclusions A novel functional wsMUR-TM_v2 with only 16% mutations was successfully engineered, expressed in E. coli and purified based on information from the crystal structure of murine MUR. This not only provides a novel alternative tool for MUR studies in solution conditions, but also offers valuable information for protein engineering and structure function relationships