Computational Design of Membrane Proteins

Membrane proteins are involved in a wide variety of cellular processes, and are typically part of the first interaction a cell has with extracellular molecules. As a result, these proteins comprise a majority of known drug targets. Membrane proteins are among the most difficult proteins to obtain and characterize, and a structure-based understanding of their properties can be difficult to elucidate. Notwithstanding, the design of membrane proteins can provide stringent tests of our understanding of these crucial biological systems, as well as introduce novel or targeted functionalities. Computational design methods have been particularly helpful in addressing these issues and this review discusses recent studies that tailor membrane proteins to display specific structures or functions, and how redesigned membrane proteins are being used to facilitate structural and functional studies

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

Molecular Recognition of Ketamine by a Subset of Olfactory G Protein–coupled Receptors

Ketamine elicits various neuropharmacological effects, including sedation, analgesia, general anesthesia, and antidepressant activity. Through an in vitro screen, we identified four mouse olfactory receptors (ORs) that responded to ketamine. In addition to their presence in the olfactory epithelium, these G protein (heterotrimeric guanine nucleotide–binding protein)–coupled receptors (GPCRs) are distributed throughout the central nervous system. To better understand the molecular basis of the interactions between ketamine and ORs, we used sequence comparison and molecular modeling to design mutations that (i) increased, reduced, or abolished ketamine responsiveness in responding receptors, and (ii) rendered non-responding receptors responsive to ketamine. We showed that olfactory sensory neurons (OSNs) that expressed distinct ORs responded to ketamine in vivo, suggesting that ORs may serve as functional targets for ketamine. The ability to both abolish and introduce responsiveness to ketamine in GPCRs enabled us to identify and confirm distinct interaction loci in the binding site, which suggested a signature ketamine-binding pocket that may guide exploration of additional receptors for this general anesthetic drug

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

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

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

NMR Structure and Dynamics of a Designed Water-Soluble Transmembrane Domain of Nicotinic Acetylcholine Receptor

The nicotinic acetylcholine receptor (nAChR) is an important therapeutic target for a wide range of pathophysiological conditions, for which rational drug designs often require receptor structures at atomic resolution. Recent proof-of-concept studies demonstrated a water-solubilization approach to structure determination of membrane proteins by NMR (Slovic et al., PNAS, 101: 1828–1833, 2004; Ma et al., PNAS, 105: 16537–42, 2008). We report here the computational design and experimental characterization of WSA, a water-soluble protein with ~ 83% sequence identity to the transmembrane (TM) domain of the nAChR α1 subunit. Although the design was based on a low-resolution structural template, the resulting high-resolution NMR structure agrees remarkably well with the recent crystal structure of the TM domains of the bacterial Gloeobacter violaceuspentameric ligand-gated ion channel (GLIC), demonstrating the robustness and general applicability of the approach. NMR T2 dispersion measurements showed that the TM2 domain of the designed protein was dynamic, undergoing conformational exchange on the NMR timescale. Photoaffinity labeling with isoflurane and propofol photolabels identified a common binding site in the immediate proximity of the anesthetic binding site found in the crystal structure of the anesthetic-GLIC complex. Our results illustrate the usefulness of high-resolution NMR analyses of water-solubilized channel proteins for the discovery of potential drug binding sites

Computational Protein Design to Re-Engineer Stromal Cell-Derived Factor-1α (SDF) Generates an Effective and Translatable Angiogenic Polypeptide Analog

BACKGROUND: Experimentally, exogenous administration of recombinant stromal cell-derived factor-1α (SDF) enhances neovasculogenesis and cardiac function after myocardial infarction. Smaller analogs of SDF may provide translational advantages including enhanced stability and function, ease of synthesis, lower cost, and potential modulated delivery via engineered biomaterials. In this study, computational protein design was used to create a more efficient evolution of the native SDF protein. METHODS AND RESULTS: Protein structure modeling was used to engineer an SDF polypeptide analog (engineered SDF analog [ESA]) that splices the N-terminus (activation and binding) and C-terminus (extracellular stabilization) with a diproline segment designed to limit the conformational flexibility of the peptide backbone and retain the relative orientation of these segments observed in the native structure of SDF. Endothelial progenitor cells (EPCs) in ESA gradient, assayed by Boyden chamber, showed significantly increased migration compared with both SDF and control gradients. EPC receptor activation was evaluated by quantification of phosphorylated AKT, and cells treated with ESA yielded significantly greater phosphorylated AKT levels than SDF and control cells. Angiogenic growth factor assays revealed a distinct increase in angiopoietin-1 expression in the ESA- and SDF-treated hearts. In addition, CD-1 mice (n=30) underwent ligation of the left anterior descending coronary artery and peri-infarct intramyocardial injection of ESA, SDF-1α, or saline. At 2 weeks, echocardiography demonstrated a significant gain in ejection fraction, cardiac output, stroke volume, and fractional area change in mice treated with ESA compared with controls. CONCLUSIONS: Compared with native SDF, a novel engineered SDF polypeptide analog (ESA) more efficiently induces EPC migration and improves post-myocardial infarction cardiac function and thus offers a more clinically translatable neovasculogenic therapy

The Repercussions of Business Process Modeling Notations on Mental Load and Mental Effort

Over the last decade, plenty business process modeling notations emerged for the documentation of business processes in enterprises. During the learning of a modeling notation, an individual is confronted with a cognitive load that has an impact on the comprehension of a notation with its underlying formalisms and concepts. To address the cognitive load, this paper presents the results from an exploratory study, in which a sample of 94 participants, divided into novices, intermediates, and experts, needed to assess process models expressed in terms of eight different process modeling notations, i.e., BPMN 2.0, Declarative Process Modeling, eGantt Charts, EPCs, Flow Charts, IDEF3, Petri Nets, and UML Activity Diagrams. The study focus was set on the subjective comprehensibility and accessibility of process models reflecting participant's cognitive load (i.e., mental load and mental effort). Based on the cognitive load, a factor reflecting the mental difficulty for comprehending process models in different modeling notations was derived. The results indicate that established modeling notations from industry (e.g., BPMN) should be the first choice for enterprises when striving for process management. Moreover, study insights may be used to determine which modeling notations should be taught for an introduction in process modeling or which notation is useful to teach and train process modelers or analysts. \keywords{Business Process Modeling Notations, Cognitive Load, Mental Load, Mental Effort, Human-centered Desig