* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download How Opioid Drugs Bind to Receptors
Cognitive neuroscience wikipedia , lookup
Multielectrode array wikipedia , lookup
Neuroinformatics wikipedia , lookup
Neuroeconomics wikipedia , lookup
Brain–computer interface wikipedia , lookup
Neuropsychology wikipedia , lookup
Development of the nervous system wikipedia , lookup
Neuroanatomy wikipedia , lookup
Neural engineering wikipedia , lookup
Neuromuscular junction wikipedia , lookup
Binding problem wikipedia , lookup
Neurotransmitter wikipedia , lookup
Aging brain wikipedia , lookup
NMDA receptor wikipedia , lookup
Metastability in the brain wikipedia , lookup
Stimulus (physiology) wikipedia , lookup
Endocannabinoid system wikipedia , lookup
Molecular neuroscience wikipedia , lookup
Signal transduction wikipedia , lookup
RESEARCH NEWS & VIEWS in which people (the atoms) repeatedly and regularly throw balls (the electrons) vertically into the air and then catch them. Even without seeing the players, one can discern a successful capture by hearing them cheer “Yeah” (photon emission). Now suppose you could blow a crosswind (the probe field) over the heads of the players. If the crosswind blows when the ball is in the air, it pushes the ball sideways. In this case, the ball lands some distance away from its starting point so that the player cannot catch it, and there is no cheer. By applying the crosswind at various times and then listening for the presence or absence of cheers, one can determine the precise throwing times (the ionization times). In their experiments, Shafir et al. used a probe field that oscillated at twice the frequency of the main field, and monitored the intensity of the emitted harmonics as the researchers varied the temporal shift between the two fields (the time elapsing between a maximum of the main field and a maximum of the probe field). But for a robust analysis, they also needed to measure electron departure and return times independently, which meant that they had to take things further. In our analogous game, a crosswind whose direction alternates could affect the ball so much that it unexpectedly hits the player from the side, making him shout “Yikes” instead of “Yeah”. Similarly, Shafir et al. deduced the angle of electron return in their experiments by detecting specific photon emissions known as even-order harmonics. These emissions, whose frequencies are even multiples of the main laser’s frequency, were generated when released electrons hit their parent ions ‘from the side’ — at an angle to the main field. Of particular note, the authors found that every harmonic emission frequency has its own ionization time, all of which fall within a range of 200 attoseconds (1 attosecond is 10−18 s). The superposition of the many different associated electron trajectories forms a quantum-mechanical wave packet — a short ‘pulse’ of travelling wave activity — for emitted electrons. The observed ionization times1 are conceptually different from the extremely small tunnelling delay time (tens of atto seconds at most) reported in a previous study of helium atoms3, which measured the delay between the maximum of the applied oscillating laser field and the most likely time of electron departure. One striking result is the excellent agreement of Shafir and colleagues’ findings with a model of high-harmonic generation that is well known to atomic physicists — the quantum orbit model4. Put simply, the model states that an electron trajectory begins with negative kinetic energy at an instant of time defined by a complex number. Just the imaginary part of time changes for the electron as it tunnels through a potential barrier; time becomes real-valued only at the exit of the tunnel. This real time is the exit time measured by Shafir and colleagues. It is the time at which the electron starts to feel the effect of the probe field. In molecules, high-harmonic generation often involves contributions from different ‘channels’ — that is, not only from the most weakly bound electrons of atoms, but also from more tightly bound ones5,6. Shafir et al.1 report that small differences in ionization times from two channels in carbon dioxide are, in principle, measurable. The characteristic differences are observable when the channels interfere nearly destructively. However, this scenario corresponds to a small range of the emitted spectrum, and generates a low number of photons. Determining reliable, channel-dependent ionization times will therefore be extremely challenging. One limitation of Shafir and colleagues’ study is that they measure only how ionization times and return times vary with harmonic frequency. But the absolute timing of ionization is of substantial interest too, because it is related to the tunnelling delay time and it may influence the absolute timing of the harmonic emission. It remains to be seen how well the authors’ technique will work for mixtures of gases, in which differences between atomic species may complicate harmonic emission profiles. Extending the present study to such a case may reveal interference between emissions from different gases in the same way that different channels in the same molecule interfere with each other. Accurate knowledge of the electron excursion times (the difference between return and ionization times) during high-harmonic generation is vital for our understanding of the many ultrafast experiments5,7 in which ionization triggers a dynamic process, and in which recombination of the resulting ion with the electron takes a snapshot of that process. Unlike the classical approach8 to ultrafast timeresolved chemistry, in which reactions are initiated using a ‘pump’ light pulse and a separate probe pulse is used to monitor reaction evolution, high-harmonic generation combines the pump and probe steps into just one shot. By facilitating the real-time observation of attosecond electron dynamics, this approach will increasingly compete with ultrafast spectroscopic methods in which molecules are directly probed by attosecond light pulses9. ■ Manfred Lein is at the Centre for Quantum Engineering and Space-Time Technology and at the Institute for Theoretical Physics, Leibniz Universität Hannover, 30167 Hanover, Germany. e-mail: [email protected] Shafir, D. et al. Nature 485, 343–346 (2012). Corkum, P. B. Phys. Rev. Lett. 71, 1994–1997 (1993). Eckle, P. et al. Science 322, 1525–1529 (2008). Salières, P. et al. Science 292, 902–905 (2001). Smirnova, O. et al. Nature 460, 972–977 (2009). McFarland, B. K., Farrell, J. P., Bucksbaum, P. H. & Gühr, M. Science 322, 1232–1235 (2008). 7. Baker, S. et al. Science 312, 424–427 (2006). 8. Zewail, A. H. Science 242, 1645–1653 (1988). 9. Krausz, F. & Ivanov, M. Rev. Mod. Phys. 81, 163–234 (2009). 1. 2. 3. 4. 5. 6. STR UC TUR A L B I OLOGY How opioid drugs bind to receptors The search for safe, non-addictive versions of morphine and other opioid drugs has just received a boost with the solving of the crystal structures of the receptors to which the drugs bind. See Articles p.321 & p.327, Letters p.395 & p.400 M A R TA F I L I Z O L A & L A K S H M I A . D E V I O pioid drugs such as morphine and codeine are powerful painkillers, but an assortment of adverse side effects limits their effective medical use. These drugs can also produce pronounced euphoria, which has led to the recreational use of common prescription painkillers. Addiction to prescription opioids is currently one of the most severe forms of drug abuse1, a fact that raises significant public-health concerns and highlights a pressing need for the development of safer painkillers. In this issue, four papers2–5 report crystal structures that provide the first 3 1 4 | NAT U R E | VO L 4 8 5 | 1 7 M AY 2 0 1 2 © 2012 Macmillan Publishers Limited. All rights reserved direct evidence for the binding mode of opioids to their receptors. This information will be invaluable for research aimed at finding opioid drugs that lack the adverse side effects. Opioid receptors (ORs) are members of the superfamily of G-protein-coupled receptors (GPCRs). The traditional model of OR signalling proposes that the binding of a ligand molecule (an opioid) to a receptor activates an associated G protein, which, in turn, triggers a biological response. Widely distributed in the brain and in the peripheral nervous system, the four types of OR are μ-OR, δ-OR, κ-OR and the nociceptin/orphanin FQ peptide receptor. These receptors represent prominent NEWS & VIEWS RESEARCH targets not only for painkillers, but is therefore crucial to understand the also for antidepressants, anti-addicspecific receptor conformations that tion medications and anti-anxiety opioids stabilize to selectively activate TM7 drugs. signalling pathways. This important The papers in this issue2–5 present aspect of ligand binding to ORs is not TM2 the long-awaited, high-resolution captured by the recent crystal strucAddress ECL2 crystal structures of all four ORs in tures, and should be the subject of TM3 ligand-bound conformations. The future research. ligands are all antagonists (receptor There is also compelling eviblockers), which means that the dence12,13 that different types of OR TM6 structures depict inactive states of associate with each other, or with the receptors. These crystal structures other GPCR subtypes, to form dimers TM4 are the latest to have been obtained and oligomers, and that this changes using revolutionary technologies — the signalling properties of the ORs, including the replacement of part of thereby adding an additional level the receptors with another protein, of complexity to an already multisuch as T4 lysozyme6,7, to facilitate faceted problem. Manglik and colMessage receptor crystallization — that have leagues’ structure4 of the μ-OR shows enabled successful structural detertightly associated pairs of receptor mination of several GPCRs. Such molecules, held together predomiproteins were once intractable to Figure 1 | Binding mode of opioids at their receptors. The structures nantly by highly complementary of the four types of opioid receptor, each in complex with a different crystallography. interactions involving TM5 and TM6. 2–5 The four OR structures reveal opioid antagonist, have been solved . A side view of one of the The researchers speculate that this structures — that of the nociceptin/orphanin FQ peptide (NOP) several evolutionarily conserved receptor — is depicted to show features shared by all four receptor pairing might regulate the signalling ligand–receptor interactions in the types. Only five of the seven transmembrane helices (TM1–7) are of the receptor. A similar interaction receptors’ binding pockets, which shown (grey cylinders). ECL2 is a β-hairpin loop region; the arrows was noted8 in the structure of CXCR4, are contained within the seven represent β-sheets. The four antagonists used in the studies are but is not found in the other OR transmembrane helices (desig- depicted as stick representations in the NOP receptor’s binding pocket. structures2,3,5. nated TM1–7) of the receptors. For The cyan surface indicates the amino-acid residues from TM3, TM6 By contrast, the κ-OR strucinstance, several amino-acid residues and TM7 that interact with the ligands’ ‘message’ regions, responsible ture shows a dimeric arrangement at the same positions in TM3, TM6 for a ligand’s efficacy. The magenta surfaces indicate the residues involving interactions of TM1, TM2 and TM7 form interactions with the from TM6 and/or TM7 that interact with the ‘address’ region — and helix 8 (H8), which is simichemical moieties of ligands that are responsible for opioid selectivity — of classical ligands, which contain lar to the alternative, less compact responsible for opioid efficacy — the ‘morphinan’ chemical structure. The light-blue surfaces represent crystal packing seen in the μ-OR residues from TM2 and TM3 that interact with the address region of the ‘message’ region of the ligands. non-classical opioids. structure. The proposed roles of the By contrast, the chemical moieties TM5–TM6 and TM1–TM2–H8 responsible for opioid selectivity interfaces are only two of several — the ‘address’ region — occupy one of two most opioid drugs are highly potent yet rapidly working hypotheses of functionally relevant different areas of the binding pocket, depend- reversible. receptor–receptor interactions that need ing on the type of opioid. Specifically, the Analysis of the OR crystal structures also to be addressed to enable investigators to addresses of classical opioids, which contain reveals an unexpected outward displacement examine the role of dimerization (or oligo the ‘morphinan’ chemical structure, interact of the extracellular half of TM1 away from the merization) in the signalling of ORs. The with TM6 and/or TM7, whereas the corre- long axis of κ-OR (ref. 3), compared with the quest for functionally relevant oligomerization sponding regions of the other opioids studied other opioid receptors2,4,5 and CXCR4 (ref. 8). interfaces therefore continues. are positioned between TM2 and TM3 of the However, as previously noted9,10 in the case of These crystal structures2–5 of inactive ORs receptor (Fig. 1), forming interactions mostly another GPCR — the β1-adrenergic recep- will contribute crucial information to a broad with those helices, but also with TM7. Accord- tor — different conformations of TM1 (and range of therapeutic areas, including those ingly, Wu and colleagues suggest3 that the mes- TM6) can be identified in inactive structures focused on pain, addiction and mental disorsage–address hypothesis of opioid binding as a result of different crystal-packing inter- ders. Future crystal structures of active ORs may not apply uniformly to all opioid ligands. actions and/or crystallization conditions. in complex with different signalling proteins The transmembrane structures of the four In other words, the unusual conformation could provide necessary — although not ORs are very similar to each other, as expected of TM1 in κ-OR may simply be one of many sufficient — information for elucidating the given that the amino-acid sequences of these conformations that could have been adopted mechanisms underlying receptor function. A structures are also very similar (homologous, by the helix. This is an important point, as complete understanding will also require the to use the jargon). More surprisingly, the struc- it reflects the intrinsic dynamic nature of integration of experimental and computational tures of non-homologous loop regions, such GPCRs. Moreover, it reminds us that crystal strategies that allow the study of receptors in as the long, extracellular loop region ECL2, structures of GPCRs are single, static snap- a natural lipid environment — necessary to are also very alike. Notably, the ECL2 struc- shots of receptors stripped of their natural lipid obtain rigorous mechanistic insight, at the ture of the ORs is similar to that8 of CXCR4 — environment, and might therefore offer limited molecular level, into the ligand-induced conanother GPCR that, like the ORs, binds both mechanistic insight. formation selection, spatio-temporal organipeptides and small molecules. This shared, Evidence suggests11 that the most addictive zation and dynamics of OR complexes. The ‘β-hairpin’ loop structure creates a wide open- opioids promote OR interactions with their challenge will then be to translate that knowling that allows ligands unobstructed access G proteins more strongly than with arrestin, edge from bench to bedside, by fine-tuning to the primary binding pocket within the another cellular signalling protein. To develop OR signalling towards therapeutic pathways, transmembrane region. Manglik et al. sug- drugs that retain the therapeutic action of and away from those that mediate adverse side gest4 that this might explain why the effects of opioids but not the unwanted side effects, it effects. ■ 1 7 M AY 2 0 1 2 | VO L 4 8 5 | NAT U R E | 3 1 5 © 2012 Macmillan Publishers Limited. All rights reserved NEWS & VIEWS RESEARCH Marta Filizola is in the Department of Structural and Chemical Biology, and Lakshmi A. Devi is in the Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, New York 10029, USA. e-mails: [email protected]; [email protected] 1. Results from the 2009 National Survey on Drug Use and Health: Volume I. Summary of National Findings (Office of Applied Studies, Rockville, MD, 2010). 2. Thompson, A. A. et al. Nature 485, 395–399 (2012). 3. Wu, H. et al. Nature 485, 327–332 (2012). 4. Manglik, A. et al. Nature 485, 321–326 (2012). 5. Granier, S. et al. Nature 485, 400–404 (2012). 6. Rosenbaum, D. M. et al. Science 318, 1266–1273 (2007). 7. Cherezov, V., Abola, E. & Stevens, R. C. Meth. Mol. NE URO SCIENCE Brain-controlled robot grabs attention Restoring voluntary actions to paralysed patients is an ambition of neuralinterface research. A study shows that people with tetraplegia can use brain control of a robotic arm to reach and grasp objects. See Letter p.372 ANDREW JACKSON M ost of us take for granted our effortless ability to interact with objects. When we are thirsty and reach for a cup, electrical signals stream from the brain through the spinal cord, instructing our muscles to move. However, a disruption of the nerve pathways along which these signals travel can cause paralysis, with devastating consequences for the person’s quality of life. So there is growing interest in technologies that allow the brain to bypass nerve injuries and communicate directly with the environment. Neural-interface systems, also known as brain–machine interfaces, detect electrical signals in the brain and use them to control external assistive devices. The first results from a clinical trial of ‘BrainGate’, a neural interface 1966 1982 Recordings of neurons in motor cortex of awake monkeys 1966 1970 1970 that enabled a patient paralysed by a spinalcord injury to move a computer cursor, were published1 in 2006. On page 372 of this issue, Hochberg et al.2 now report that two people with long-standing paralysis can control the reaching and grasping actions of a robotic arm using BrainGate. One of the participants was even able to drink from a bottle using the robotic arm, something she had not been able to do with her own limb since a stroke nearly 15 years ago. To access brain signals, BrainGate uses thin silicon electrodes surgically inserted a few millimetres into the primary motor cortex, a part of the brain that controls movements. Remarkably, neurons in this area responded when the patients imagined controlling the robotic arm, although both of them had lost the use of their limbs many years earlier. Movement parameters are ‘decoded’ from ensembles of cortical neurons 1978 1982 During a calibration phase, the researchers constructed a ‘decoder’ that translated participants’ intentions into three-dimensional movements and into a closing of the robotic hand. They then tested the participants’ ability to reach for and grasp foam balls presented in front of them. Although the speed and accuracy of the robot’s movements fell well short of those of natural arm control, the participants successfully touched the foam balls on 49% to 95% of attempts across multiple sessions with two different robot designs. What’s more, about two-thirds of successful reaches resulted in correct grasping. The authors further established the efficacy of brain control by one participant in a bottle-grasping and drinking task, demonstrating that a neural-interface system can perform actions that are useful in daily life. Apart from being one of only a handful of studies to use indwelling electrodes for neural interfacing in humans, Hochberg and colleagues’ work is notable in that one patient had had the implanted electrodes for more than five years. Although several techniques (such as electroencephalography) can record signals from the brain in a non-invasive manner, it is generally thought that electrodes positioned inside the brain convey more information. However, as well as the risks associated with surgery, a disadvantage of such implants is the potential for scar tissue to form around the electrodes, which can result 2000 Motor cortex neurons of monkeys are tuned to the direction of reaching movements 1974 Biol. 654, 141–168 (2010). 8. Wu, B. et al. Science 330, 1066–1071 (2010). 9. Warne, T. et al. Nature 454, 486–491 (2008). 10.Moukhametzianov, R. et al. Proc. Natl Acad. Sci. USA 108, 8228–8232 (2011). 11.Molinari, P. et al. J. Biol. Chem. 285, 12522–12535 (2010). 12.Rozenfeld, R. & Devi, L. A. Trends Pharmacol Sci. 31, 124–130 (2010). 13.van Rijn, R. M., Whistler, J. L. & Waldhoer, M. Curr. Opin. Pharmacol. 10, 73–79 (2010). 2006 ‘Open-loop’ control (with no feedback) of a robot arm by monkeys 1986 1990 1994 1994 Neural recordings with the silicon electrode technology later used for BrainGate Demonstration of BrainGate in humans 1998 2002 2002 ‘Closed-loop’ control (with real-time visual feedback) of a neural interface by monkeys 2012 Reaching and grasping of a robot arm controlled by paralysed patients 2006 2010 2008 Monkeys use a braincontrolled robot arm for self-feeding Figure 1 | Within reach. Hochberg et al.2 show that people with tetraplegia can use a neural device, known as BrainGate, to control a robotic arm for reaching and grasping objects. This work builds on decades of previous research on the neural mechanisms that control arm movements13–15 (blue), on electrode development16 (orange) and on neural interfaces in monkeys3–6 (green), which opened the way to studies in humans1,2 (purple). 1 7 M AY 2 0 1 2 | VO L 4 8 5 | NAT U R E | 3 1 7 © 2012 Macmillan Publishers Limited. All rights reserved