市民社会のプレーヤー交代劇と政治-仁平典宏の<贈与のパラドックス>論を読み解く-

千葉大学大学院人文社会科学研究科研究プロジェクト報告書第257集『都市コミュニティにおける相互扶助と次世代育成』水島治郎 編Sustainable Urban Communities: Communality and Generativity Report on the Research Projects No.25

Complex Structures between the N‑Type Calcium Channel (Ca_V2.2) and ω‑Conotoxin GVIA Predicted via Molecular Dynamics

The N-type voltage-gated Ca²⁺ channel Ca_V2.2 is one of the important targets for pain management. ω-Conotoxins isolated from venoms of cone snails, which specifically inhibit Ca_V2.2, are promising scaffolds for novel analgesics. The inhibitory action of ω-conotoxins on Ca_V2.2 has been examined experimentally, but the modes of binding of the toxins to this and other related subfamilies of Ca²⁺ channels are not understood in detail. Here molecular dynamics simulations are used to construct models of ω-conotoxin GVIA in complex with a homology model of the pore domain of Ca_V2.2. Three different binding modes in which the side chain of Lys2, Arg17, or Lys24 from the toxin protrudes into the selectivity filter of Ca_V2.2 are considered. In all the modes, the toxin forms a salt bridge with an aspartate residue of subunit II just above the EEEE ring of the selectivity filter. Using the umbrella sampling technique and potential of mean force calculations, the half-maximal inhibitory concentration (IC₅₀) values are calculated to be 1.5 and 0.7 nM for the modes in which Lys2 and Arg17 occlude the ion conduction pathway, respectively. Both IC₅₀ values compare favorably with the values of 0.04–1.0 nM determined experimentally. The similar IC₅₀ values calculated for the different binding modes demonstrate that GVIA can inhibit Ca_V2.2 with alternative binding modes. Such a multiple-binding mode mechanism may be common for ω-conotoxins

Structural Basis of the Selective Block of Kv1.2 by Maurotoxin from Computer Simulations

<div><p>The 34-residue polypeptide maurotoxin (MTx) isolated from scorpion venoms selectively inhibits the current of the voltage-gated potassium channel Kv1.2 by occluding the ion conduction pathway. Here using molecular dynamics simulation as a docking method, the binding modes of MTx to three closely related channels (Kv1.1, Kv1.2 and Kv1.3) are examined. We show that MTx forms more favorable electrostatic interactions with the outer vestibule of Kv1.2 compared to Kv1.1 and Kv1.3, consistent with the selectivity of MTx for Kv1.2 over Kv1.1 and Kv1.3 observed experimentally. One salt bridge in the bound complex of MTx-Kv1.2 forms and breaks in a simulation period of 20<b> </b>ns, suggesting the dynamic nature of toxin-channel interactions. The toxin selectivity likely arises from the differences in the shape of the channel outer vestibule, giving rise to distinct orientations of MTx on block. Potential of mean force calculations show that MTx blocks Kv1.1, Kv1.2 and Kv1.3 with an IC₅₀ value of 6 µM, 0.6<b> </b>nM and 18 µM, respectively.</p> </div

Engineering a Potent and Specific Blocker of Voltage-Gated Potassium Channel Kv1.3, a Target for Autoimmune Diseases

A polypeptide toxin extracted from scorpion venom, OSK1, is modified such that its potency is drastically enhanced in blocking one class of voltage-gated potassium channels, Kv1.3, which is a pharmacological target for immunosuppressive therapy. The bound complex of Kv1.3 and OSK1 reveals that one lysine residue of the toxin is in the proximity of another lysine residue on the external vestibule of the channel, just outside of the selectivity filter. This unfavorable electrostatic interaction is eliminated by interchanging the positions of two amino acids in the toxin. The potentials of mean force of the wild-type and mutant OSK1 bound to Kv1.1–Kv1.3 channels are constructed using molecular dynamics, and the half-maximal inhibitory concentration (IC₅₀) of each toxin–channel complex is computed. We show that the IC₅₀ values predicted for three toxins and three channels match closely with experiment. Kv1.3 is half-blocked by 0.2 pM mutant OSK1; it is >10000-fold more specific for this channel than for Kv1.1 and Kv1.2

The average changes in van der Waals () and electrostatic () interaction energies (kcal/mol) between MTx and the surroundings due to the binding of MTx to Kv1.1–Kv1.3 channels.

<p>Standard deviations are shown. Δ<i>G</i>_bind is calculated as , where C₀ is 1 M. The IC₅₀ values are 6 µM, 0.6<b> </b>nM and 18 µM for Kv1.1, Kv1.2 and Kv1.3, respectively.</p

Time evolution of the salt bridge lengths.

<p>The lengths of the salt bridges Arg14-Asp355 and Lys7-Asp363 formed in the MTx-Kv1.2 complexes as a function of the simulation time over the last 15<b> </b>ns.</p

MTx bound to Kv1.2.

<p>In (A), two key residue pairs Lys23-Tyr377 and Arg14-Asp355 are highlighted. Two channel subunits are shown for clarity. (B) The MTx-Kv1.2 complex rotated by approximately 90° clockwise from that of (A). The third key residue pair Lys7-Asp363 is highlighted in (B).</p

The position of MTx (yellow) relative to Kv1.1-Kv1.3 channels.

<p>The key residue 381 is highlighted in red. Green arrows represent the dipole moment of MTx.</p

Mechanism of μ-Conotoxin PIIIA Binding to the Voltage-Gated Na⁺ Channel Na_V1.4

<div><p>Several subtypes of voltage-gated Na⁺ (Na_V) channels are important targets for pain management. μ-Conotoxins isolated from venoms of cone snails are potent and specific blockers of different Na_V channel isoforms. The inhibitory effect of μ-conotoxins on Na_V channels has been examined extensively, but the mechanism of toxin specificity has not been understood in detail. Here the known structure of μ-conotoxin PIIIA and a model of the skeletal muscle channel Na_V1.4 are used to elucidate elements that contribute to the structural basis of μ-conotoxin binding and specificity. The model of Na_V1.4 is constructed based on the crystal structure of the bacterial Na_V channel, Na_VAb. Six different binding modes, in which the side chain of each of the basic residues carried by the toxin protrudes into the selectivity filter of Na_V1.4, are examined in atomic detail using molecular dynamics simulations with explicit solvent. The dissociation constants (<i>K</i>_d) computed for two selected binding modes in which Lys9 or Arg14 from the toxin protrudes into the filter of the channel are within 2 fold; both values in close proximity to those determined from dose response data for the block of Na_V currents. To explore the mechanism of PIIIA specificity, a double mutant of Na_V1.4 mimicking Na_V channels resistant to μ-conotoxins and tetrodotoxin is constructed and the binding of PIIIA to this mutant channel examined. The double mutation causes the affinity of PIIIA to reduce by two orders of magnitude.</p></div

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study

<div><p><b>Objective:</b> Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human–machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.</p><p><b>Methods:</b> The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.</p><p><b>Results:</b> Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40–50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.</p><p><b>Conclusions:</b> This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.</p></div