Novel long-chain neurotoxins from Bungarus candidus distinguish the two binding sites in muscle-type nicotinic acetylcholine receptors

αδ-Bungarotoxins, a novel group of long-chain α-neurotoxins, manifest different affinity to two agonist/competitive antagonist binding sites of muscle-type nicotinic acetylcholine receptors (nAChRs), being more active at the interface of α–δ subunits. Three isoforms (αδ-BgTx-1–3) were identified in Malayan Krait (Bungarus candidus) from Thailand by genomic DNA analysis; two of them (αδ-BgTx-1 and 2) were isolated from its venom. The toxins comprise 73 amino acid residues and 5 disulfide bridges, being homologous to α-bungarotoxin (α-BgTx), a classical blocker of muscle-type and neuronal α7, α8, and α9α10 nAChRs. The toxicity of αδ-BgTx-1 (LD50 = 0.17–0.28 µg/g mouse, i.p. injection) is essentially as high as that of α-BgTx. In the chick biventer cervicis nerve–muscle preparation, αδ-BgTx-1 completely abolished acetylcholine response, but in contrast with the block by α-BgTx, acetylcholine response was fully reversible by washing. αδ-BgTxs, similar to α-BgTx, bind with high affinity to α7 and muscle-type nAChRs. However, the major difference of αδ-BgTxs from α-BgTx and other naturally occurring α-neurotoxins is that αδ-BgTxs discriminate the two binding sites in the Torpedo californica and mouse muscle nAChRs showing up to two orders of magnitude higher affinity for the α–δ site as compared with α–ε or α–γ binding site interfaces. Molecular modeling and analysis of the literature provided possible explanations for these differences in binding mode; one of the probable reasons being the lower content of positively charged residues in αδ-BgTxs. Thus, αδ-BgTxs are new tools for studies on nAChRs

The method of PID controllers synthesis for sixth-order systems

The methodical of proportional-integral-derivative (PID) controllers designing for systems the sixth order are suggested. At the first stage of implementation of the method algorithm, the transfer function of the corrected closed control system is determined. Then you can specify the number and type of roots of this polynomial. The roots of the polynomial are set taking into account the root quality indicators. The next step of the algorithm determines the values of the parameters desired polynomial of the corrected closed system. These parameters will depend on the imaginary parts of complex-conjugate roots. Parameters of the polynomial must be positive It follows from the stability condition of the system. For a sixth-order model, the four higher-order parameters of this polynomial do not depend on the controller parameters and their values cannot be changed, since they are determined by calculations. Next, the coefficients of the PID controller model are calculated. You can set the condition for positive values of the regulator parameters. Checking the results of calculating the parameters of the controller is performed by the step response of the system. The efficiency of the proposed method of the designing PID controller is performed on the example of a system with a sixth order object. © Published under licence by IOP Publishing Ltd