PubMed 24138859

Referenced in Channelpedia wiki pages of: none

Automatically associated channels: Cav3.1 , SK1 , SK4

Title: Molecular dynamics simulations of scorpion toxin recognition by the Ca(2+)-activated potassium channel KCa3.1.

Authors: Rong Chen, Shin-Ho Chung

Journal, date & volume: Biophys. J., 2013 Oct 15 , 105, 1829-37

PubMed link:

The Ca(2+)-activated channel of intermediate-conductance (KCa3.1) is a target for antisickling and immunosuppressant agents. Many small peptides isolated from animal venoms inhibit KCa3.1 with nanomolar affinities and are promising drug scaffolds. Although the inhibitory effect of peptide toxins on KCa3.1 has been examined extensively, the structural basis of toxin-channel recognition has not been understood in detail. Here, the binding modes of two selected scorpion toxins, charybdotoxin (ChTx) and OSK1, to human KCa3.1 are examined in atomic detail using molecular dynamics (MD) simulations. Employing a homology model of KCa3.1, we first determine conduction properties of the channel using Brownian dynamics and ascertain that the simulated results are in accord with experiment. The model structures of ChTx-KCa3.1 and OSK1-KCa3.1 complexes are then constructed using MD simulations biased with distance restraints. The ChTx-KCa3.1 complex predicted from biased MD is consistent with the crystal structure of ChTx bound to a voltage-gated K(+) channel. The dissociation constants (Kd) for the binding of both ChTx and OSK1 to KCa3.1 determined experimentally are reproduced within fivefold using potential of mean force calculations. Making use of the knowledge we gained by studying the ChTx-KCa3.1 complex, we attempt to enhance the binding affinity of the toxin by carrying out a theoretical mutagenesis. A mutant toxin, in which the positions of two amino acid residues are interchanged, exhibits a 35-fold lower Kd value for KCa3.1 than that of the wild-type. This study provides insight into the key molecular determinants for the high-affinity binding of peptide toxins to KCa3.1, and demonstrates the power of computational methods in the design of novel toxins.