**5. Conclusions**

Rapid advances in computer hardware and software in the last decade has enabled accurate description of the structure and energetics of protein-ligand complexes. As shown in the present work, the accuracy of the computational methods now extends to description of channel-toxin complexes. This opens new avenues for design of drugs from natural sources such as marine toxins. Affinity and selectivity properties of a toxin for a given channel target can be improved by making rational choices for mutations from accurate complex models, and then performing free energy calculations to find the free energy change associated with the mutation. While very good results have been obtained in the examples discussed here, more tests need to be performed to check the predictive power of the computational methods and increase the confidence in them. This is especially so in sodium channels, whose crystal structure has been solved only recently, and there are only a few computational studies of ligand binding to sodium channels as yet. It is expected that there will be a lot of activity in simulation of ligand binding to sodium channels in near future, which will bring our understanding of ligand binding to a level similar to that in potassium channels.

The computational methods have an enormous potential for rational drug design from toxins targeting ion channels, but lack of crystal structures for many types of ion channels is hindering progress. Solution of the structures of channel proteins is the main bottleneck at present and more effort should go in this direction. For example, the ligand-gated ion channels such as nicotinic acetylcholine receptor are even more important therapeutic targets than the voltage-gated ion channels [2], and there are many families of toxins that bind to these channels with high affinity and selectivity. Thus solution of the structures of these channels would enable application of the computational methods for the purpose of designing novel therapeutic agents from the toxin blockers of these channels.

**Acknowledgments:** This work was supported by grants from the Australian Research Council. Calculations were performed using the HPC facilities at the National Computational Infrastructure (Canberra). We thank Ray Norton for useful discussions on binding properties of ShK toxin and its analogs, and Po-Chia Chen for helping with the simulation studies of toxin binding.

### **References**


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