Computational Studies of Ion Channel Blockers and Protein Aggregation

Abstract

The ion channels are important membrane bound proteins and multi-therapeutic target for a number of diseases. There are many scorpion toxins reported to bind with KV1 channels. We have identified a plant toxin. We have studied binding of two different sequence length peptides, 1-47 and 10-44 with KV1.3 and KV1.1 channels. The binding complex structures are obtained by molecular docking and stability of the complexes are checked by MD simulations. The pore inserting residue is predicted as K33. The KV1.1-toxin complexes are found to be unstable, and toxin doesn't block the pore in MD simulations. The predicted binding free energies for both complexes of KV1.3-toxin are within the range of experimental values with picomolar and nanomolar activities, respectively. Experiments have also confirmed that Toxin(1-47) does not block the current with KV1.1 channel as well. The inward-rectifier potassium (Kir) channels play significant roles in several physiological disorders. I have studied binding of honey bee toxin called tertiapin (TPN) with Kir3.x channels. K21 is predicted to be the pore inserting residue. The binding free energies are calculated and validated with experimental value. I have studied TPN complexes with Kir3.1 and Kir 3.3 and explained insensitivity of TPN to these channels. A few crystal structures of bacterial sodium channels have been determined but the crystal structures of mammalian ones have not been resolved yet. So, it is important to understand similarities and differences between the bacterial and mammalian channels. The validated homology modeled NaV1.4 complexed with GIIIA system provides a good model for such comparisons. Here I have studied the binding of GIIIA to the bacterial sodium channels NaVAb and NaVRh by combination of docking and MD simulations then the potential mean forces are constructed. Comparison of the binding mode of GIIIA between mammalian and bacterial channels. Protein aggregation affects both human physiological functions and bioengineered products so finding methods to prevent aggregation will we very useful. It is an important area of investigation which could be facilitated by a molecular-level understanding of dimer formation, which is the first step in aggregation. Here we propose a computational method based on molecular dynamics simulations that will facilitate finding aggregation-prone regions on human lysozyme HL[D67H], noting that the wild type HL doesn't aggregate.