Ricerc@Sapienza

The KCNA2 gene encodes the Kv1.2 channel, a mammalian Shaker-like voltage gated potassium channel that is wide expressed in mammals by visceral smooth muscle cells and neurons. Its defections are linked to neuronal deficiency inducing encephalopathies, epilepsy, ataxia, and cerebellar atrophy. While the gating mechanism has been extensively characterized, the inactivation remains hereby elusive. However, the inactivated state represents a crucial point in the kinetic behaviour of the channel because it biases the conformation of the pore towards the closed state. In this project I will study the Kv1.2 inactivation via Molecular Dynamics simulations that give the possibility to explore phenomena with the correct spatial and temporal resolution allowing to connect the protein structure to its function and dynamics. At first I will reproduce the computational system corresponding to the wild type channel. After the equilibration, I will apply a network theoretical approach to identify the most likely communication pathway as the motion propagation occurring in the protein during the inactivation. I will represent the channel as a graph with edges weighted by the information distance dij=-log|Cij|, Cij being the correlation coefficient and then using the Dijkstra's algorithm I will identify the minimal paths connecting
the Voltage Sensor Domain and Pore Domain with the Selectivity Filter whose rearrangements induce the channel to inactivate. Finally, I will compute a contact map to characterize microscopically the resulting pathways and I will compare them to those of some mutants. Understanding the molecular mechanism underlying Kv1.2 channel inactivation, focusing on the crucial residues and their interactions, is important not only for human health but also to extract the general principles to design artificial nanopores and biosensors with higher selectivity.