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.
This project aims at characterizing molecularly the inactivation of the Kv1.2 channel, a required transition that acts as a hub in the channel kinetics allowing the pore to open and close. It will bring innovations for both biomedical and engineering scientific research. In the first case, the description at atomistic level of this veiled state will promote a large scale screening program to test new drugs for the severe neuronal conditions linked to a deficiency of the channel (1). On the other hand, there will be several advancements in the engineering. Understanding the inactivation mechanism that occur at the level of the selectivity filter of an ion channel will inspire the production of artificial bio-mimetic channels to study various biomolecules in confined spaces and in real time by current measurements. The artificial channels are becoming the focus of attention because, compared with their biological counterparts, they offer greater flexibility in terms of shape and size, robustness and surface properties. Due to their functions, biological ion channels give the possibility to extract the basic principles of selectivity, sensing and regulation related to their molecular composition and to reproduce them artificially depending on the desired application. In fact chemical modification of the interior surface of the artificial channels with functional molecules that closely mimic the biological mechanisms may provide a highly efficient way to control and quantify ionic or molecular transport through them in response to ambient stimuli such as applied force, light, pH and specific ions (2). They have also a rapid and reversible capacity to detect the concentration of confined analytes, for example in a cell, or to identify several analytes concurrently. There are many examples of bio-mimetic smart channels: the stochastic sensors based on the staphylococcal alfa-hemolysin (3) or the DNA sequencer produced on the transmembrane porin made from DNA origami (4).
In summary, the identification of the Kv1.2 channel inactivation will be useful to reveal the molecular determinants of many neuronal disorders in order to design both new specific drugs to treat it and smart artificial channels or sensors with high specificity to an analyte.
References:
(1) Bagal, Sharan K et al. Ion channels as therapeutic targets: a drug discovery perspective. Journal of medicinal chemistry vol. 56,3 (2013): 593-624.
(2) Hou, Xu, and Lei Jiang. Learning from nature: building bio-inspired smart nanochannels. ACS nano vol. 3,11 (2009): 3339-42.
(3) Bayley, H, and P S Cremer. Stochastic sensors inspired by biology. Nature vol. 413,6852 (2001): 226-30.
(4) Göpfrich, Kerstin et al. Large-Conductance Transmembrane Porin Made from DNA Origami. ACS nano vol. 10,9 (2016): 8207-14.