310-Helix Conformation Facilitates the Transition of a Voltage Sensor S4 Segment toward the Down State

AbstractThe activation of voltage-gated ion channels is controlled by the S4 helix, with arginines every third residue. The x-ray structures are believed to reflect an open-inactivated state, and models propose combinations of translation, rotation, and tilt to reach the resting state. Recently, experiments and simulations have independently observed occurrence of 310-helix in S4. This suggests S4 might make a transition from α- to 310-helix in the gating process. Here, we show 310-helix structure between Q1 and R3 in the S4 segment of a voltage sensor appears to facilitate the early stage of the motion toward a down state. We use multiple microsecond-steered molecular simulations to calculate the work required for translating S4 both as α-helix and transformed to 310-helix. The barrier appears to be caused by salt-bridge reformation simultaneous to R4 passing the F233 hydrophobic lock, and it is almost a factor-two lower with 310-helix. The latter facilitates translation because R2/R3 line up to face E183/E226, which reduces the requirement to rotate S4. This is also reflected in a lower root mean-square deviation distortion of the rest of the voltage sensor. This supports the 310 hypothesis, and could explain some of the differences between the open-inactivated- versus activated-states

Conformational Changes and Slow Dynamics through Microsecond Polarized Atomistic Molecular Simulation of an Integral Kv1.2 Ion Channel

Structure and dynamics of voltage-gated ion channels, in particular the motion of the S4 helix, is a highly interesting and hotly debated topic in current membrane protein research. It has critical implications for insertion and stabilization of membrane proteins as well as for finding how transitions occur in membrane proteins—not to mention numerous applications in drug design. Here, we present a full 1 µs atomic-detail molecular dynamics simulation of an integral Kv1.2 ion channel, comprising 120,000 atoms. By applying 0.052 V/nm of hyperpolarization, we observe structural rearrangements, including up to 120° rotation of the S4 segment, changes in hydrogen-bonding patterns, but only low amounts of translation. A smaller rotation (∼35°) of the extracellular end of all S4 segments is present also in a reference 0.5 µs simulation without applied field, which indicates that the crystal structure might be slightly different from the natural state of the voltage sensor. The conformation change upon hyperpolarization is closely coupled to an increase in 310 helix contents in S4, starting from the intracellular side. This could support a model for transition from the crystal structure where the hyperpolarization destabilizes S4–lipid hydrogen bonds, which leads to the helix rotating to keep the arginine side chains away from the hydrophobic phase, and the driving force for final relaxation by downward translation is partly entropic, which would explain the slow process. The coordinates of the transmembrane part of the simulated channel actually stay closer to the recently determined higher-resolution Kv1.2 chimera channel than the starting structure for the entire second half of the simulation (0.5–1 µs). Together with lipids binding in matching positions and significant thinning of the membrane also observed in experiments, this provides additional support for the predictive power of microsecond-scale membrane protein simulations

Modeling of voltage-gated ion channels

The recent determination of several crystal structures of voltage-gated ion channels has catalyzed computational efforts of studying these remarkable molecular machines that are able to conduct ions across biological membranes at extremely high rates without compromising the ion selectivity. Starting from the open crystal structures, we have studied the gating mechanism of these channels by molecular modeling techniques. Firstly, by applying a membrane potential, initial stages of the closing of the channel were captured, manifested in a secondary-structure change in the voltage-sensor. In a follow-up study, we found that the energetic cost of translocating this 310-helix conformation was significantly lower than in the original conformation. Thirdly, collaborators of ours identified new molecular constraints for different states along the gating pathway. We used those to build new protein models that were evaluated by simulations. All these results point to a gating mechanism where the S4 helix undergoes a secondary structure transformation during gating. These simulations also provide information about how the protein interacts with the surrounding membrane. In particular, we found that lipid molecules close to the protein diffuse together with it, forming a large dynamic lipid-protein cluster. This has important consequences for the understanding of protein-membrane interactions and for the theories of lateral diffusion of membrane proteins. Further, simulations of the simple ion channel antiamoebin were performed where different molecular models of the channel were evaluated by calculating ion conduction rates, which were compared to experimentally measured values. One of the models had a conductance consistent with the experimental data and was proposed to represent the biological active state of the channel. Finally, the underlying methods for simulating molecular systems were probed by implementing the CHARMM force field into the GROMACS simulation package. The implementation was verified and specific GROMACS-features were combined with CHARMM and evaluated on long timescales. The CHARMM interaction potential was found to sample relevant protein conformations indifferently of the model of solvent used.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript

Increasing Outbreak Detection Power by Data Transformations

Syndromic data involves data variation that can be difficult to handle by traditional methods of analysis, e.g. mass gatherings, extreme weather and other high-profile events. For the purpose of optimizing baselines for outbreak detection, we carried out a power analysis of data transformations, e.g. ratios and geometric means. ANOVAs were applied to power simulations, using the gamma distribution to generate baseline and outbreak distributions. The results were compared with empirical findings on syndromic surveillance (Swedish Health Care Direct 1177). The study supports the potential value of data transformations to increase detection power and control for sporadic events

Increasing Outbreak Detection Power by Data Transformations

Syndromic data involves data variation that can be difficult to handle by traditional methods of analysis, e.g. mass gatherings, extreme weather and other high-profile events. For the purpose of optimizing baselines for outbreak detection, we carried out a power analysis of data transformations, e.g. ratios and geometric means. ANOVAs were applied to power simulations, using the gamma distribution to generate baseline and outbreak distributions. The results were compared with empirical findings on syndromic surveillance (Swedish Health Care Direct 1177). The study supports the potential value of data transformations to increase detection power and control for sporadic events

Syndromic Surveillance for Outbreak Detection and Investigation

For the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data: telephone triage of acute gastroenteritis, web queries about symptoms of gastrointestinal illness, and OTC pharmacy sales of anti-diarrhea medication. The sensitivity and specificity were highest for telephone triage data. It provided the most promising source of syndromic data for surveillance of point-source outbreaks. Currently, a project has been initialized to develop and implement a national system in Sweden for daily syndromic surveillance based on 1177 Health Care Direct, supporting regional and local outbreak detection and investigation