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Temperature-dependent regulation of ion channel activity is critical for a variety of physiological processes ranging from immune response to perception of noxious stimuli. Our understanding of the structural mechanisms that underlie temperature sensing remains limited, in part due to the difficulty of combining high-resolution structural analysis with temperature stimulus. Here, we use NMR to compare the temperature-dependent behavior of Shaker potassium channel voltage sensor domain (WT-VSD) to its engineered temperature sensitive (TS-VSD) variant. Further insight into the molecular basis for temperature-dependent behavior is obtained by analyzing the experimental results together with molecular dynamics simulations. Our studies reveal that the overall secondary structure of the engineered TS-VSD is identical to the wild-type channels except for local changes in backbone torsion angles near the site of substitution (V369S and F370S). Remarkably however, these structural differences result in increased hydration of the voltage-sensing arginines and the S4–S5 linker helix in the TS-VSD at higher temperatures, in contrast to the WT-VSD. These findings highlight how subtle differences in the primary structure can result in large-scale changes in solvation and thereby confer increased temperature-dependent activity beyond that predicted by linear summation of solvation energies of individual substituents. 

Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large datasets, existing analysis tools are not suitable to process the large volume of data obtained in high-throughput paradigms. Here, we present a new analysis platform (DISC) that accelerates unsupervised analysis of single-molecule trajectories. By merging model-free statistical learning with the Viterbi algorithm, DISC idealizes single-molecule trajectories up to three orders of magnitude faster with improved accuracy compared to other commonly used algorithms. Further, we demonstrate the utility of DISC algorithm to probe cooperativity between multiple binding events in the cyclic nucleotide binding domains of HCN pacemaker channel. Given the flexible and efficient nature of DISC, we anticipate it will be a powerful tool for unsupervised processing of high-throughput data across a range of single-molecule experiments.