Potassium channels are responsible for the selective permeation of K ions across cell membranes. K ions permeate in single file through the selectivity filter, a narrow pore lined by backbone carbonyls that compose four K binding sites. Here, we report on the two-dimensional infrared (2D IR) spectra of a semisynthetic KcsA channel with site-specific heavy (13C18O) isotope labels in the selectivity filter. The ultrafast time resolution of 2D IR spectroscopy provides an instantaneous snapshot of the multi-ion configurations and structural distributions that occur spontaneously in the filter. Two elongated features are resolved, revealing the statistical weighting of two structural conformations.
Potassium (K) channels have been evolutionarily tuned for activation by diverse biological stimuli, and pharmacological activation is thought to target these specific gating mechanisms. Here we report a class of negatively charged activators (NCAs) that bypass the specific mechanisms but act as master keys to open K channels gated at their selectivity filter (SF), including many two-pore domain K (K2P) channels, voltage-gated hERG (human ether-à-go-go–related gene) channels and calcium (Ca2)–activated big-conductance potassium (BK)–type channels. Functional analysis, x-ray crystallography, and molecular dynamics simulations revealed that the NCAs bind to similar sites below the SF, increase pore and SF K occupancy, and open the filter gate. These results uncover an unrecognized polypharmacology among K channel activators and highlight a filter gating machinery that is conserved across different families of K channels with implications for rational drug design.
Class selectivity, typically defined as how different a neuron's responses are across different classes of stimuli or data samples, is a common metric used to interpret the function of individual neurons in biological and artificial neural networks. However, it remains an open question whether it is necessary and/or sufficient for deep neural networks (DNNs) to learn class selectivity in individual units. In order to investigate the causal impact of class selectivity on network function, we directly regularize for or against class selectivity. Using this regularizer, we were able to reduce mean class selectivity across units in convolutional neural networks by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small ($\sim$2%) change in test accuracy. In contrast, increasing class selectivity beyond the levels naturally learned during training had rapid and disastrous effects on test accuracy. These results indicate that class selectivity in individual units is neither neither sufficient nor strictly necessary for DNN performance, and more generally encourage caution when focusing on the properties of single units as representative of the mechanisms by which DNNs function.
Organic electrosynthesis can transform the chemical industry by introducing electricity-driven processes that are more energy efficient and that can be easily integrated with renewable energy sources. However, their deployment is severely hindered by the difficulties of controlling selectivity and achieving a large energy conversion efficiency at high current density due to the low solubility of organic reactants in practical electrolytes. This control can be improved by carefully balancing the mass transport processes and electrocatalytic reaction rates at the electrode diffusion layer through pulsed electrochemical methods. In this study, we explore these methods in the context of the electrosynthesis of adiponitrile (ADN), the largest organic electrochemical process in industry. Systematically exploring voltage pulses in the timescale between 5 and 150 ms led to a 20% increase in production of ADN and a 250% increase in relative selectivity with respect to the state-of-the-art constant voltage process.