Hancox, J. | Search bioRxiv

bioRxiv2026

Integrating AI and molecular modeling for structural prediction of a closed state of the hERG channel

The voltage-gated potassium channel hERG (Kv11.1) plays a central role in cardiac repolarisation by mediating the rapid delayed rectifier K+ current (IKr). Blockage of hERG by small molecules can lead to delayed repolarisation, QT interval prolongation, and potentially fatal arrhythmias, making the channel a critical focus in drug safety screening. Despite extensive pharmacological and electrophysiological characterisation, a complete structural understanding of hERG gating remains limited by the absence of an experimentally determined closed-state structure. Here, we use AI-based structural modelling to predict and compare candidate closed conformations of hERG. Building on recent work in which AlphaFold2 (AF2) predictions guided by engineered structural templates captured closed and inactivated states, we applied the emerging protein structure predictor, Chai-1, which employs a single-sequence, language model-based approach independent of multiple-sequence alignments. The resulting Chai-1 hERG model was compared with the AF2-derived closed structure, a homology model based on the Rattus norvegicus EAG channel, and an experimentally resolved open-state cryo-EM structure. We assessed these models using a combination of all-atom and coarse-grained molecular dynamics simulations, analysing protein dynamics, pore geometry, gating residue orientation, hydration, and lipid interactions. The Chai-1 and AF2 models displayed strong structural and dynamic agreement, both adopting compact, non-conductive conformations consistent with a physiologically closed state. Our data reveal insights into VSD dynamics, as well as suggesting a state dependence for ceramide binding at the previously identified M651 residue. Our findings support the validity of AI-derived closed-state hERG models and underscore the growing potential of deep learning-based protein structure prediction to identify previously uncharacterised, pharmacologically relevant conformations of membrane proteins. Further, our Chai-1 derived closed state model expands our structural insights into hERG gating and may have utility for investigation of drug-hERG interactions.

biochemistry↗

bioRxiv2019

Rapid characterisation of hERG channel kinetics II: temperature dependence

Ion channel behaviour can depend strongly on temperature, with faster kinetics at physiological temperatures leading to considerable changes in currents relative to room temperature. These temperature-dependent changes in voltage-dependent ion channel kinetics (rates of opening, closing and inactivating) are commonly represented with Q10 coefficients or an Eyring relationship. In this paper we assess the validity of these representations by characterising channel kinetics at multiple temperatures. We focus on the hERG channel, which is important in drug safety assessment and commonly screened at room temperature, so that results require extrapolation to physiological temperature. In Part I of this study we established a reliable method for high-throughput characterisation of hERG1a (Kv11.1) kinetics, using a 15 second information-rich optimised protocol. In this Part II, we use this protocol to study the temperature dependence of hERG kinetics using CHO cells over-expressing hERG1a on the Nanion SyncroPatch 384PE, a 384-well automated patch clamp platform, with temperature control. We characterise the temperature dependence of hERG gating by fitting the parameters of a mathematical model of hERG kinetics to data obtained at five distinct temperatures between 25 and 37 {degrees}C, and validate the models using different protocols. Our models reveal that activation is far more temperature sensitive than inactivation, and we observe that the temperature dependency of the kinetic parameters is not represented well by Q10 coefficients: it broadly follows a generalised, but not the standardly-used, Eyring relationship. We also demonstrate that experimental estimations of Q10 coefficients are protocol-dependent. Our results show that a direct fit using our 15 second protocol best represents hERG kinetics at any given temperature, and suggests that predictions from the Generalised Eyring theory may be preferentially used if no experimentally-derived data are available.\n\nStatement of SignificanceIon channel currents are highly sensitive to temperature changes. Yet because many experiments are performed more easily at room temperature, it is common to extrapolate findings to physiological temperatures through the use of Q10 coefficients or Eyring rate theory. By applying short, information-rich protocols that we developed in Part I of this study we identify how kinetic parameters change over temperature. We find that the commonly-used Q10 and Eyring formulations are incapable of describing the parameters temperature dependence, a more Generalised Eyring relationship works well, but remeasuring kinetics and refitting a model is optimal. The findings have implications for the accuracy of the many applications of Q10 coefficients in electrophysiology, and suggest that care is needed to avoid misleading extrapolations in their many scientific and industrial pharmaceutical applications.

biophysics↗