Abnormalities in the human Nav1.5 (hNav1.5) voltage-gated sodium ion channel (VGSC) are associated with a wide range of cardiac problems and diseases in humans. Current structural models of hNav1.5 are still far from complete and, consequently, their ability to study atomistic interactions of this channel is very limited. Here, we report a comprehensive atomistic model of the hNav1.5 ion channel, constructed using homology modeling technique and refined through long molecular dynamics simulations (680 ns) in the lipid membrane bilayer. Our model was comprehensively validated by using reported mutagenesis data, comparisons with previous models, and binding to a panel of known hNav1.5 blockers. The relatively long classical MD simulation was sufficient to observe a natural sodium permeation event across the channel’s selectivity filters to reach the channel’s central cavity, together with the identification of a unique role of the lysine residue. Electrostatic potential calculations revealed the existence of two potential binding sites for the sodium ion at the outer selectivity filters. To obtain further mechanistic insight into the permeation event from the central cavity to the intracellular region of the channel, we further employed “state-of-the-art” steered molecular dynamics (SMD) simulations. Our SMD simulations revealed two different pathways through which a sodium ion can be expelled from the channel. Further, the SMD simulations identified the key residues that are likely to control these processes. Finally, we discuss the potential binding modes of a panel of known hNav1.5 blockers to our structural model of hNav1.5. We believe that the data presented here will enhance our understanding of the structure–property relationships of the hNav1.5 ion channel and the underlying molecular mechanisms in sodium ion permeation and drug interactions. The results presented here could be useful for designing safer drugs that do not block the hNav1.5 channel.

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The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews’ correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.

The voltage-gated KCNQ1 potassium ion channel interacts with the type I transmembrane protein minK (KCNE1) to generate the slow delayed rectifier (IKs) current in the heart. Mutations in these transmembrane proteins have been linked with several heart-related issues, including long QT syndromes (LQTS), congenital atrial fibrillation, and short QT syndrome. Off-target interactions of several drugs with that of KCNQ1/KCNE1 ion channel complex have been known to cause fatal cardiac irregularities. Thus, KCNQ1/KCNE1 remains an important avenue for drug-design and discovery research. In this work, we present the structural and mechanistic details of potassium ion permeation through an open KCNQ1 structural model using the combined molecular dynamics and steered molecular dynamics simulations. We discuss the processes and key residues involved in the permeation of a potassium ion through the KCNQ1 ion channel, and how the ion permeation is affected by (i) the KCNQ1-KCNE1 interactions and (ii) the binding of chromanol 293B ligand and its derivatives into the complex. The results reveal that interactions between KCNQ1 with KCNE1 causes a pore constriction in the former, which in-turn forms small energetic barriers in the ion-permeation pathway. These findings correlate with the previous experimental reports that interactions of KCNE1 dramatically slows the activation of KCNQ1. Upon ligand-binding onto the complex, the energy-barriers along ion permeation path are more pronounced, as expected, therefore, requiring higher force in our steered-MD simulations. Nevertheless, pulling the ion when a weak blocker is bound to the channel does not necessitate high force in SMD. This indicates that our SMD simulations have been able to discern between strong and week blockers and reveal their influence on potassium ion permeation. The findings presented here will have some implications in understanding the potential off-target interactions of the drugs with the KCNQ1/KCNE1 channel that lead to cardiotoxic effects.