Comparisons of voltage-gated sodium channel structures with open and closed gates and implications for state-dependent drug design

2018 ◽  
Vol 46 (6) ◽  
pp. 1567-1575 ◽  
Author(s):  
Giulia Montini ◽  
Jennifer Booker ◽  
Altin Sula ◽  
B.A. Wallace
Keyword(s):  
Drug Design ◽  
Sodium Channels ◽  
Rational Drug Design ◽  
Voltage Gated ◽  
Voltage Gated Sodium Channels ◽  
State Dependent ◽  
Rational Drug ◽  
Sequence Homologies ◽  
Arcobacter Butzleri ◽  
Functional Consequences

Voltage-gated sodium channels (Navs) are responsible for the initiation of the action potential in excitable cells. Several prokaryotic sodium channels, most notably NavMs from Magnetococcus marinus and NavAb from Arcobacter butzleri, have been shown to be good models for human sodium channels based on their sequence homologies and high levels of functional similarities, including ion flux, and functional consequences of critical mutations. The complete full-length crystal structures of these prokaryotic sodium channels captured in different functional states have now revealed the molecular natures of changes associated with the gating process. These include the structures of the intracellular gate, the selectivity filter, the voltage sensors, the intra-membrane fenestrations, and the transmembrane (TM) pore. Here we have identified for the first time how changes in the fenestrations in the hydrophobic TM region associated with the opening of the intracellular gate could modulate the state-dependent ingress and binding of drugs in the TM cavity, in a way that could be exploited for rational drug design.

scholarly journals V102862 (Co 102862): a potent, broad-spectrum state-dependent blocker of mammalian voltage-gated sodium channels

2005 ◽  
Vol 144 (6) ◽  
pp. 801-812 ◽  
Author(s):  
Victor I Ilyin ◽  
Dianne D Hodges ◽  
Edward R Whittemore ◽  
Richard B Carter ◽  
Sui Xiong Cai ◽  
...  
Keyword(s):  
Sodium Channels ◽  
Broad Spectrum ◽  
Voltage Gated ◽  
Voltage Gated Sodium Channels ◽  
State Dependent

scholarly journals Molecular determinants of state-dependent block of voltage-gated sodium channels by pilsicainide

2010 ◽  
Vol 160 (6) ◽  
pp. 1521-1533 ◽  
Author(s):  
J-F Desaphy ◽  
A Dipalma ◽  
T Costanza ◽  
C Bruno ◽  
G Lentini ◽  
...  
Keyword(s):  
Sodium Channels ◽  
Voltage Gated ◽  
Voltage Gated Sodium Channels ◽  
State Dependent ◽  
Molecular Determinants

State-dependent block of voltage-gated sodium channels by the casein-kinase 1 inhibitor IC261

2017 ◽  
Vol 35 (3) ◽  
pp. 277-289 ◽  
Author(s):  
Karl J. Föhr ◽  
Uwe Knippschild ◽  
Anna Herkommer ◽  
Michael Fauler ◽  
Christian Peifer ◽  
...  
Keyword(s):  
Sodium Channels ◽  
Casein Kinase ◽  
Casein Kinase 1 ◽  
Voltage Gated ◽  
Voltage Gated Sodium Channels ◽  
State Dependent

scholarly journals Brevetoxin and Conotoxin Interactions with Single-Domain Voltage-Gated Sodium Channels from a Diatom and Coccolithophore

Marine Drugs ◽  
2021 ◽  
Vol 19 (3) ◽  
pp. 140
Author(s):  
Ping Yates ◽  
Julie A. Koester ◽  
Alison R. Taylor
Keyword(s):  
Ion Channels ◽  
Sodium Channels ◽  
Single Domain ◽  
Marine Diatom ◽  
Voltage Gated ◽  
Voltage Gated Sodium Channels ◽  
State Dependent ◽  
Steady State Inactivation ◽  
Outer Pore ◽  
Voltage Gated Ion Channels

The recently characterized single-domain voltage-gated ion channels from eukaryotic protists (EukCats) provide an array of novel channel proteins upon which to test the pharmacology of both clinically and environmentally relevant marine toxins. Here, we examined the effects of the hydrophilic µ-CTx PIIIA and the lipophilic brevetoxins PbTx-2 and PbTx-3 on heterologously expressed EukCat ion channels from a marine diatom and coccolithophore. Surprisingly, none of the toxins inhibited the peak currents evoked by the two EukCats tested. The lack of homology in the outer pore elements of the channel may disrupt the binding of µ-CTx PIIIA, while major structural differences between mammalian sodium channels and the C-terminal domains of the EukCats may diminish interactions with the brevetoxins. However, all three toxins produced significant negative shifts in the voltage dependence of activation and steady state inactivation, suggesting alternative and state-dependent binding conformations that potentially lead to changes in the excitability of the phytoplankton themselves.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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