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Pliushcheuskaya, P.; Künze, G. Computational Drug Design on Ion Channels. Encyclopedia. Available online: https://encyclopedia.pub/entry/45331 (accessed on 13 June 2024).
Pliushcheuskaya P, Künze G. Computational Drug Design on Ion Channels. Encyclopedia. Available at: https://encyclopedia.pub/entry/45331. Accessed June 13, 2024.
Pliushcheuskaya, Palina, Georg Künze. "Computational Drug Design on Ion Channels" Encyclopedia, https://encyclopedia.pub/entry/45331 (accessed June 13, 2024).
Pliushcheuskaya, P., & Künze, G. (2023, June 08). Computational Drug Design on Ion Channels. In Encyclopedia. https://encyclopedia.pub/entry/45331
Pliushcheuskaya, Palina and Georg Künze. "Computational Drug Design on Ion Channels." Encyclopedia. Web. 08 June, 2023.
Computational Drug Design on Ion Channels
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Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development.

ion channels ion channel structure drug design virtual screening ligand docking molecular dynamics

1. Introduction

Ion channels are membrane proteins that form pores within cell membranes, allowing the passive transport of ions, such as Na+, K+, Ca2+, and Cl, from one side of the membrane to the other. More than 300 different ion channels, belonging to 13 different families, have been identified in the human genome [1]. Ion channels play important roles in a variety of biological processes, such as electric signaling in excitable cells, the regulation of nutrient transport in epithelial cells, and the maintenance of ion homeostasis in subcellular organelles (e.g., the regulation of calcium levels in the endoplasmic reticulum) [2][3]. Many human diseases are caused by the disruption of normal ion channel function [4][5][6]. Disorders resulting from mutations in ion channel encoding genes, so called channelopathies, include a variety of pathologies of the nervous, cardiovascular, and endocrine system. Because of their central pathophysiological functions, ion channels represent major drug targets and only 18% of currently used drugs target them. This makes ion channels the second largest drug target group [7].
In the past 25 years, there has been a significant increase in the number of ion channel structures. Starting from 1998 when the first three-dimensional structure of an ion channel, the KcsA channel from Streptomyces lividans, was solved with the help of X-ray crystallography [8], the number of ion channel structures has increased to more than one thousand five hundred [9]. This progress has been primarily driven by technological advancements in X-ray crystallography and cryo-electron microscopy (cryo-EM).

2. Computational Refinement of Cryo-EM Structures

As mentioned above, structure-based CADD methods require a three-dimensional structure of the protein target of interest. However, not for every protein has a three-dimensional structure been determined. Several tools exist for protein structure prediction. Homology modeling employs information from template structures of homologous proteins. Some common tools include SWISS-MODEL [10], RosettaCM [11], and Modeller [12]. However, with the advance in the artificial intelligence field, these methods were superseded by methods like AlphaFold [13], RoseTTAFold [14], or OmegaFold [15], which can accurately predict protein three-dimensional structures in the absence of template information and also provide a likelihood of the structure accuracy.
AlphaFold can also be used to refine protein models obtained from cryo-EM density maps. Terashi et al. [16] developed a deep learning-based model quality score that evaluates the likelihood of an amino acid residue’s position in the cryo-EM map. The regions of the protein that are classified as incorrect, according to this score, are subjected to remodeling with AlphaFold to generate a refined structure. The protocol was tested on membrane proteins and can be used to refine structures of ion channels.
Furthermore, Khan et al. [17] used MD simulations to refine the cryo-EM structure of the hERG ion channel. The hERG gene encodes a voltage-gated potassium channel (Kv11.1), wherein mutations are the main cause of long QT syndrome [18]. The cryo-EM structure of the human hERG channel was solved at medium resolution (3.8 Å) [19] and the data from the structure, in particular the positions of amino acid sidechains, were inconsistent to numerous experimental studies highlighting the importance of salt bridges and other interactions present in the VSD. Therefore, the structure was refined using the molecular dynamics flexible fitting (MDFF) method [20]. The bilayer-embedded structure, used in fitting, was obtained from a long MD simulation of the hERG channel structure of several microseconds. The refined structure showed the presence of salt bridges in the VSD that were known from experimental studies to be important for channel function. Additional salt bridge interactions were detected in the structure and confirmed in electrophysiological testing.
MD simulations were also used to refine the cryo-EM structure of the glycine receptor [21]. Glycine receptors belong to the family of pentameric ligand-gated ion channels and are targets for chronic pain treatments [22]. Dämgen et al. [21] performed all-atom MD simulations to refine the open state cryo-EM structure of the glycine receptor and described several interactions that stabilize the open state.

3. Characterization of Drug-Ion Channel Interactions by Molecular Dynamics Simulations

MD simulation is a widely used method in drug design, which can be used to investigate, e.g., how a potential active molecule influences ion channel gating and ion conduction processes. Houtman et al. [23] and Chen et al. [24] used MD coupled with pharmacophore modeling to identify new inhibitors of ATP-dependent potassium channels Kir6.1 and Kir6.2. Obtaining function mutations in KATP channels lead to the development of epilepsy and neonatal diabetes, known as the DEND syndrome. Existing drugs derived from sulfonylurea inhibit KATP channels but fail to elicit significant inhibition of disease-causing KATP channel mutants, which are known to be sulfonylurea resistant [25]. Thus, with the help of Martini 2.2-based [26], coarse-grained MD simulations, putative binding sites of the hit molecule betaxolol were identified. Three main binding sites were discovered and tested in more detail in all-atom MD simulation in Gromacs. In subsequent all-atom simulations, betaxolol was found to interact with a set of pore-lining residues in the protein, thus plugging the ion conduction pathway and explaining its mode of action. Complexes with betaxolol were tested in inhibition studies by the patch–clamp technique and showed inhibition (IC50) values of 27–37 μM towards the KATP channel. Moreover, betaxolol was also shown to inhibit sulfonylurea-resistant DEND-causing KATP channel mutants.
Yelshanskaya et al. [27] applied MD-based experiments to study the binding of trans-4-butylcyclohexane carboxylic acid (4-BCCA), which acts as an inhibitor of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors. Overactivation of the AMPA receptors results in seizure occurrences and epilepsy. Perampanel is known to inhibit AMPA receptors but may cause behavioral side effects [28][29].
Shi et al. [30] employed all-atom MD simulations to analyze the mode of action of zafirlukast on the Ca2+-activated chloride channel TMEM16A. TMEM16A is a drug target against lung adenocarcinoma [31]. Zafirlukast was identified as a potential drug candidate through a virtual screen of 1400 FDA-approved drugs. Using MD simulations, the authors showed that zafirlukast blocks the pore by binding to the nonselective inhibitor binding pocket of the TMEM16A channel. Zafirlukast inhibited the proliferation of lung adenocarcinoma cells in vitro and could significantly decrease tumor growth in mice in vivo. It was also assigned a better safety and stability profile compared to natural compounds against lung adenocarcinoma discovered by the same group.
Zimova et al. [32] used molecular docking and MD tools to characterize the binding site of the drug duloxetine on the TRPC5 ion channel. Duloxetine is used to treat severe painful disorders that are difficult to manage in clinical practice [33]. TRPC5 is a cold-sensitive, calcium-permeable, non-selective cation channel, which is known to be involved in the development of painful neuropathies [34]. The authors established that duloxetine suppresses TRPC5 action, which explains its analgesic effect. The molecular docking and all-atom MD simulation results suggested that duloxetine binds to the VSD in TRPC5, which was subsequently confirmed by point mutagenesis.
Drug repurposing is a strategy that seeks to find known drugs for the treatment of diseases that are outside of the original medical indication [35]. This strategy has also been applied in the development of drugs against COVID-19. Toft-Bertelsen et al. [36] demonstrated that amantadine and hexamethylene–amiloride are able to inhibit ion channel activity of Protein E from SARS-CoV-2. Amantadine is a well-known ion channel blocker and has been used already for several decades for the influenza treatment [37]. Toft-Bertelsen et al. used all-atom MD simulations to characterize the binding profile of amantadine, and observed that it binds to the amino acid residues in the pore region, thus blocking the current in Protein E and inhibiting its activity that directly relates to the virus replication and subsequent inflammation.

4. Identification of Ion Channel Ligands by Virtual Docking Techniques

Drug design endeavors require detailed understanding of how a small molecule binds to the target of interest. To achieve high binding affinity and selectivity, a ligand should ideally have a shape, charge, and hydrogen bond donor/acceptor pattern that complement those features in the receptor binding pocket. Molecular docking techniques try to predict low-energy, ligand-binding poses and are massively employed for in silico drug screening [38].
Abdelsayed et al. [39] conducted a virtual docking study combined with validation by electrophysiology to detect molecules that will bind to and restore the function of variants of the voltage-gated sodium NaV1.5 channel carrying loss-of-function (LoF) mutations. NaV1.5 is a cardiac ion channel and mutations in the NaV1.5 gene are associated with various arrythmias [40]. The majority of the existing NaV1.5 drugs (e.g., lidocaine [41], ranolazine [42]) target gain-of-function mutants of NaV1.5 by blocking the ion channel pore [43]. However, no ligands that will activate and rescue LoF mutants of NaV1.5 exist [44]. Abdelsayed et al. [39] performed virtual docking using the ZINC database and obtained 21 hits including molecules with sulfonamide and carboxamide groups. These small molecules were found to bind in side fenestrations of the NaV1.5 channel structure, leaving the central ion conduction pathway passable, which is essential for their activity enhancing action. Based on favorable affinity for the fenestrations, six compounds with a hydrophobic core were selected from the initial 21 hits and screened against the NaV1.5 channel. The experiments suggested that these six molecules have a higher affinity to fenestrations, thus representing a potential for treating LoF-arrythmias.
Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels that are involved in a variety of biological processes and neuronal disorders, e.g., schizophrenia, epilepsy, and Alzheimer’s disease (AD) [45]. For example, it was shown that the α4β2- and α7-subtypes of nAChRs possesses a high affinity towards the Aβ peptide, which is known to be a major causing factor for the onset and progression of AD. Thus, interactions between the α4β2 and α7 nAChRs and Aβ oligomers may induce malfunction of the synaptic neuron transfer [46][47]. Batista et al. [48] designed a docking-based pharmacophore model that could be used to search for new nAChR inhibitors. The authors started by performing docking of known nAChR ligands (~200 molecules) with available bioactivity data and extensively described critical contacts and binding modes to obtain a pharmacophore model. The sum of contacts present in the docking models were correlated with the biological activity of known ligands, and the model was used to predict the pKi values for each new molecule. The pharmacophore models were validated using ZINC and ChEMBL databases. Approximately 1500 decoys were determined during the validation procedure, which showed compliance with the pharmacophoric maps. Receiver operating characteristic (ROC) values lied suitably within 0.86–0.93 range.
Doñate-Macian et al. [49] performed a large-scale docking experiment on the transient receptor potential vanilloid 4 (TRPV4) cation channel. TRPV4 takes part in numerous biological processes in the human organism. It is involved in the decrease of the regulatory volume in the respiratory epithelium, in the generation of the myofibroblast, and in the development of fibrosis. Thus, mutations or irregularities of the TRPV4 function may lead to asthma development and lung fibrosis. Furthermore, normal TRPV4 functioning is important to fight toxic substances and pathogens [50]. Doñate-Macian et al. [49] performed a docking-based virtual screening of the NCI database [51], consisting of ca. 250,000 compounds, on the TRPV4 channel structure. Based on the docking scores, 40 hit molecules were identified, which were subjected to the evaluation of their biological effect on TRPV4. Three out of forty hit compounds showed an inhibition activity against TRPV4. These three compounds could be further exploited for the development of therapeutics for an antiviral strategy via TRPV4 [52].

5. Prediction of Ion Channel Ligands with Structure-Based Virtual Screening

Electrophysiological experiments, (e.g., patch-clamp measurements) or fluorescence-based assays, are often used for ion channel screening purposes [53]. However, screening a huge number of molecules by in vitro experiments is not only slow, but also highly expensive. This is why virtual screening represents an approach to compensate for the drawbacks of electrophysiological testing [54].
Liu et al. [55] carried out a docking-based virtual screening experiment to identify blockers of Shaker voltage-gated potassium (K+) channels. Blocking of the K+ channels’ pore is a widespread approach to design drugs with antiarrhythmic action [56]. New K+ blockers were identified by performing a structure-based virtual screening of the MDL Available Chemicals Directory (ACD) database [57] consisting of ~600,000 commercially available chemicals. First, Liu et al. selected 300 compounds out of the ACD database which exhibited the best docking scores. Afterwards, 20 out of 300 candidates were selected according to the drug-like requirements (logP, structure optimization, etc.). These 20 molecules were then tested in electrophysiological assays with six compounds showing an inhibition of voltage-activated K+ current. Furthermore, Liu et al. calculated binding free energies of the final six compounds, which were also in agreement with their inhibitory potencies (IC50 values).
Structure-based virtual screening was also applied in the study of Pegoraro et al. [58] to identify blockers of the KV1.3 ion channel, which is known to be a target in autoimmune diseases and T-cell proliferation [59]. Pegoraro et al. screened a compiled database of 3.3 million commercially and virtually available compounds, and 500 molecules were selected after the docking and optimization procedures. Blocking activity was found for 37 compounds using patch-clamp technology. The study identified molecules exhibiting a new structural biaryl core that exert an inhibitory effect on T-cell proliferation.
Llanos et al. [60] performed a virtual screening on the transient receptor potential vanilloid 1 (TRPV1) receptor as a part of drug repurposing campaign. TRPV1 is a nonselective cation channel that is known to regulate the body temperature [61]. Several TRPV1 antagonists were found to have anticonvulsant activity [62], but because of their hypothermic effect, the development of these types of drugs was hindered [63]. Llanos et al. [60] designed a docking model and applied it to screen the DrugBank database containing over 10,000 approved drugs. They obtained three hit scaffolds represented by montelukast, novobiocin, and cinnarizine molecules. All three molecules showed nanomolar inhibition against TRPV1 and antiseizure activity in in vivo assays.
Using structure-based virtual screening, Pasqualetto et al. [64] identified novel antagonists of the P2X purinoceptor 7 (P2X7). P2X7 belongs to the family of ligand-gated ion channels, which are activated upon the binding of ATP. P2X7 receptor stimulates inflammatory and infection processes in the human body [65]. By performing docking-based screening of the Specs library [66] on the P2X7 structure, 17 hit molecules were identified and subjected to evaluation of their biological activity. Among them, compound GP-25 demonstrated an inhibition activity in the micromolar range. Pasqualetto et al. deduced a pharmacophore model based on the interaction pattern between the GP-25 ligand and the P2X7 receptor. The model was then used to screen commercially available GP-25 analogues yielding several compounds with P2X7 antagonistic activity.
Virtual screening can be further facilitated by the use of machine learning methods, which have become very popular these days due to advancements in the mathematical theory on machine learning and computational resources [67]. Mostly ligand-based virtual screening approaches are aided with machine learning. For example, Kong et al. [68] designed multiple machine learning frameworks based on ligand molecular fingerprints to screen the ChEMBL database to identify inhibitors of NaV1.5 as potential anti-arrhythmic drugs.

References

  1. Tanner, M.R.; Beeton, C. Differences in Ion Channel Phenotype and Function between Humans and Animal Models. Front. Biosci. Landmark 2018, 23, 43–64.
  2. Zaydman, M.A.; Silva, J.R.; Cui, J. Ion Channel Associated Diseases: Overview of Molecular Mechanisms. Chem. Rev. 2012, 112, 6319–6333.
  3. Cox, B. Ion Channel Drug Discovery: A Historical Perspective; University of Sussex: Brighton, UK, 2015; Volume 2015, ISBN 9781849735087.
  4. Yan, P.; Ke, B.; Fang, X. Ion Channels as a Therapeutic Target for Renal Fibrosis. Front. Physiol. 2022, 13, 1019028.
  5. Boyle, Y.; Johns, T.G.; Fletcher, E. V Potassium Ion Channels in Malignant Central Nervous System Cancers. Cancers 2022, 14, 4767.
  6. Fakih, D.; Migeon, T.; Moreau, N.; Baudouin, C.; Réaux-Le Goazigo, A.; Mélik Parsadaniantz, S. Transient Receptor Potential Channels: Important Players in Ocular Pain and Dry Eye Disease. Pharmaceutics 2022, 14, 1859.
  7. Santos, R.; Ursu, O.; Gaulton, A.; Bento, A.P.; Donadi, R.S.; Bologa, C.G.; Karlsson, A.; Al-lazikani, B.; Hersey, A.; Oprea, T.I.; et al. A Comprehensive Map of Molecular Drug Targets. Nat. Rev. Drug Discov. 2016, 16, 19–34.
  8. Doyle, D.A.; Cabral, M.; Pfuetzner, R.A.; Kuo, A.; Gulbis, J.M.; Cohen, S.L.; Chait, B.T.; Mackinnon, R. The Structure of the Potassium Channel: Molecular Basis of K+ Conduction and Selectivity. Science 1998, 280, 69–77.
  9. Membrane Proteins of Known Structure. Available online: https://blanco.biomol.uci.edu/mpstruc/ (accessed on 10 March 2023).
  10. Schwede, T.; Kopp, J.; Guex, N.; Peitsch, M.C. SWISS-MODEL: An Automated Protein Homology-Modeling Server. Nucleic Acids Res. 2003, 31, 3381–3385.
  11. Song, Y.; Dimaio, F.; Wang, R.Y.R.; Kim, D.; Miles, C.; Brunette, T.; Thompson, J.; Baker, D. High-Resolution Comparative Modeling with RosettaCM. Structure 2013, 21, 1735–1742.
  12. Modeller. Available online: https://salilab.org/modeller/ (accessed on 20 February 2023).
  13. AlphaFold. Available online: https://www.deepmind.com/research/highlighted-research/alphafold (accessed on 20 February 2023).
  14. Baek, M.; DiMaio, F.; Anishchenko, I.; Dauparas, J.; Ovchinnikov, S.; Lee, G.R.; Wang, J.; Cong, Q.; Kinch, L.N.; Schaeffer, R.D.; et al. Accurate Prediction of Protein Structures and Interactions Using a Three-Track Neural Network. Science 2021, 373, 871–876.
  15. Wu, R.; Ding, F.; Wang, R.; Shen, R.; Zhang, X.; Luo, S.; Su, C.; Wu, Z.; Xie, Q.; Bergerc, B.; et al. High-Resolution de Novo Structure Prediction from Primary Sequence. bioRxiv 2022.
  16. Terashi, G.; Wang, X.; Kihara, D. Protein Model Refinement for Cryo-EM Maps Using AlphaFold 2 and the DAQ Score Research Papers. Acta Crystallogr. Sect. D Struct. Biol. 2023, 79, 10–21.
  17. Khan, H.M.; Guo, J.; Duff, H.J.; Tieleman, D.P.; Noskov, S.Y. Refinement of a Cryo-EM Structure of HERG: Bridging Structure and Function. Biophys. J. 2021, 120, 738–748.
  18. Vandenberg, J.I.; Perry, M.D.; Perrin, M.J.; Mann, S.A.; Ke, Y.; Hill, A.P. HERG K(+) Channels: Structure, Function, and Clinical Significance. Physiol. Rev. 2012, 92, 1393–1478.
  19. Wang, W.; MacKinnon, R. Cryo-EM Structure of the Open Human Ether-à-Go-Go-Related K+ Channel HERG. Cell 2017, 169, 422–430.e10.
  20. Trabuco, L.G.; Villa, E.; Schreiner, E.; Harrison, C.B.; Schulten, K. Molecular Dynamics Flexible Fitting: A Practical Guide to Combine Cryo-Electron Microscopy and X-Ray Crystallography. Methods 2009, 49, 174–180.
  21. Dämgen, M.A.; Biggin, P.C. A Refined Open State of the Glycine Receptor Obtained via Molecular Dynamics Simulations. Structure 2020, 28, 130–139.e2.
  22. San Martín, V.P.; Sazo, A.; Utreras, E.; Moraga-Cid, G.; Yévenes, G.E. Glycine Receptor Subtypes and Their Roles in Nociception and Chronic Pain. Front. Mol. Neurosci. 2022, 15, 848642.
  23. Houtman, M.J.C.; Friesacher, T.; Chen, X.; Zangerl-Plessl, E.M.; van der Heyden, M.A.G.; Stary-Weinzinger, A. Development of IKATP Ion Channel Blockers Targeting Sulfonylurea Resistant Mutant KIR6.2 Based Channels for Treating DEND Syndrome. Front. Pharmacol. 2022, 12, 4051.
  24. Chen, X.; Garon, A.; Wieder, M.; Houtman, M.J.C.; Zangerl-Plessl, E.M.; Langer, T.; Van Der Heyden, M.A.G.; Stary-Weinzinger, A. Computational Identification of Novel Kir6 Channel Inhibitors. Front. Pharmacol. 2019, 10, 549.
  25. Ashcroft, F.M.; Puljung, M.C.; Vedovato, N. Neonatal Diabetes and the KATP Channel: From Mutation to Therapy. Trends Endocrinol. Metab. 2017, 28, 377–387.
  26. De Jong, D.H.; Singh, G.; Bennett, W.F.D.; Arnarez, C.; Wassenaar, T.A.; Schäfer, L.V.; Periole, X.; Tieleman, D.P.; Marrink, S.J. Improved Parameters for the Martini Coarse-Grained Protein Force Field. J. Chem. Theory Comput. 2013, 9, 687–697.
  27. Yelshanskaya, M.V.; Singh, A.K.; Narangoda, C.; Williams, R.S.B.; Kurnikova, M.G.; Sobolevsky, A.I. Structural Basis of AMPA Receptor Inhibition by Trans-4-Butylcyclohexane Carboxylic Acid. Br. J. Pharmacol. 2022, 179, 3628–3644.
  28. Hanada, T. Ionotropic Glutamate Receptors in Epilepsy: A Review Focusing on Ampa and Nmda Receptors. Biomolecules 2020, 10, 464.
  29. Rugg-Gunn, F. Adverse Effects and Safety Profile of Perampanel: A Review of Pooled Data. Epilepsia 2014, 55, 13–15.
  30. Shi, S.; Ma, B.; Sun, F.; Qu, C.; Li, G.; Shi, D.; Liu, W.; Zhang, H.; Hailong, A. Zafirlukast Inhibits the Growth of Lung Adenocarcinoma via Inhibiting TMEM16A Channel Activity. J. Biol. Chem. 2022, 298, 101731.
  31. Ji, Q.; Guo, S.; Wang, X.; Pang, C.; Zhan, Y.; Chen, Y.; An, H. Recent Advances in TMEM16A: Structure, Function, and Disease. J. Cell. Physiol. 2019, 234, 7856–7873.
  32. Zimova, L.; Ptakova, A.; Mitro, M.; Krusek, J.; Vlachova, V. Activity Dependent Inhibition of TRPC1/4/5 Channels by Duloxetine Involves Voltage Sensor-like Domain. Biomed. Pharmacother. 2022, 152, 113262.
  33. Rodrigues-Amorim, D.; Olivares, J.M.; Spuch, C.; Rivera-Baltanás, T. A Systematic Review of Efficacy, Safety, and Tolerability of Duloxetine. Front. Psychiatry 2020, 11, 554899.
  34. Zimmermann, K.; Lennerz, J.K.; Hein, A.; Link, A.S.; Stefan Kaczmarek, J.; Delling, M.; Uysal, S.; Pfeifer, J.D.; Riccio, A.; Clapham, D.E. Transient Receptor Potential Cation Channel, Subfamily C, Member 5 (TRPC5) Is a Cold-Transducer in the Peripheral Nervous System. Proc. Natl. Acad. Sci. USA 2011, 108, 18114–18119.
  35. Pushpakom, S.; Iorio, F.; Eyers, P.A.; Escott, K.J.; Hopper, S.; Wells, A.; Doig, A.; Guilliams, T.; Latimer, J.; McNamee, C.; et al. Drug Repurposing: Progress, Challenges and Recommendations. Nat. Rev. Drug Discov. 2018, 18, 41–58.
  36. Toft-Bertelsen, T.L.; Jeppesen, M.G.; Tzortzini, E.; Xue, K.; Giller, K.; Becker, S.; Mujezinovic, A.; Bentzen, B.H.; Andreas, L.B.; Kolocouris, A.; et al. Amantadine Has Potential for the Treatment of COVID-19 Because It Inhibits Known and Novel Ion Channels Encoded by SARS-CoV-2. Commun. Biol. 2021, 4, 1347.
  37. Jefferson, T.; Demicheli, V.; Di Pietrantonj, C.; Rivetti, D. Amantadine and Rimantadine for Influenza A in Adults. Cochrane Database Syst. Rev. 2006, 2006, CD001169.
  38. Meng, X.-Y.; Zhang, H.-X.; Mezei, M.; Cui, M. Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery. Curr. Comput. Aided Drug Des. 2012, 7, 146–157.
  39. Abdelsayed, M.; Page, D.; Ruben, P.C. ARumenamides: A Novel Class of Potential Antiarrhythmic Compounds. Front. Pharmacol. 2022, 13, 976903.
  40. Han, D.; Tan, H.; Sun, C.; Li, G. Dysfunctional Nav1.5 Channels Due to SCN5A Mutations. Exp. Biol. Med. 2018, 243, 852–863.
  41. Balserfi, J.R.; Nuss, H.B.; Romashko, D.N.; Marban, E. Functional Consequences of Lidocaine Binding to Slow-Inactivated Sodium Channels. J. Gen. Physiol. 1996, 107, 643–658.
  42. Sokolov, S.; Peters, C.H.; Rajamani, S.; Ruben, P.C. Proton-Dependent Inhibition of the Cardiac Sodium Channel Nav1.5 by Ranolazine. Front. Pharmacol. 2013, 4, 78.
  43. Potet, F.; Egecioglu, D.E.; Burridge, P.W.; George, A.L. GS-967 and Eleclazine Block Sodium Channels in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes. Mol. Pharmacol. 2020, 98, 540–547.
  44. Urdaneta, R.K.F.; Mcarthur, J.R.; Ohm, M.P.G.; Gaudet, R.; Tikhonov, D.B.; Zhorov, B.S.; French, R.J. Batrachotoxin Acts as a Stent to Hold Open Homotetrameric Prokaryotic Voltage-Gated Sodium Channels. J. Gen. Physiol. 2019, 151, 186–199.
  45. Jensen, A.A.; Frølund, B.; Liljefors, T.; Krogsgaard-Larsen, P. Neuronal Nicotinic Acetylcholine Receptors: Structural Revelations, Target Identifications, and Therapeutic Inspirations. J. Med. Chem. 2005, 48, 4705–4745.
  46. Lasala, M.; Fabiani, C.; Corradi, J.; Antollini, S.; Bouzat, C. Molecular Modulation of Human α 7 Nicotinic Receptor by Amyloid- β Peptides. Front. Cell Neurosci. 2019, 13, 37.
  47. Laikowski, M.M.; Reisdorfer, F.; Moura, S. NAChR A4β2 Subtype and Their Relation with Nicotine Addiction, Cognition, Depression and Hyperactivity Disorder. Curr. Med. Chem. 2018, 26, 3792–3811.
  48. Batista, V.S.; Gonçalves, A.M. Pharmacophore Mapping Combined with DbCICA Reveal New Structural Features for the Development of Novel Ligands Targeting A4β2 and A7 Nicotinic Acetylcholine Receptors. Molecules 2022, 27, 8236.
  49. Doñate-Macian, P.; Duarte, Y.; Rubio-Moscardo, F.; Pérez-Vilaró, G.; Canan, J.; Díez, J.; González-Nilo, F.; Valverde, M.A. Structural Determinants of TRPV4 Inhibition and Identification of New Antagonists with Antiviral Activity. Br. J. Pharmacol. 2022, 179, 3576–3591.
  50. Rajan, S.; Schremmer, C.; Weber, J.; Alt, P.; Geiger, F.; Dietrich, A. Ca2+ Signaling by TRPV4 Channels in Respiratory Function and Disease. Cells 2021, 10, 822.
  51. NCI Open Database Compounds. Available online: https://cactus.nci.nih.gov/download/nci/ (accessed on 20 February 2023).
  52. Valverde, M.A.; Diez, J. The TRPV4 Channel Links Calcium in Fl Ux to DDX3X Activity and Viral Infectivity. Nat. Commun. 2018, 9, 2307.
  53. Dallas, M. Patch Clamp Physiology; Molecular Devices, LLC.: San Jose, CA, USA, 2021; ISBN 9781071608173.
  54. Walters, W.P.; Stahl, M.T.; Murcko, M.A. Virtual Screening—An Overview. Drug Discov. Today 1998, 3, 160–178.
  55. Liu, H.; Gao, Z.B.; Yao, Z.; Zheng, S.; Li, Y.; Zhu, W.; Tan, X.; Luo, X.; Shen, J.; Chen, K.; et al. Discovering Potassium Channel Blockers from Synthetic Compound Database by Using Structure-Based Virtual Screening in Conjunction with Electrophysiological Assay. J. Med. Chem. 2007, 50, 83–93.
  56. Wulff, H.; Castle, N.A.; Pardo, L.A. Voltage-Gated Potassium Channels as Therapeutic Targets. Nat. Rev. Drug Discov. 2009, 8, 982–1001.
  57. MDL®. Available Chemicals Directory. Available online: http://www.mdli.com/acd/ (accessed on 13 March 2023).
  58. Pegoraro, S.; Lang, M.; Dreker, T.; Kraus, J.; Hamm, S.; Meere, C.; Feurle, J.; Tasler, S.; Prütting, S.; Kuras, Z.; et al. Inhibitors of Potassium Channels KV1.3 and IK-1 as Immunosuppressants. Bioorganic Med. Chem. Lett. 2009, 19, 2299–2304.
  59. Teisseyre, A.; Palko-Labuz, A.; Sroda-Pomianek, K.; Michalak, K. Voltage-Gated Potassium Channel Kv1.3 as a Target in Therapy of Cancer. Front. Oncol. 2019, 9, 933.
  60. Llanos, M.A.; Enrique, N.; Sbaraglini, M.L.; Garofalo, F.M.; Talevi, A.; Gavernet, L.; Mart, P. Structure-Based Virtual Screening Identifies Novobiocin, Montelukast, and Cinnarizine as TRPV1 Modulators with Anticonvulsant Activity In Vivo. J. Chem. Inf. Model. 2022, 62, 3008–3022.
  61. Montell, C.; Caterina, M.J. Thermoregulation: Channels That Are Cool to the Core. Curr. Biol. 2007, 17, 885–887.
  62. Cho, S.J.; Vaca, M.A.; Miranda, C.J.; Gouemo, P.N. Inhibition of Transient Potential Receptor Vanilloid Type 1 Suppresses Seizure Susceptibility in the Genetically Epilepsy-Prone Rat. CNS Neurosci. Ther. 2018, 24, 18–28.
  63. Garami, A.; Pakai, E.; McDonald, H.A.; Reilly, R.M.; Gomtsyan, A.; Corrigan, J.J.; Pinter, E.; Zhu, D.X.D.; Lehto, S.G.; Gavva, N.R.; et al. TRPV1 Antagonists That Cause Hypothermia, Instead of Hyperthermia, in Rodents: Compounds’ Pharmacological Profiles, in Vivo Targets, Thermoeffectors Recruited and Implications for Drug Development. Acta Physiol. 2018, 223, e13038.
  64. Pasqualetto, G.; Zuanon, M.; Brancale, A.; Young, M.T. Identification of a Novel P2X7 Antagonist Using Structure-Based Virtual Screening. Front. Pharmacol. 2023, 13, 1094607.
  65. Di Virgilio, F.; Dal Ben, D.; Sarti, A.C.; Giuliani, A.L.; Falzoni, S. Review the P2X7 Receptor in Infection and Inflammation. Immunity 2017, 47, 15–31.
  66. Specs. Available online: https://www.specs.net (accessed on 20 February 2023).
  67. Kimber, T.B.; Chen, Y.; Volkamer, A. Deep Learning in Virtual Screening: Recent Applications and Developments. Int. J. Mol. Sci. 2021, 22, 4435.
  68. Kong, W.; Huang, W.; Peng, C.; Zhang, B.; Duan, G.; Ma, W.; Huang, Z. Multiple Machine Learning Methods Aided Virtual Screening of NaV1.5 Inhibitors. J. Cell. Mol. Med. 2023, 27, 266–276.
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