Computational Approaches for Elucidating Protein-Protein Interactions in Cation Channel Signaling

Baichun       Hu; Xiaoming       Zheng; Ying       Wang; Jian       Wang; Fengjiao       Zhang
doi:10.2174/1389450120666190906154412
Abstract

Background: The lipid bilayer of the plasma membrane is impermeable to ions, yet changes in the flux of ions across the cell membrane are critical regulatory events in cells. Because of their regulatory roles in a range of physiological processes, such as electrical signaling in muscles and neurons, to name a few, these proteins are one of the most important drug targets.
Objective: This review mainly focused on the computational approaches for elucidating proteinprotein interactions in cation channel signaling.
Discussion: Due to continuously advanced facilities and technologies in computer sciences, the physical contacts of macromolecules of channel structures have been virtually visualized. Indeed, techniques like protein-protein docking, homology modeling, and molecular dynamics simulation are valuable tools for predicting the protein complex and refining channels with unreleased structures. Undoubtedly, these approaches will greatly expand the cation channel signaling research, thereby speeding up structure-based drug design and discovery.
Conclusion: We introduced a series of valuable computational tools for elucidating protein-protein interactions in cation channel signaling, including molecular graphics, protein-protein docking, homology modeling, and molecular dynamics simulation.
Keywords: Cation channels, molecular modeling, molecular dynamics simulation, protein-protein docking, lipid bilayer, plasma membrane.
« Previous Next »
Graphical Abstract

[1] 
Bartoszewski R, Matalon S, Collawn JF. Ion channels of the lung and their role in disease pathogenesis. Am J Physiol Lung Cell Mol Physiol  2017; 313(5): L859-72.
[http://dx.doi.org/10.1152/ajplung.00285.2017] [PMID:  29025712] 
[2] 
Kondratskyi A, Kondratska K, Skryma R, Klionsky DJ, Prevarskaya N. Ion channels in the regulation of autophagy. Autophagy  2018; 14(1): 3-21.
[http://dx.doi.org/10.1080/15548627.2017.1384887] [PMID:  28980859] 
[3] 
Murthy SE, Dubin AE, Patapoutian A. Piezos thrive under pressure: mechanically activated ion channels in health and disease. Nat Rev Mol Cell Biol  2017; 18(12): 771-83.
[http://dx.doi.org/10.1038/nrm.2017.92] [PMID:  28974772] 
[4] 
Koch-Nolte F, Eichhoff A, Pinto-Espinoza C, et al. Novel biologics targeting the P2X7 ion channel. Curr Opin Pharmacol  2019; 47: 110-8.
[http://dx.doi.org/10.1016/j.coph.2019.03.001] [PMID:  30986625] 
[5] 
Benemei S, Dussor G. TRP channels and migraine: recent developments and new therapeutic opportunities. Pharmaceuticals  2019; 12(2)E54
[http://dx.doi.org/10.3390/ph12020054] [PMID:  30970581] 
[6] 
Boyd CM, Bubeck D. Advances in cryoEM and its impact on β-pore forming proteins. Curr Opin Struct Biol  2018; 52: 41-9.
[http://dx.doi.org/10.1016/j.sbi.2018.07.010] [PMID:  30125772] 
[7] 
Dubochet J. On the development of electron cryo-microscopy (Nobel Lecture). Angew Chem Int Ed Engl  2018; 57(34): 10842-6.
[http://dx.doi.org/10.1002/anie.201804280] [PMID:  29984876] 
[8] 
Higgins MK, Lea SM. On the state of crystallography at the dawn of the electron microscopy revolution. Curr Opin Struct Biol  2017; 46: 95-101.
[http://dx.doi.org/10.1016/j.sbi.2017.06.005] [PMID:  28686957] 
[9] 
Balasuriya D, D’Sa L, Talker R, et al. A direct interaction between the sigma-1 receptor and the hERG voltage-gated K+ channel revealed by atomic force microscopy and homogeneous time-resolved fluorescence (HTRF®). J Biol Chem  2014; 289(46): 32353-63.
[http://dx.doi.org/10.1074/jbc.M114.603506] [PMID:  25266722] 
[10] 
Castro-Rodrigues AF, Zhao Y, Fonseca F, et al. The interaction between the drosophila eag potassium channel and the protein kinase camkii involves an extensive interface at the active site of the kinase. J Mol Biol  2018; 430(24): 5029-49.
[http://dx.doi.org/10.1016/j.jmb.2018.10.015] [PMID:  30381148] 
[11] 
Shiotani H, Miyata M, Mizutani K, et al. Interaction of nectin-2α with the auxiliary protein of the voltage-gated A-type K+ channel Kv4.2 dipeptidyl aminopeptidase-like protein at the boundary between the adjacent somata of clustered cholinergic neurons in the medial habenula. Mol Cell Neurosci  2019; 94: 32-40.
[http://dx.doi.org/10.1016/j.mcn.2018.11.001] [PMID:  30408526] 
[12] 
Sun XL, Yuan JF, Jin T, et al. Physical and functional interaction of Snapin with Cav1.3 calcium channel impacts channel protein trafficking in atrial myocytes. Cell Signal  2017; 30: 118-29.
[http://dx.doi.org/10.1016/j.cellsig.2016.11.019] [PMID:  27915047] 
[13] 
Yu G, Liu Y, Qin J, Wang Z, Hu Y, Wang F, et al. Mechanistic insights into the interaction of J. Biol Chem  2018; 293(47): 18207-17.
[http://dx.doi.org/10.1074/jbc.RA118.003997] 
[14] 
Findeisen F, Campiglio M, Jo H, et al. Stapled voltage-gated calcium channel (cav) α-interaction domain (aid) peptides act as selective protein-protein interaction inhibitors of cav function. ACS Chem Neurosci  2017; 8(6): 1313-26.
[http://dx.doi.org/10.1021/acschemneuro.6b00454] [PMID:  28278376] 
[15] 
Zhang Y, Zhu Y, He F. An overview of human protein databases and their application to functional proteomics in health and disease. Sci China Life Sci  2011; 54(11): 988-98.
[http://dx.doi.org/10.1007/s11427-011-4247-x] [PMID:  22173304] 
[16] 
Shkurin A, Vellido A. Using random forests for assistance in the curation of G-protein coupled receptor databases. Biomed Eng Online  2017; 16(Suppl. 1): 75.
[http://dx.doi.org/10.1186/s12938-017-0357-4] [PMID:  28830426] 
[17] 
Burley SK, Berman HM, Bhikadiya C, et al. RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res  2019; 47(D1): D464-74.
[http://dx.doi.org/10.1093/nar/gky1004] [PMID:  30357411] 
[18] 
The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res  2017; 45(D1): D158-69.
[http://dx.doi.org/10.1093/nar/gkw1099] [PMID:  27899622] 
[19] 
Laskowski RA. PDBsum: summaries and analyses of PDB structures. Nucleic Acids Res  2001; 29(1): 221-2.
[http://dx.doi.org/10.1093/nar/29.1.221] [PMID:  11125097] 
[20] 
Jenuth JP. The NCBI. Publicly available tools and resources on the Web. Methods Mol Biol  2000; 132: 301-12.
[PMID:  10547843] 
[21] 
Ulrich EL, Akutsu H, Doreleijers JF, et al. BioMagResBank. Nucleic Acids Res  2008; 36(Database issue): D402-8.
[PMID:  17984079] 
[22] 
Long SB, Campbell EB, Mackinnon R. Crystal structure of a mammalian voltage-dependent Shaker family K+ channel. Science  2005; 309(5736): 897-903.
[http://dx.doi.org/10.1126/science.1116269] [PMID:  16002581] 
[23] 
Sun J, MacKinnon R. Cryo-EM structure of a kcnq1/cam complex reveals insights into congenital long qt syndrome. Cell  2017; 169(6): 1042-50. e9
[24] 
Lee SY, Lee A, Chen J, MacKinnon R. Structure of the KvAP voltage-dependent K+ channel and its dependence on the lipid membrane. Proc Natl Acad Sci USA  2005; 102(43): 15441-6.
[http://dx.doi.org/10.1073/pnas.0507651102] [PMID:  16223877] 
[25] 
Whicher JR, MacKinnon R. Structure of the voltage-gated K+ channel Eag1 reveals an alternative voltage sensing mechanism. Science  2016; 353(6300): 664-9.
[http://dx.doi.org/10.1126/science.aaf8070] [PMID:  27516594] 
[26] 
Yan Z, Zhou Q, Wang L, Wu J, Zhao Y, Huang G, et al. Structure of the nav1.4-beta1 complex from electric eel. Cell  2017; 170(3): 470-82. e11
[27] 
Pan X, Li Z, Huang X, et al. Molecular basis for pore blockade of human Na+ channel Nav1.2 by the μ-conotoxin KIIIA. Science  2019; 363(6433): 1309-13.
[http://dx.doi.org/10.1126/science.aaw2999] [PMID:  30765605] 
[28] 
Hughes TET, Pumroy RA, Yazici AT, et al. Structural insights on TRPV5 gating by endogenous modulators. Nat Commun  2018; 9(1): 4198.
[http://dx.doi.org/10.1038/s41467-018-06753-6] [PMID:  30305626] 
[29] 
Saotome K, Singh AK, Yelshanskaya MV, Sobolevsky AI. Crystal structure of the epithelial calcium channel TRPV6. Nature  2016; 534(7608): 506-11.
[http://dx.doi.org/10.1038/nature17975] [PMID:  27296226] 
[30] 
Wu J, Yan Z, Li Z, et al. Structure of the voltage-gated calcium channel Ca(v)1.1 at 3.6 Å resolution. Nature  2016; 537(7619): 191-6.
[http://dx.doi.org/10.1038/nature19321] [PMID:  27580036] 
[31] 
Tang L, Gamal El-Din TM, Swanson TM, et al. Structural basis for inhibition of a voltage-gated Ca2+ channel by Ca2+ antagonist drugs. Nature  2016; 537(7618): 117-21.
[http://dx.doi.org/10.1038/nature19102] [PMID:  27556947] 
[32] 
Mariamé B, Kappler-Gratias S, Kappler M, Balor S, Gallardo F, Bystricky K. Real-time visualization and quantification of human cytomegalovirus replication in living cells using the anchor dna labeling technology. J Virol  2018; 92(18): e00571-18.
[http://dx.doi.org/10.1128/JVI.00571-18] [PMID:  29950406] 
[33] 
DeBlasio SL, Chavez JD, Alexander MM, et al. visualization of host-polerovirus interaction topologies using protein interaction reporter technology. J Virol  2015; 90(4): 1973-87.
[http://dx.doi.org/10.1128/JVI.01706-15] [PMID:  26656710] 
[34] 
Du QS, Cui J, Zhang CJ, He K. Visualization analysis of CRISPR/Cas9 gene editing technology studies. J Zhejiang Univ Sci B  2016; 17(10): 798-806.
[http://dx.doi.org/10.1631/jzus.B1601985] [PMID:  27704749] 
[35] 
BIOVIA DS. Discovery studio modeling environment. San Diego: Dassault Systèmes 2017.
[36] 
Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem  2006; 49(21): 6177-96.
[http://dx.doi.org/10.1021/jm051256o] [PMID:  17034125] 
[37] 
Yuan S, Chan HCS, Filipek S, Vogel H. PyMOL and inkscape bridge the data and the data visualization. Structure  2016; 24(12): 2041-2.
[http://dx.doi.org/10.1016/j.str.2016.11.012] [PMID:  27926832] 
[38] 
James T, Hsieh ML, Knipling L, Hinton D. Determining the architecture of a protein-dna complex by combining febabe cleavage analyses, 3-d printed structures, and the icm molsoft program. Methods Mol Biol  2015; 1334: 29-40.
[http://dx.doi.org/10.1007/978-1-4939-2877-4_3] [PMID:  26404142] 
[39] 
Pettersen EF, Goddard TD, Huang CC, et al. UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem  2004; 25(13): 1605-12.
[http://dx.doi.org/10.1002/jcc.20084] [PMID:  15264254] 
[40] 
Guex N, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis  1997; 18(15): 2714-23.
[http://dx.doi.org/10.1002/elps.1150181505] [PMID:  9504803] 
[41] 
Pikora M, Gieldon A. RASMOL AB - new functionalities in the program for structure analysis. Acta Biochim Pol  2015; 62(3): 629-31.
[http://dx.doi.org/10.18388/abp.2015_972] [PMID:  26317128] 
[42] 
Sales TT, Resende FF, Chaves NL, et al. Suppression of the Eag1 potassium channel sensitizes glioblastoma cells to injury caused by temozolomide. Oncol Lett  2016; 12(4): 2581-9.
[http://dx.doi.org/10.3892/ol.2016.4992] [PMID:  27698831] 
[43] 
Wang X, Chen Y, Zhang Y, et al. Eag1 voltage-dependent potassium channels: structure, electrophysiological characteristics, and function in cancer. J Membr Biol  2017; 250(2): 123-32.
[http://dx.doi.org/10.1007/s00232-016-9944-8] [PMID:  28160046] 
[44] 
Liu GX, Yu YC, He XP, et al. Expression of eag1 channel associated with the aggressive clinicopathological features and subtype of breast cancer. Int J Clin Exp Pathol  2015; 8(11): 15093-9.
[PMID:  26823849] 
[45] 
Wang X, Chen Y, Li J, et al. Tetrandrine, a novel inhibitor of ether-à-go-go-1 (Eag1), targeted to cervical cancer development. J Cell Physiol  2019; 234(5): 7161-73.
[http://dx.doi.org/10.1002/jcp.27470] [PMID:  30362536] 
[46] 
Kaczor AA, Bartuzi D, Stępniewski TM, Matosiuk D, Selent J. Protein-protein docking in drug design and discovery. Methods Mol Biol  2018; 1762: 285-305.
[http://dx.doi.org/10.1007/978-1-4939-7756-7_15] [PMID:  29594778] 
[47] 
Zhang Q, Feng T, Xu L, et al. Recent advances in protein-protein docking. Curr Drug Targets  2016; 17(14): 1586-94.
[http://dx.doi.org/10.2174/1389450117666160112112640] [PMID:  26758670] 
[48] 
Gundampati RK, Chikati R, Kumari M, et al. Protein-protein docking on molecular models of Aspergillus niger RNase and human actin: novel target for anticancer therapeutics. J Mol Model  2012; 18(2): 653-62.
[http://dx.doi.org/10.1007/s00894-011-1078-4] [PMID:  21562828] 
[49] 
Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem  2009; 30(16): 2785-91.
[http://dx.doi.org/10.1002/jcc.21256] [PMID:  19399780] 
[50] 
Roberts VA, Thompson EE, Pique ME, Perez MS, Ten Eyck LF. DOT2: Macromolecular docking with improved biophysical models. J Comput Chem  2013; 34(20): 1743-58.
[http://dx.doi.org/10.1002/jcc.23304] [PMID:  23695987] 
[51] 
Totrov M, Abagyan R. Rapid boundary element solvation electrostatics calculations in folding simulations: successful folding of a 23-residue peptide. Biopolymers  2001; 60(2): 124-33.
[http://dx.doi.org/10.1002/1097-0282(2001)60:2<124:AID-BIP1008>3.0.CO;2-S] [PMID:  11455546] 
[52] 
Orlev N, Shamir R, Shiloh Y. PIVOT: protein interacions visualizatiOn tool. Bioinformatics  2004; 20(3): 424-5.
[http://dx.doi.org/10.1093/bioinformatics/btg426] [PMID:  14960471] 
[53] 
Beglov D, Hall DR, Wakefield AE, et al. Exploring the structural origins of cryptic sites on proteins. Proc Natl Acad Sci USA  2018; 115(15): E3416-25.
[http://dx.doi.org/10.1073/pnas.1711490115] [PMID:  29581267] 
[54] 
Gabb HA, Jackson RM, Sternberg MJ. Modelling protein docking using shape complementarity, electrostatics and biochemical information. J Mol Biol  1997; 272(1): 106-20.
[http://dx.doi.org/10.1006/jmbi.1997.1203] [PMID:  9299341] 
[55] 
Pierce BG, Wiehe K, Hwang H, Kim BH, Vreven T, Weng Z. ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics  2014; 30(12): 1771-3.
[http://dx.doi.org/10.1093/bioinformatics/btu097] [PMID:  24532726] 
[56] 
Tovchigrechko A, Vakser IA. GRAMM-X public web server for protein-protein docking  Nucleic Acids Res  2006; 34(Web Server issue): W310-4.
[http://dx.doi.org/10.1093/nar/gkl206] 
[57] 
Ghoorah AW, Devignes MD, Smaïl-Tabbone M, Ritchie DW. Protein docking using case-based reasoning. Proteins  2013; 81(12): 2150-8.
[http://dx.doi.org/10.1002/prot.24433] [PMID:  24123156] 
[58] 
Gray JJ, Moughon S, Wang C, et al. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J Mol Biol  2003; 331(1): 281-99.
[http://dx.doi.org/10.1016/S0022-2836(03)00670-3] [PMID:  12875852] 
[59] 
van Zundert GCP, Rodrigues JPGLM, Trellet M, et al. The haddock2.2 web server: user-friendly integrative modeling of biomolecular complexes. J Mol Biol  2016; 428(4): 720-5.
[http://dx.doi.org/10.1016/j.jmb.2015.09.014] [PMID:  26410586] 
[60] 
Gabdoulline RR, Wade RC, Walther D. MolSurfer: two-dimensional maps for navigating three-dimensional structures of proteins. Trends Biochem Sci  1999; 24(7): 285-7.
[http://dx.doi.org/10.1016/S0968-0004(99)01412-7] [PMID:  10390619] 
[61] 
Rashid M, Ramasamy S, Raghava GP. A simple approach for predicting protein-protein interactions. Curr Protein Pept Sci  2010; 11(7): 589-600.
[http://dx.doi.org/10.2174/138920310794109120] [PMID:  20887258] 
[62] 
Camacho CJ, Gatchell DW. Successful discrimination of protein interactions. Proteins  2003; 52(1): 92-7.
[http://dx.doi.org/10.1002/prot.10394] [PMID:  12784373] 
[63] 
Dang S, van Goor MK, Asarnow D, et al. Structural insight into TRPV5 channel function and modulation. Proc Natl Acad Sci USA  2019; 116(18): 8869-78.
[http://dx.doi.org/10.1073/pnas.1820323116] [PMID:  30975749] 
[64] 
Zhang W, Na T, Peng JB. WNK3 positively regulates epithelial calcium channels TRPV5 and TRPV6 via a kinase-dependent pathway. Am J Physiol Renal Physiol  2008; 295(5): F1472-84.
[http://dx.doi.org/10.1152/ajprenal.90229.2008] [PMID:  18768590] 
[65] 
Schattling B, Fazeli W, Engeland B, et al. Activity of NaV1.2 promotes neurodegeneration in an animal model of multiple sclerosis. JCI Insight  2016; 1(19)e89810
[http://dx.doi.org/10.1172/jci.insight.89810] [PMID:  27882351] 
[66] 
Plant LD, Marks JD, Goldstein SA. SUMOylation of NaV1.2 channels mediates the early response to acute hypoxia in central neurons. eLife  2016; 5: 5.
[http://dx.doi.org/10.7554/eLife.20054] [PMID:  28029095] 
[67] 
Guan G, Zhao M, Xu X, et al. Abnormal changes in voltage-gated sodium channels subtypes NaV1.1, NaV1.2, NaV1.3, NaV1.6 and CaM/CaMKII pathway in low-grade astrocytoma. Neurosci Lett  2018; 674: 148-55.
[http://dx.doi.org/10.1016/j.neulet.2018.03.047] [PMID:  29578003] 
[68] 
Feldkamp MD, Yu L, Shea MA. Structural and energetic determinants of apo calmodulin binding to the IQ motif of the Na(V)1.2 voltage-dependent sodium channel. Structure  2011; 19(5): 733-47.
[http://dx.doi.org/10.1016/j.str.2011.02.009] [PMID:  21439835] 
[69] 
Vyas VK, Ukawala RD, Ghate M, Chintha C. Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci  2012; 74(1): 1-17.
[http://dx.doi.org/10.4103/0250-474X.102537] [PMID:  23204616] 
[70] 
Ung PM, Song W, Cheng L, et al. Inhibitor discovery for the human glut1 from homology modeling and virtual screening. ACS Chem Biol  2016; 11(7): 1908-16.
[http://dx.doi.org/10.1021/acschembio.6b00304] [PMID:  27128978] 
[71] 
Bohnuud T, Luo L, Wodak SJ, et al. A benchmark testing ground for integrating homology modeling and protein docking. Proteins  2017; 85(1): 10-6.
[http://dx.doi.org/10.1002/prot.25063] [PMID:  27172383] 
[72] 
Teng F, Sun J, Yu L, Li Q, Cui Y. Homology modeling and epitope prediction of Der f 33. Braz J Med Biol Res  2018; 51(5)e6213
[http://dx.doi.org/10.1590/1414-431x20186213] [PMID:  29561952] 
[73] 
Tomar JS, Peddinti RK. Optimized method for TAG protein homology modeling: In silico and experimental structural characterization. Int J Biol Macromol  2016; 88: 102-12.
[http://dx.doi.org/10.1016/j.ijbiomac.2016.03.047] [PMID:  27017978] 
[74] 
Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc  2010; 5(4): 725-38.
[http://dx.doi.org/10.1038/nprot.2010.5] [PMID:  20360767] 
[75] 
Waterhouse A, Bertoni M, Bienert S, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res  2018; 46(W1): W296-303.
[http://dx.doi.org/10.1093/nar/gky427] [PMID:  29788355] 
[76] 
Lambert C, Léonard N, De Bolle X, Depiereux E. ESyPred3D: Prediction of proteins 3D structures. Bioinformatics  2002; 18(9): 1250-6.
[http://dx.doi.org/10.1093/bioinformatics/18.9.1250] [PMID:  12217917] 
[77] 
Pandit SB, Zhang Y, Skolnick J. TASSER-Lite: an automated tool for protein comparative modeling. Biophys J  2006; 91(11): 4180-90.
[http://dx.doi.org/10.1529/biophysj.106.084293] [PMID:  16963505] 
[78] 
Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, Hamp T, et al. PredictProtein--an open resource for online prediction of
protein structural and functional features. Nucleic Acids Res  2014; 42(Web Server issue): W337-43.
[79] 
Wang Q, Canutescu AA, Dunbrack RL Jr. SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling. Nat Protoc  2008; 3(12): 1832-47.
[http://dx.doi.org/10.1038/nprot.2008.184] [PMID:  18989261] 
[80] 
Källberg M, Wang H, Wang S, et al. Template-based protein structure modeling using the RaptorX web server. Nat Protoc  2012; 7(8): 1511-22.
[http://dx.doi.org/10.1038/nprot.2012.085] [PMID:  22814390] 
[81] 
Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res  2004; 32(Web Server issue): W526-31.
[http://dx.doi.org/10.1093/nar/gkh468] 
[82] 
Zhu J, Wang S, Bu D, Xu J. Protein threading using residue co-variation and deep learning. Bioinformatics  2018; 34(13): i263-73.
[http://dx.doi.org/10.1093/bioinformatics/bty278] [PMID:  29949980] 
[83] 
Wang Z, Eickholt J, Cheng J. MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8. Bioinformatics  2010; 26(7): 882-8.
[http://dx.doi.org/10.1093/bioinformatics/btq058] [PMID:  20150411] 
[84] 
Wu Z, Li L, Xie F, et al. Activation of kcnq channels suppresses spontaneous activity in dorsal root ganglion neurons and reduces chronic pain after spinal cord injury. J Neurotrauma  2017; 34(6): 1260-70.
[http://dx.doi.org/10.1089/neu.2016.4789] [PMID:  28073317] 
[85] 
Nouvian R, Ruel J, Wang J, Guitton MJ, Pujol R, Puel JL. Degeneration of sensory outer hair cells following pharmacological blockade of cochlear KCNQ channels in the adult guinea pig. Eur J Neurosci  2003; 17(12): 2553-62.
[http://dx.doi.org/10.1046/j.1460-9568.2003.02715.x] [PMID:  12823462] 
[86] 
Chang A, Abderemane-Ali F, Hura GL, Rossen ND, Gate RE, Minor DL Jr. A Calmodulin c-lobe ca(2+)-dependent switch governs kv7 channel function. Neuron  2018; 97(4): 836-52. e6
[87] 
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol  1990; 215(3): 403-10.
[http://dx.doi.org/10.1016/S0022-2836(05)80360-2] [PMID:  2231712] 
[88] 
Remmert M, Biegert A, Hauser A, Söding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods  2011; 9(2): 173-5.
[http://dx.doi.org/10.1038/nmeth.1818] [PMID:  22198341] 
[89] 
Liu X, Shi D, Zhou S, Liu H, Liu H, Yao X. Molecular dynamics simulations and novel drug discovery. Expert Opin Drug Discov  2018; 13(1): 23-37.
[http://dx.doi.org/10.1080/17460441.2018.1403419] [PMID:  29139324] 
[90] 
Pradiba D, Aarthy M, Shunmugapriya V, Singh SK, Vasanthi M. Structural insights into the binding mode of flavonols with the active site of matrix metalloproteinase-9 through molecular docking and molecular dynamic simulations studies. J Biomol Struct Dyn  2018; 36(14): 3718-39.
[http://dx.doi.org/10.1080/07391102.2017.1397058] [PMID:  29068268] 
[91] 
Ge X, Mandava CS, Lind C, Åqvist J, Sanyal S. Complementary charge-based interaction between the ribosomal-stalk protein L7/12 and IF2 is the key to rapid subunit association. Proc Natl Acad Sci USA  2018; 115(18): 4649-54.
[http://dx.doi.org/10.1073/pnas.1802001115] [PMID:  29686090] 
[92] 
Kaczor AA, Targowska-Duda KM, Patel JZ, et al. Comparative molecular field analysis and molecular dynamics studies of α/β hydrolase domain containing 6 (ABHD6) inhibitors. J Mol Model  2015; 21(10): 250.
[http://dx.doi.org/10.1007/s00894-015-2789-8] [PMID:  26350245] 
[93] 
Mishra V, Pathak C. Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex. J Biomol Struct Dyn  2019; 37(8): 1968-91.
[http://dx.doi.org/10.1080/07391102.2018.1474804] [PMID:  29842849] 
[94] 
Case DA, Cheatham TE III, Darden T, et al. The Amber biomolecular simulation programs. J Comput Chem  2005; 26(16): 1668-88.
[http://dx.doi.org/10.1002/jcc.20290] [PMID:  16200636] 
[95] 
Kevin J. Bowers EC, Huafeng Xu, et al. Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the ACM/IEEE Conference on Supercomputing (SC06) 2006.
[96] 
Pronk S, Páll S, Schulz R, et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics  2013; 29(7): 845-54.
[http://dx.doi.org/10.1093/bioinformatics/btt055] [PMID:  23407358] 
[97] 
Plimpton S. Fast parallel algorithms for short–range molecular dynamics. J Tomout Phys  1995; 117(1): 1-19.
[http://dx.doi.org/10.1006/jcph.1995.1039] 
[98] 
Tripuraneni NS, Azam MA. Pharmacophore modeling, 3D-QSAR and docking study of 2-phenylpyrimidine analogues as selective PDE4B inhibitors. J Theor Biol  2016; 394: 117-26.
[http://dx.doi.org/10.1016/j.jtbi.2016.01.007] [PMID:  26804643] 
[99] 
Sahoo BR, Maharana J, Bhoi GK, et al. A conformational analysis of mouse Nalp3 domain structures by molecular dynamics simulations, and binding site analysis. Mol Biosyst  2014; 10(5): 1104-16.
[http://dx.doi.org/10.1039/C3MB70600A] [PMID:  24595807] 
Rights & Permissions Print Cite
Article Metrics
21
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/1389450120666190906154412	Print ISSN 1389-4501
Publisher Name Bentham Science Publisher	Online ISSN 1873-5592
Current Drug Targets

Computational Approaches for Elucidating Protein-Protein Interactions in Cation Channel Signaling

Abstract Play Pause

Graphical Abstract

Related Journals

Related Books

Abstract
