General Review Article

阐明阳离子通道信号传导中蛋白质与蛋白质相互作用的计算方法

卷 21, 期 2, 2020

页: [179 - 192] 页: 14

弟呕挨: 10.2174/1389450120666190906154412

价格: $65

摘要

背景:质膜的脂质双层不能渗透离子,但是离子穿过细胞膜的通量变化是细胞中的关键调节事件。由于它们在一系列生理过程中的调节作用,例如肌肉和神经元中的电信号传导,因此这些蛋白质是最重要的药物靶标之一。 目的:本综述主要侧重于阐明阳离子通道信号传导中蛋白质相互作用的计算方法。 讨论:由于计算机科学领域的不断先进的设施和技术,虚拟化了通道结构大分子的物理接触。确实,诸如蛋白质-蛋白质对接,同源性建模和分子动力学模拟之类的技术是预测蛋白质复合物和完善具有未释放结构的通道的有价值的工具。无疑,这些方法将极大地扩展阳离子通道信号的研究,从而加快基于结构的药物设计和发现。 结论:我们介绍了一系列有价值的计算工具,用于阐明阳离子通道信号传导中的蛋白质-蛋白质相互作用,包括分子图形,蛋白质-蛋白质对接,同源性建模和分子动力学模拟。

关键词: 阳离子通道,分子建模,分子动力学模拟,蛋白-蛋白对接,脂质双层,质膜。

图形摘要
[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
© 2024 Bentham Science Publishers | Privacy Policy