[1]
Domene CS, Haider MSP. Sansom, Ion channel structures: a review of recent progress. Curr Opin Drug Discov Devel 2003; 6(5): 611-9.
[2]
Bagal SK, Brown AD, Cox PJ, et al. Ion channels as therapeutic targets: a drug discovery perspective. J Med Chem 2013; 56(3): 593-624.
[3]
Ger MF, Rendon G, Tilson JL, Jakobsson E, et al. Domain-based identification and analysis of glutamate receptor ion channels and their relatives in prokaryotes. PLoS One 2010; 5(10): e12827.
[4]
Tabassum N, Ahmed F. Ion Channels and their Modulation. Eur J Pharm Sci 2011; 1(1): 20-5.
[5]
Bech-Hansen NT, Naylor MJ, Maybaum TA, et al. Loss-of-function mutations in a calcium-channel alpha1-subunit gene in Xp11.23 cause incomplete X-linked congenital stationary night blindness. Nat Genet 1998; 19(3): 264-7.
[6]
Jentsch TJ. Neuronal KCNQ potassium channels: physiology and role in disease. Nat Rev Neurosci 2000; 1(1): 21-30.
[7]
Peters DJ, Spruit L, Saris JJ, et al. Chromosome 4 localization of a second gene for autosomal dominant polycystic kidney disease. Nat Genet 1993; 5(4): 359-62.
[8]
Curran ME, Splawski I, Timothy KW, et al. A molecular basis for cardiac arrhythmia: HERG mutations cause long QT syndrome. Cell 1995; 80(5): 795-803.
[9]
Wang Q, Shen J, Splawski I, et al. SCN5A mutations associated with an inherited cardiac arrhythmia, long QT syndrome. Cell 1995; 80(5): 805-11.
[10]
Lafreniere RG, Cader MZ, Poulin JF, et al. A dominant-negative mutation in the TRESK potassium channel is linked to familial migraine with aura. Nat Med 2010; 16(10): 1157-U1501.
[11]
Kaczorowski GJ, McManus OB, Priest BT, et al. Ion channels as drug targets: The next GPCRs. JGP 2008; 131(5): 399-405.
[12]
Sheu S-S, Lederer W. Lidocaine’s negative inotropic and antiarrhythmic actions. Dependence on shortening of action potential duration and reduction of intracellular sodium activity. Circ Res 1985; 57(4): 578-90.
[13]
Skov MJ, Beck JC, de Kater AW, et al. Nonclinical safety of ziconotide: an intrathecal analgesic of a new pharmaceutical class. Int J Toxicol 2007; 26(5): 411-21.
[14]
Schmidtko A, Lötsch J, Freynhagen R, Geisslinger G, et al. Ziconotide for treatment of severe chronic pain. Lancet 2010; 375(9725): 1569-77.
[15]
Santos R, Ursu O, Gaulton A, et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov 2017; 16(1): 19.
[16]
Gabashvili IS, Sokolowski BH, Morton CC, Giersch AB, et al. Ion Channel Gene Expression in the Inner Ear. J Assoc Res Otolaryngol 2007; 8(3): 305-28.
[17]
Consortium U. UniProt: the universal protein knowledgebase. Nucleic Acids Res 2018; 46(5): 2699.
[18]
Saha S, Zack J, Singh B, Raghava GP, et al. VGIchan: prediction and classification of voltage-gated ion channels. Genomics Proteomics Bioinformatics 2006; 4(4): 253-8.
[19]
Chen W, Lin H. Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine. Comput Biol Med 2012; 42(4): 504-7.
[20]
Liu LX, Li ML, Tan FY, et al. Local sequence information‐based support vector machine to classify voltage‐gated potassium channels. Acta Biochim Biophys Sin (Shanghai) 2006; 38(6): 363-71.
[21]
Liu W, Deng EZ, Chen W, Lin H. Identifying the subfamilies of voltage-gated potassium channels using feature selection technique. Int J Mol Sci 2014; 15(7): 12940-51.
[22]
Gao J, Cui W, Sheng Y, et al. PSIONplus: Accurate sequence-based predictor of ion channels and their types. PLoS One 2016; 11(4): e0152964.
[23]
Lin H, Ding H. Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J Theor Biol 2011; 269(1): 64-9.
[24]
Tiwari AK, Srivastava R. An efficient approach for the prediction of ion channels and their subfamilies. Comput Biol Chem 2015; 58: 205-21.
[25]
Zhao YW, Su ZD, Yang W, et al. IonchanPred 2.0: a tool to predict ion channels and their types. Int J Mol Sci 2017; 18(9)
[26]
Altschul SF, Madden TL, Schäffer AA, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25(17): 3389-402.
[27]
Cao R, Cheng J. Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks. Methods 2016; 93: 84-91.
[28]
Cao R, Cheng J. Protein single-model quality assessment by feature-based probability density functions. Sci Rep 2016; 6: 23990.
[29]
Cao R, Freitas C, Chan L, et al. ProLanGO: Protein function prediction
using neural machine translation based on a recurrent neural
network. Mol 2017; 22(10).
[30]
Cao R, Wang Z, Wang Y, Cheng J. SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines. BMC Bioinformatics 2014; 15: 120.
[31]
Meng F, Uversky VN, Kurgan L. Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions. Cell Mol Life Sci 2017; 74(17): 3069-90.
[32]
Hayat M, Khan A. Mem-PHybrid: Hybrid features-based prediction system for classifying membrane protein types. Anal Biochem 2012; 424(1): 35-44.
[33]
Meng F, Wang C, Kurgan L. fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization. BMC Bioinformatics 2018; 18(1): 580.
[34]
Mishra NK, Chang J, Zhao PX. Prediction of membrane transport proteins and their substrate specificities using primary sequence information. PLoS One 2014; 9(6): e100278.
[35]
Peng Z, Kurgan L. High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res 2015; 43(18): e121-1.
[36]
Gao J, Eshel F, Yaoqi Z, Jishou R, Lukasz K. BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS One 2012; 7(6): e40104.
[37]
Xianfang W, Wang J, Wang X, Zhang Y. Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model. J BioMed Res Int 2017; 2017: 8.
[38]
Nugent T, Jones DT. Detecting pore-lining regions in transmembrane protein sequences. BMC Bioinformatics 2012; 13: 169.
[39]
Zheng C, Kurgan L. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments. BMC Bioinformatics 2008; 9: 430-0.
[40]
Yan J, Marcus M, Kurgan L. Comprehensively designed consensus of standalone secondary structure predictors improves Q3 by over 3%. J Biomol Struct Dyn 2014; 32(1): 36-51.
[41]
Yan J, Mizianty MJ, Filipow PL, Uversky VN, Kurgan L. RAPID: Fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale. Biochim Biophys Acta 2013; 1834(8): 1671-80.
[42]
Kumar R, Kumari B, Kumar M. Proteome-wide prediction and annotation of mitochondrial and sub-mitochondrial proteins by incorporating domain information. Mitochondrion 2018; 42: 11-22.
[43]
Hayat S, Elofsson A. BOCTOPUS: improved topology prediction of transmembrane β barrel proteins. Bioinformatics 2012; 28(4): 516-22.
[44]
Disfani FM, Hsu WL, Mizianty MJ, et al. MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins. Bioinformatics 2012; 28(12): i75-83.
[45]
Zhang T, Zhang H, Chen K, et al. Accurate sequence-based prediction of catalytic residues. Bioinformatics 2008; 24(20): 2329-38.
[46]
Kurgan L, Cios K, Chen K. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences. BMC Bioinformatics 2008; 9: 226-6.
[47]
Chen K, Mizianty MJ, Kurgan L. ATPsite: sequence-based prediction of ATP-binding residues. Proteome Sci 2011; 9(Suppl. 1): S4-4.
[48]
Cao R, Debswapna B, Jie H, Jianlin C. DeepQA: improving the estimation of single protein model quality with deep belief networks. BMC Bioinformatics 2016; 17(1): 495.
[49]
Gao J, Yang Y, Zhou Y. Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures. BMC Bioinformatics 2018; 19(1): 29.
[50]
Gao J, Yang Y, Zhou Y. Predicting the errors of predicted local backbone angles and non-local solvent- accessibilities of proteins by deep neural networks. Bioinformatics 2016; 32(24): 3768-73.
[51]
Fu L, Niu B, Zhu Z, Wu S, Li W, et al. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012; 28(23): 3150-2.
[52]
Huang Y, Niu B, Gao Y, Fu L, Li W, et al. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 2010; 26(5): 680-2.
[53]
Wang H, Feng L, Webb GI, et al. Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity. Brief Bioinform 2017; 18(6): 1092.
[54]
Yan J, Friedrich S, Kurgan L. A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues. Brief Bioinform 2016; 17(1): 88-105.
[55]
Zhao H, Yang Y, Zhou Y. Prediction of RNA binding proteins comes of age from low resolution to high resolution. Mol Biosyst 2013; 9(10): 2417-25.
[56]
Peng Z, Mizianty MJ, Kurgan L. Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins 2014; 82(1): 145-58.
[57]
Zheng C, Kurgan L. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments. BMC Bioinformatics 2008; 9: 430.
[58]
Jiang Q, Jin X, Lee SJ, Yao S, et al. Protein secondary structure prediction: A survey of the state of the art. J Mol Graph Model 2017; 76: 379-402.
[59]
Zhang H, Zhang T, Chen K, et al. Critical assessment of high-throughput standalone methods for secondary structure prediction. Brief Bioinform 2011; 12(6): 672-88.
[60]
Gao J, Zhang N, Ruan J. Prediction of protein modification sites of gamma-carboxylation using position specific scoring matrices based evolutionary information. Comput Biol Chem 2013; 47: 215-20.
[61]
Wang T, Zheng W, Wuyun Q, et al. PrAS: Prediction of amidation sites using multiple feature extraction. Comput Biol Chem 2017; 66: 57-62.
[62]
Mizianty MJ, Kurgan L. Improved identification of outer membrane beta barrel proteins using primary sequence, predicted secondary structure, and evolutionary information. Proteins 2011; 79(1): 294-303.
[63]
Tsaousis GN, Hamodrakas SJ, Bagos PG. Predicting Beta Barrel Transmembrane Proteins Using HMMs. Hidden Markov Models: Methods Mol Biol 2017; 1552: 43-61.
[64]
Miao Z, Westhof E. A Large-Scale Assessment of Nucleic Acids Binding Site Prediction Programs. PLOS Comput Biol 2015; 11(12): e1004639.
[65]
Zhang J, Ma Z, Kurgan L. Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains. Brief Bioinform 2017.
[66]
Ding XM, Pan XY, Xu C, Shen HB. Computational prediction of DNA-protein interactions: a review. Curr Comput Aided Drug Des 2010; 6(3): 197-206.
[67]
Walia RR, El-Manzalawy Y, Honavar VG, Dobbs D, et al. Sequence-based prediction of rna-binding residues in proteins. Prediction of Protein Secondary Structure. Methods Mol Biol 2017; 1484: 205-35.
[68]
Yan J, Kurgan L. DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues. Nucleic Acids Res 2017; 45(10): e84.
[69]
Zhang J, Kurgan L. Review and comparative assessment of sequence-based predictors of protein-binding residues. Brief Bioinform 2018; 19(5): 821-37.
[70]
Zhang ML, Zhou ZH. A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 2014; 26(8): 1819-37.
[71]
Cerri R, Barros RC, André CPLF, et al. Reduction strategies for hierarchical multi-label classification in protein function prediction. BMC Bioinformatics 2016; 17: 373.
[72]
Wan S, Mak M-W, Kung S-Y. Mem-ADSVM: A two-layer multi-label predictor for identifying multi-functional types of membrane proteins. J Theor Biol 2016; 398: 32-42.
[73]
Stojanova D, Ceci M, Malerba D, Dzeroski S. Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction. BMC Bioinformatics 2013; 14: 285.
[74]
Guo X, Fulin L, Ying J, Zhen W, Chunyu W, et al. Human protein subcellular localization with integrated source and multi-label ensemble classifier. Sci Rep 2016; 6: 28087.
[75]
Xu Y-Y, Yang F, Shen H-B. Incorporating organelle correlations into semi-supervised learning for protein subcellular localization prediction. Bioinformatics 2016; 32(14): 2184-92.
[76]
Wan S, Duan Y, Zou Q. HPSLPred: an ensemble multi-label classifier for human protein subcellular location prediction with Imbalanced Source. Proteomics 2017; 17(17-18)
54
4
