iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-steps Rule and Pseudo Components

Page: [306 - 320] Pages: 15

  • * (Excluding Mailing and Handling)

Abstract

Background: The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological processes. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites.

Methodology: In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are incorporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and independent testing.

Results: Accuracy determined through validation was 93.93% for jackknife test, 95.16% for crossvalidation, 94.3% for self-consistency and 94.3% for independent testing.

Conclusion: The proposed model has better performance as compared to the existing predictors, however, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.

Keywords: Sulfation, sulfotyrosine, statistical moments, PseAAC, 5-step rule, pseudo components.

Graphical Abstract

[1]
Whitford, D. Proteins: structure and function; John Wiley and Sons, 2013.
[2]
Lazure, C.; Seidah, N.G.; Pélaprat, D.; Chrétien, M. Proteases and posttranslational processing of prohormones: A review. Can. J. Biochem. Cell Biol., 1983, 61(7), 501-515.
[http://dx.doi.org/10.1139/o83-066] [PMID: 6354396]
[3]
Xu, Y.; Chou, K-C. Recent progress in predicting posttranslational modification sites in proteins. Curr. Top. Med. Chem., 2016, 16(6), 591-603.
[http://dx.doi.org/10.2174/1568026615666150819110421] [PMID: 26286211]
[4]
Farzan, M.; Babcock, G.J.; Vasilieva, N.; Wright, P.L.; Kiprilov, E.; Mirzabekov, T.; Choe, H. The role of post-translational modifications of the CXCR4 amino terminus in stromal-derived factor 1 α association and HIV-1 entry. J. Biol. Chem., 2002, 277(33), 29484-29489.
[http://dx.doi.org/10.1074/jbc.M203361200] [PMID: 12034737]
[5]
Huttner, W.B. Protein tyrosine sulfation. Trends Biochem. Sci., 1987, 12, 361-363.
[http://dx.doi.org/10.1016/0968-0004(87)90166-6]
[6]
Moore, K.L. The biology and enzymology of protein tyrosine O-sulfation. J. Biol. Chem., 2003, 278(27), 24243-24246.
[http://dx.doi.org/10.1074/jbc.R300008200] [PMID: 12730193]
[7]
Yu, Y.; Hoffhines, A.J.; Moore, K.L.; Leary, J.A. Determination of the sites of tyrosine O-sulfation in peptides and proteins. Nat. Methods, 2007, 4(7), 583-588.
[http://dx.doi.org/10.1038/nmeth1056] [PMID: 17558413]
[8]
Zhang, Y.; Jiang, H.; Go, E.P.; Desaire, H. Distinguishing phosphorylation and sulfation in carbohydrates and glycoproteins using ion-pairing and mass spectrometry. J. Am. Soc. Mass Spectrom., 2006, 17(9), 1282-1288.
[http://dx.doi.org/10.1016/j.jasms.2006.05.013] [PMID: 16820302]
[9]
Kehoe, J.W.; Bertozzi, C.R. Tyrosine sulfation: A modulator of extracellular protein-protein interactions. Chem. Biol., 2000, 7(3), R57-R61.
[http://dx.doi.org/10.1016/S1074-5521(00)00093-4] [PMID: 10712936]
[10]
Önnerfjord, P.; Heathfield, T.F.; Heinegård, D. Identification of tyrosine sulfation in extracellular leucine-rich repeat proteins using mass spectrometry. J. Biol. Chem., 2004, 279(1), 26-33.
[http://dx.doi.org/10.1074/jbc.M308689200] [PMID: 14551184]
[11]
Akbar, S.; Hayat, M. iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J. Theor. Biol., 2018, 455, 205-211.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.018] [PMID: 30031793]
[12]
Chen, W.; Ding, H.; Zhou, X.; Lin, H.; Chou, K-C. iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition. Anal. Biochem., 2018, 561-562, 59-65.
[http://dx.doi.org/10.1016/j.ab.2018.09.002] [PMID: 30201554]
[13]
Chen, W.; Feng, P.; Ding, H.; Lin, H.; Chou, K-C. iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal. Biochem., 2015, 490, 26-33.
[http://dx.doi.org/10.1016/j.ab.2015.08.021] [PMID: 26314792]
[14]
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K-C. iRNA-3typeA: Identifying three types of modification at RNA’s adenosine sites. Mol. Ther. Nucleic Acids, 2018, 11, 468-474.
[http://dx.doi.org/10.1016/j.omtn.2018.03.012] [PMID: 29858081]
[15]
Chen, W.; Tang, H.; Ye, J.; Lin, H.; Chou, K-C. iRNA-PseU: Identifying RNA pseudouridine sites. Mol. Ther. Nucleic Acids, 2016, 5e332
[PMID: 28427142]
[16]
Feng, P.; Ding, H.; Yang, H.; Chen, W.; Lin, H.; Chou, K-C. iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol. Ther. Nucleic Acids, 2017, 7, 155-163.
[http://dx.doi.org/10.1016/j.omtn.2017.03.006] [PMID: 28624191]
[17]
Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W.; Chou, K-C. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics, 2018, 111(1), 96-102.
[PMID: 29360500]
[18]
Ghauri, A.W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC. Curr. Pharm. Des., 2018, 24(34), 4034-4043.
[http://dx.doi.org/10.2174/1381612825666181127101039] [PMID: 30479209]
[19]
Jia, C.; Lin, X.; Wang, Z. Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou’s pseudo amino acid composition. Int. J. Mol. Sci., 2014, 15(6), 10410-10423.
[http://dx.doi.org/10.3390/ijms150610410] [PMID: 24918295]
[20]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal. Biochem., 2016, 497, 48-56.
[http://dx.doi.org/10.1016/j.ab.2015.12.009] [PMID: 26723495]
[21]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J. Theor. Biol., 2016, 394, 223-230.
[http://dx.doi.org/10.1016/j.jtbi.2016.01.020] [PMID: 26807806]
[22]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. iCar-PseCp: Identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget, 2016, 7(23), 34558-34570.
[http://dx.doi.org/10.18632/oncotarget.9148] [PMID: 27153555]
[23]
Jia, J.; Zhang, L.; Liu, Z.; Xiao, X.; Chou, K-C. pSumo-CD: Predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics, 2016, 32(20), 3133-3141.
[http://dx.doi.org/10.1093/bioinformatics/btw387] [PMID: 27354696]
[24]
Ju, Z.; Cao, J-Z.; Gu, H. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. J. Theor. Biol., 2016, 397, 145-150.
[http://dx.doi.org/10.1016/j.jtbi.2016.02.020] [PMID: 26908349]
[25]
Ju, Z.; He, J-J. Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou’s general PseAAC. J. Mol. Graph. Model., 2017, 77, 200-204.
[http://dx.doi.org/10.1016/j.jmgm.2017.08.020] [PMID: 28886434]
[26]
Ju, Z.; Wang, S-Y. Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition. Gene, 2018, 664, 78-83.
[http://dx.doi.org/10.1016/j.gene.2018.04.055] [PMID: 29694908]
[27]
Khan, Y.D.; Rasool, N.; Hussain, W.; Khan, S.A.; Chou, K-C. iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Anal. Biochem., 2018, 550, 109-116.
[http://dx.doi.org/10.1016/j.ab.2018.04.021] [PMID: 29704476]
[28]
Khan, Y.D.; Rasool, N.; Hussain, W.; Khan, S.A.; Chou, K-C. iPhosY-PseAAC: Identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol. Biol. Rep., 2018, 45(6), 2501-2509.
[http://dx.doi.org/10.1007/s11033-018-4417-z] [PMID: 30311130]
[29]
Liu, L-M.; Xu, Y.; Chou, K-C. iPGK-PseAAC: Identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general PseAAC. Med. Chem., 2017, 13(6), 552-559.
[http://dx.doi.org/10.2174/1573406413666170515120507] [PMID: 28521678]
[30]
Liu, Z.; Xiao, X.; Yu, D-J.; Jia, J.; Qiu, W-R.; Chou, K-C. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal. Biochem., 2016, 497, 60-67.
[http://dx.doi.org/10.1016/j.ab.2015.12.017] [PMID: 26748145]
[31]
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, D.; Chou, K.C. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Mol. Inform., 2017, 36(5-6)
[http://dx.doi.org/10.1002/minf.201600010] [PMID: 28488814]
[32]
Qiu, W-R.; Jiang, S-Y.; Sun, B-Q.; Xiao, X.; Cheng, X.; Chou, K-C. iRNA-2methyl: Identify RNA 2′-O-methylation sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier. Med. Chem., 2017, 13(8), 734-743.
[http://dx.doi.org/10.2174/1573406413666170623082245] [PMID: 28641529]
[33]
Qiu, W-R.; Jiang, S-Y.; Xu, Z-C.; Xiao, X.; Chou, K-C. iRNAm5C-PseDNC: Identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget, 2017, 8(25), 41178-41188.
[http://dx.doi.org/10.18632/oncotarget.17104] [PMID: 28476023]
[34]
Qiu, W-R.; Sun, B-Q.; Xiao, X.; Xu, Z-C.; Chou, K-C. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget, 2016, 7(28), 44310-44321.
[http://dx.doi.org/10.18632/oncotarget.10027] [PMID: 27322424]
[35]
Qiu, W-R.; Sun, B-Q.; Xiao, X.; Xu, Z-C.; Chou, K-C. iPTM-mLys: Identifying multiple lysine PTM sites and their different types. Bioinformatics, 2016, 32(20), 3116-3123.
[http://dx.doi.org/10.1093/bioinformatics/btw380] [PMID: 27334473]
[36]
Qiu, W.-R.; Xiao, X.; Lin, W.-Z.; Chou, K.-C. iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach BioMed Res. Int, 2014, 2014
[37]
Qiu, W-R.; Xiao, X.; Lin, W-Z.; Chou, K-C. iUbiq-Lys: Prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model. J. Biomol. Struct. Dyn., 2015, 33(8), 1731-1742.
[http://dx.doi.org/10.1080/07391102.2014.968875] [PMID: 25248923]
[38]
Qiu, W-R.; Xiao, X.; Xu, Z-C.; Chou, K-C. iPhos-PseEn: Identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget, 2016, 7(32), 51270-51283.
[http://dx.doi.org/10.18632/oncotarget.9987] [PMID: 27323404]
[39]
Sabooh, M.F.; Iqbal, N.; Khan, M.; Khan, M.; Maqbool, H.F. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC. J. Theor. Biol., 2018, 452, 1-9.
[http://dx.doi.org/10.1016/j.jtbi.2018.04.037] [PMID: 29727634]
[40]
Xie, H-L.; Fu, L.; Nie, X-D. Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou’s PseAAC. Protein Eng. Des. Sel., 2013, 26(11), 735-742.
[http://dx.doi.org/10.1093/protein/gzt042] [PMID: 24048266]
[41]
Xu, Y.; Ding, J.; Wu, L-Y.; Chou, K-C. iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS One, 2013, 8(2)e55844
[http://dx.doi.org/10.1371/journal.pone.0055844] [PMID: 23409062]
[42]
Xu, Y.; Shao, X-J.; Wu, L-Y.; Deng, N-Y.; Chou, K-C. iSNO-AAPair: Incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ, 2013, 1e171
[http://dx.doi.org/10.7717/peerj.171] [PMID: 24109555]
[43]
Xu, Y.; Wang, Z.; Li, C.; Chou, K-C. iPreny-PseAAC: Identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC. Med. Chem., 2017, 13(6), 544-551.
[http://dx.doi.org/10.2174/1573406413666170419150052] [PMID: 28425870]
[44]
Xu, Y.; Wen, X.; Shao, X-J.; Deng, N-Y.; Chou, K-C. iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int. J. Mol. Sci., 2014, 15(5), 7594-7610.
[http://dx.doi.org/10.3390/ijms15057594] [PMID: 24857907]
[45]
Xu, Y.; Wen, X.; Wen, L-S.; Wu, L-Y.; Deng, N-Y.; Chou, K-C. iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One, 2014, 9(8)e105018
[http://dx.doi.org/10.1371/journal.pone.0105018] [PMID: 25121969]
[46]
Zhang, J.; Zhao, X.; Sun, P.; Ma, Z. PSNO: Predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou’s PseAAC. Int. J. Mol. Sci., 2014, 15(7), 11204-11219.
[http://dx.doi.org/10.3390/ijms150711204] [PMID: 24968264]
[47]
Ehsan, A.; Mahmood, K.; Khan, Y.D.; Khan, S.A.; Chou, K-C. A novel modeling in mathematical biology for classification of signal peptides. Sci. Rep., 2018, 8(1), 1039.
[http://dx.doi.org/10.1038/s41598-018-19491-y] [PMID: 29348418]
[48]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal. Biochem., 2018, 568, 14-23.
[PMID: 30593778]
[49]
Khan, Y.D.; Jamil, M.; Hussain, W.; Rasool, N.; Khan, S.A.; Chou, K-C. pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J. Theor. Biol., 2018, 463, 47-55.
[PMID: 30550863]
[50]
Butt, A.H.; Khan, S.A.; Jamil, H.; Rasool, N.; Khan, Y.D. A prediction model for membrane proteins using moments based features. BioMed Res. Int., 2016, 2016, 1-7.
[http://dx.doi.org/10.1155/2016/8370132]
[51]
Butt, A.H.; Rasool, N.; Khan, Y.D. A treatise to computational approaches towards prediction of membrane protein and its subtypes. J. Membr. Biol., 2017, 250(1), 55-76.
[http://dx.doi.org/10.1007/s00232-016-9937-7] [PMID: 27866233]
[52]
Butt, A.H.; Rasool, N.; Khan, Y.D. Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC. Mol. Biol. Rep., 2018, 45(6), 2295-2306.
[http://dx.doi.org/10.1007/s11033-018-4391-5] [PMID: 30238411]
[53]
Awais, M.; Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.-C. iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and general pseudo amino acid composition. IEEE/ACM Trans. Comput. Biol. Bioinform, 2019.
[54]
Chandra, A.; Sharma, A.; Dehzangi, A.; Ranganathan, S.; Jokhan, A.; Chou, K-C.; Tsunoda, T. PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Sci. Rep., 2018, 8(1), 17923.
[http://dx.doi.org/10.1038/s41598-018-36203-8] [PMID: 30560923]
[55]
Chen, Z.; Liu, X.; Li, F.; Li, C.; Marquez-Lago, T.; Leier, A.; Akutsu, T.; Webb, G.I.; Xu, D.; Smith, A. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Brief. Bioinform., 2018.30285084
[http://dx.doi.org/10.1093/bib/bby089]
[56]
Chou, K-C. Impacts of bioinformatics to medicinal chemistry. Med. Chem., 2015, 11(3), 218-234.
[http://dx.doi.org/10.2174/1573406411666141229162834] [PMID: 25548930]
[57]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal. Biochem., 2019, 568, 14-23.
[http://dx.doi.org/10.1016/j.ab.2018.12.019] [PMID: 30593778]
[58]
Li, F.; Zhang, Y.; Purcell, A. W.; Webb, G. I.; Chou, K.-C.; Lithgow, T.; Li, C.; Song, J. Positive-unlabelled learning of glycosylation sites in the human proteome. 2019, 20(1), 112.
[http://dx.doi.org/10.1186/s12859-019-2700-1]
[59]
Qiu, W-R.; Sun, B-Q.; Xiao, X.; Xu, Z-C.; Jia, J-H.; Chou, K-C. iKCR-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics, 2017, 110(5), 239-246.
[PMID: 29107015]
[60]
Wang, L.; Zhang, R.; Mu, Y. Fu-SulfPred: Identification of protein s-sulfenylation sites by fusing forests via Chou’s general PseAAC. 2019, 461, 51-58.
[61]
Xie, H.-L.; Fu, L.; Nie, X.-D. J.; Design, P.E. Selection, using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC. 2013, 26(11), 735-742.
[62]
Zhang, Y.; Xie, R.; Wang, J.; Leier, A.; Marquez-Lago, T.T.; Akutsu, T.; Webb, G.I.; Chou, K-C.; Song, J. Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Brief. Bioinform., 2018, 5.
[http://dx.doi.org/10.1093/bib/bby079] [PMID: 30351377]
[63]
Yu, K.M.; Liu, J.; Moy, R.; Lin, H.C.; Nicholas, H.B., Jr; Rosenquist, G.L. Prediction of tyrosine sulfation in seven-transmembrane peptide receptors. Endocrine, 2002, 19(3), 333-338.
[http://dx.doi.org/10.1385/ENDO:19:3:333] [PMID: 12624435]
[64]
Monigatti, F.; Gasteiger, E.; Bairoch, A.; Jung, E. The Sulfinator: predicting tyrosine sulfation sites in protein sequences. Bioinformatics, 2002, 18(5), 769-770.
[http://dx.doi.org/10.1093/bioinformatics/18.5.769] [PMID: 12050077]
[65]
Chang, W.C.; Lee, T.Y.; Shien, D.M.; Hsu, J.B.K.; Horng, J.T.; Hsu, P.C.; Wang, T.Y.; Huang, H.D.; Pan, R.L. Incorporating support vector machine for identifying protein tyrosine sulfation sites. J. Comput. Chem., 2009, 30(15), 2526-2537.
[http://dx.doi.org/10.1002/jcc.21258] [PMID: 19373826]
[66]
Niu, S.; Huang, T.; Feng, K.; Cai, Y.; Li, Y. Prediction of tyrosine sulfation with mRMR feature selection and analysis. J. Proteome Res., 2010, 9(12), 6490-6497.
[http://dx.doi.org/10.1021/pr1007152] [PMID: 20973568]
[67]
Huang, S-Y.; Shi, S-P.; Qiu, J-D.; Sun, X-Y.; Suo, S-B.; Liang, R-P. PredSulSite: Prediction of protein tyrosine sulfation sites with multiple features and analysis. Anal. Biochem., 2012, 428(1), 16-23.
[http://dx.doi.org/10.1016/j.ab.2012.06.003] [PMID: 22691961]
[68]
Jia, C.; Zhang, Y.; Wang, Z. SulfoTyrP: A high accuracy predictor of protein sulfotyrosine sites. Match Commun. Math. Comput. Chem, 2014, 71, 227-240.
[69]
Chou, K-C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol., 2011, 273(1), 236-247.
[http://dx.doi.org/10.1016/j.jtbi.2010.12.024] [PMID: 21168420]
[70]
Chou, K-C. Using subsite coupling to predict signal peptides. Protein Eng., 2001, 14(2), 75-79.
[http://dx.doi.org/10.1093/protein/14.2.75] [PMID: 11297664]
[71]
Cheng, X.; Lin, W-Z.; Xiao, X.; Chou, K-C.; Hancock, J. pLoc_bal-mAnimal: Predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics, 2018, 1, 9.
[PMID: 30010789]
[72]
Cheng, X.; Xiao, X.; Chou, K-C. pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. J. Theor. Biol., 2018, 458, 92-102.
[http://dx.doi.org/10.1016/j.jtbi.2018.09.005] [PMID: 30201434]
[73]
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K-C. pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics, 2018, 111(4), 886-892.
[PMID: 29842950]
[74]
Chou, K-C.; Cheng, X.; Xiao, X. pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasibalancing training dataset Genomics, 2018, S0888-7543(18), 30276-3.
[http://dx.doi.org/10.1016/j.ygeno.2018.08.007] [PMID: 30179658]
[75]
Sankari, E.S.; Manimegalai, D. Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC. J. Theor. Biol., 2018, 455, 319-328.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.032] [PMID: 30056084]
[76]
Contreras-Torres, E. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC. J. Theor. Biol., 2018, 454, 139-145.
[http://dx.doi.org/10.1016/j.jtbi.2018.05.033] [PMID: 29870696]
[77]
Javed, F.; Hayat, M. Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou’s PseAAC Genomics, 2018, S0888-7543(18), 30519-6.
[http://dx.doi.org/10.1016/j.ygeno.2018.09.004] [PMID: 30196077]
[78]
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K-C. iRNA-AI: Identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget, 2017, 8(3), 4208-4217.
[http://dx.doi.org/10.18632/oncotarget.13758] [PMID: 27926534]
[79]
Chen, W.; Feng, P-M.; Deng, E-Z.; Lin, H.; Chou, K-C. iTIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal. Biochem., 2014, 462, 76-83.
[80]
Chen, W.; Feng, P-M.; Lin, H.; Chou, K-C. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res., 2013, 41(6)e68
[81]
Cheng, X.; Xiao, X.; Chou, K-C. pLoc_bal-mPlant: Predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. Curr. Pharm. Des., 2018, 24(34), 4013-4022.
[http://dx.doi.org/10.2174/1381612824666181119145030] [PMID: 30451108]
[82]
Chou, K.; Cheng, X.; Xiao, X. pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med. Chem., 2018, 15(5), 472-485.
[83]
Ding, H.; Deng, E.-Z.; Yuan, L.-F.; Liu, L.; Lin, H.; Chen, W.; Chou, K.-C. A sequence-based predictor for identifying the types of conotoxins in targeting ion channels. 2014, 2014, 1-10.
[84]
Feng, P-M.; Chen, W.; Lin, H.; Chou, K-C. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem., 2013, 442(1), 118-125.
[http://dx.doi.org/10.1016/j.ab.2013.05.024] [PMID: 23756733]
[85]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. SPrenylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins. J. Theor. Biol., 2019, 468, 1-11.
[http://dx.doi.org/10.1016/j.jtbi.2019.02.007] [PMID: 30768975]
[86]
Jia, J.; Li, X.; Qiu, W.; Xiao, X.; Chou, K-C. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. J. Theor. Biol., 2019, 460, 195-203.
[http://dx.doi.org/10.1016/j.jtbi.2018.10.021] [PMID: 30312687]
[87]
Khan, Y.D.; Batool, A.; Rasool, N.; Khan, S.A.; Chou, K-C. Prediction of nitrosocysteine sites using position and composition variant features. Lett. Org. Chem., 2019, 16(4), 283-293.
[88]
Li, J.-X.; Wang, S.-Q.; Du, Q.-S.; Wei, H.; Li, X.-M.; Meng, J.-Z.; Wang, Q.-Y.; Xie, N.-Z.; Huang, R.-B.; Chou, K.-C. Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from Bacillus deramificans. 2018, 24(34), 4023-4033.
[89]
Lin, H.; Deng, E-Z.; Ding, H.; Chen, W.; Chou, K-C. iPro54-PseKNC: A sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res., 2014, 42(21), 12961-12972.
[http://dx.doi.org/10.1093/nar/gku1019] [PMID: 25361964]
[90]
Liu, B.; Fang, L.; Long, R.; Lan, X.; Chou, K-C. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics, 2015, 32(3), 362-369.
[91]
Liu, B.; Fang, L.; Wang, S.; Wang, X.; Li, H.; Chou, K.-C. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. 2015, 385, 153-159.
[http://dx.doi.org/10.1016/j.jtbi.2015.08.025]]
[92]
Liu, Z.; Xiao, X.; Qiu, W-R.; Chou, K-C.J.A.b. iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Anal. Biochem., 2015, 474, 69-77.
[93]
Lu, Y.; Wang, S.; Wang, J.; Zhou, G.; Zhang, Q.; Zhou, X.; Niu, B.; Chen, Q.; Chou, K-C. An epidemic avian influenza prediction model based on google trends. Lett. Org. Chem., 2019, 16(4), 303-310.
[94]
Xiao, X.; Min, J.-L.; Lin, W.-Z.; Liu, Z.; Cheng, X.; Chou, K.-C. Dynamics, iDrug-Target: Predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. 2015, 33(10), 2221-2233.
[95]
Chou, K.C. Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr. Med. Chem., 2019.
[http://dx.doi.org/10.2174/0929867326666190507082559] [PMID: 31060481]
[96]
Zhang, C.T.; Chou, K.C. An optimization approach to predicting protein structural class from amino acid composition. Protein Sci., 1992, 1(3), 401-408.
[http://dx.doi.org/10.1002/pro.5560010312]
[97]
Chou, K.C.; Cai, Y.D. Prediction and classification of protein subcellular location-sequenceorder effect and pseudo amino acid composition. J. Cell. Biochem., 2003, 90(6), 1250-1260.
[98]
Chou, K-C.; Elrod, D.W. Bioinformatical analysis of G-protein-coupled receptors. J. Proteome Res., 2002, 1(5), 429-433.
[http://dx.doi.org/10.1021/pr025527k]
[99]
Hu, L.; Huang, T.; Shi, X.; Lu, W.-C.; Cai, Y.-D.; Chou, K.-C. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. 2011, 6(1)e14556
[http://dx.doi.org/10.1371/journal.pone.0014556]]
[100]
Cai, Y.-D.; Feng, K.-Y.; Lu, W.-C.; Chou, K.-C. Using LogitBoost classifier to predict protein structural classes. 2006, 238(1), 172-176.
[http://dx.doi.org/10.1016/j.jtbi.2005.05.034]
[101]
Chou, K-C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics, 2004, 21(1), 10-19.
[102]
Ahmad, J.; Hayat, M. MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. J. Theor. Biol., 2019, 463, 99-109.
[103]
Akbar, S.; Hayat, M. iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J. Theor. Biol., 2018, 455, 205-211.
[104]
Behbahani, M.; Mohabatkar, H.; Nosrati, M. Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou’s general pseudo amino acid composition. J. Theor. Biol., 2016, 411, 1-5.
[http://dx.doi.org/10.1016/j.jtbi.2016.09.001]
[105]
Contreras-Torres, E. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC. J. Theor. Biol., 2018, 454, 139-145.
[http://dx.doi.org/10.1016/j.jtbi.2018.05.033]
[106]
Dehzangi, A.; Heffernan, R.; Sharma, A.; Lyons, J.; Paliwal, K.; Sattar, A. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳ s general PseAAC. J. Theor. Biol., 2015, 364, 284-294.
[107]
Ju, Z.; He, J-J. Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou’s PseAAC. J. Mol. Graph. Model., 2017, 76, 356-363.
[108]
Kabir, M.; Hayat, M. iRSpot-GAEnsC: Identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol. Genet. Genomics, 2016, 291(1), 285-296.
[109]
Meher, P.K.; Sahu, T.K.; Saini, V.; Rao, A. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci. Rep., 2017, 7, 42362.
[http://dx.doi.org/10.1038/srep42362]
[110]
Tahir, M.; Hayat, M.; Khan, S. iNuc-ext-PseTNC: An efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition. Mol. Genet. Genomics, 2019, 294(1), 199-210.
[111]
Yu, B.; Li, S.; Qiu, W-Y.; Chen, C.; Chen, R-X.; Wang, L.; Wang, M-H.; Zhang, Y. Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising. Oncotarget, 2017, 8(64)107640
[112]
Zhang, S.; Liang, Y. Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC. J. Theor. Biol., 2018, 457, 163-169.
[http://dx.doi.org/10.1016/j.jtbi.2018.08.042]
[113]
Chou, K-C. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr. Top. Med. Chem., 2017, 457, 163-169.
[http://dx.doi.org/10.2174/1568026617666170414145508]
[114]
Shen, H-B.; Chou, K-C. PseAAC: A flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem., 2008, 373(2), 386-388.
[http://dx.doi.org/10.1016/j.ab.2007.10.012]
[115]
Du, P.; Wang, X.; Xu, C.; Gao, Y. PseAAC-Builder: A cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions. Anal. Biochem., 2012, 425(2), 117-119.
[http://dx.doi.org/10.1016/j.ab.2012.03.015]
[116]
Cao, D-S.; Xu, Q-S.; Liang, Y-Z.J.B. Propy: A tool to generate various modes of Chou’s PseAAC. Bioinformatics, 2013, 29(7), 960-962.
[117]
Du, P.; Gu, S.; Jiao, Y. PseAAC-General: Fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. Int. J. Mol. Sci., 2014, 15(3), 3495-3506.
[http://dx.doi.org/10.3390/ijms15033495]
[118]
Chou, K-C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteomics, 2009, 6(4), 262-274.
[http://dx.doi.org/10.2174/157016409789973707]
[119]
Chen, W.; Lei, T-Y.; Jin, D-C.; Lin, H.; Chou, K-C. PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition. Anal. Biochem., 2014, 456, 53-60.
[http://dx.doi.org/10.1016/j.ab.2014.04.001] [PMID: 24732113]
[120]
Chen, W.; Lin, H.; Chou, K-C. Pseudo nucleotide composition or PseKNC: An effective formulation for analyzing genomic sequences. Mol. Biosyst., 2015, 11(10), 2620-2634.
[http://dx.doi.org/10.1039/C5MB00155B]
[121]
Liu, B.; Yang, F.; Huang, D-S.; Chou, K-C. iPromoter-2L: A two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics, 2018, 34(1), 33-40.
[http://dx.doi.org/10.1093/bioinformatics/btx579] [PMID: 28968797]
[122]
Tahir, M.; Tayara, H.; Chong, K. iRNA-PseKNC (2methyl): Identify RNA 2′-O-methylation sites by convolution neural network and Chou’s pseudo components. J. Theor. Biol., 2019, 465, 1-6.
[123]
Liu, B.; Liu, F.; Wang, X.; Chen, J.; Fang, L.; Chou, K-C. Pse-in-One: A web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res., 2015, 43(W1), W65-W71.
[124]
Liu, B.; Wu, H.; Chou, K-C.J.N.S. Pse-in-One 2.0: An improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res., 2017, 9(04), 67.
[125]
Akmal, M.A.; Rasool, N.; Khan, Y.D. Prediction of N-linked glycosylation sites using position relative features and statistical moments. PLoS One, 2017, 12(8)e0181966
[http://dx.doi.org/10.1371/journal.pone.0181966] [PMID: 28797096]
[126]
Khan, Y.D.; Ahmad, F.; Anwar, M.W. A neuro-cognitive approach for iris recognition using back propagation. World Appl. Sci. J., 2012, 16(5), 678-685.
[127]
Khan, Y.D.; Ahmed, F.; Khan, S.A. Situation recognition using image moments and recurrent neural networks. Neural Comput. Appl., 2014, 24(7-8), 1519-1529.
[http://dx.doi.org/10.1007/s00521-013-1372-4]
[128]
Khan, Y.D.; Khan, N.S.; Farooq, S.; Abid, A.; Khan, S.A.; Ahmad, F.; Mahmood, M.K. An efficient algorithm for recognition of human actions. The Sci. World J., 2014, 2014, 1-11.
[http://dx.doi.org/10.1155/2014/875879]
[129]
Khan, Y.D.; Khan, S.A.; Ahmad, F.; Islam, S. Iris recognition using image moments and k-means algorithm. The Sci. World J., 2014, 2014, 1-9.
[http://dx.doi.org/10.1155/2014/723595]
[130]
Chou, K-C. Prediction of signal peptides using scaled window. Peptides, 2001, 22(12), 1973-1979.
[http://dx.doi.org/10.1016/S0196-9781(01)00540-X]
[131]
Chou, K.C. Bioinformatics, Prediction of protein signal sequences and their cleavage sites. Proteins, 2001, 42(1), 136-139.
[132]
Chou, K-C. Prediction of signal peptides using scaled window. Peptides, 2001, 22(12), 1973-1979.
[133]
Feng, P-M.; Ding, H.; Chen, W.; Lin, H. Naive Bayes classifier with feature selection to identify phage virion proteins. Comput. Math. Methods Med., 2013, 2013530696
[http://dx.doi.org/10.1155/2013/530696]
[134]
Xu, Y.; Shao, X.J.; Wu, L.Y.; Deng, N.Y.; Chou, K.C. iSNO-AAPair: Incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ, 2013, 1e171
[http://dx.doi.org/10.7717/peerj.171] [PMID: 24109555]
[135]
Chen, W.; Feng, P.; Ding, H.; Lin, H.; Chou, K-C. Using deformation energy to analyze nucleosome positioning in genomes. Genomics, 2016, 107(2-3), 69-75.
[http://dx.doi.org/10.1016/j.ygeno.2015.12.005] [PMID: 26724497]
[136]
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, D.; Chou, K.C. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Mol. Inform., 2017, 36(5-6)1600010
[http://dx.doi.org/10.1002/minf.201600010] [PMID: 28488814]
[137]
Xiao, X.; Ye, H-X.; Liu, Z.; Jia, J-H.; Chou, K-C. iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget, 2016, 7(23), 34180-34189.
[http://dx.doi.org/10.18632/oncotarget.9057] [PMID: 27147572]
[138]
Lin, H.; Deng, E.Z.; Ding, H.; Chen, W.; Chou, K.C. iPro54-PseKNC: A sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res., 2014, 42(21), 12961-12972.
[http://dx.doi.org/10.1093/nar/gku1019] [PMID: 25361964]
[139]
Xu, Y.; Wen, X.; Wen, L.S.; Wu, L.Y.; Deng, N.Y.; Chou, K.C. iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One, 2014, 9(8)e105018
[http://dx.doi.org/10.1371/journal.pone.0105018] [PMID: 25121969]
[140]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J. Theor. Biol., 2016, 394, 223-230.
[http://dx.doi.org/10.1016/j.jtbi.2016.01.020] [PMID: 26807806]
[141]
Zhang, C.J.; Tang, H.; Li, W.C.; Lin, H.; Chen, W.; Chou, K.C. iOri-Human: Identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget, 2016, 7(43), 69783-69793.
[http://dx.doi.org/10.18632/oncotarget.11975] [PMID: 27626500]
[142]
Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K.C. iACP: A sequence-based tool for identifying anticancer peptides. Oncotarget, 2016, 7(13), 16895-16909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID: 26942877]
[143]
Liu, B.; Yang, F.; Chou, K.C. 2L-piRNA: A two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Mol. Ther. Nucleic Acids, 2017, 7, 267-277.
[http://dx.doi.org/10.1016/j.omtn.2017.04.008] [PMID: 28624202]
[144]
Liu, B.; Wang, S.; Long, R.; Chou, K.C. iRSpot-EL: Identify recombination spots with an ensemble learning approach. Bioinformatics, 2017, 33(1), 35-41.
[http://dx.doi.org/10.1093/bioinformatics/btw539] [PMID: 27531102]
[145]
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-AI: Identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget, 2017, 8(3), 4208-4217.
[http://dx.doi.org/10.18632/oncotarget.13758] [PMID: 27926534]
[146]
Feng, P.; Ding, H.; Yang, H.; Chen, W.; Lin, H.; Chou, K.C. iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol. Ther. Nucleic Acids, 2017, 7, 155-163.
[http://dx.doi.org/10.1016/j.omtn.2017.03.006] [PMID: 28624191]
[147]
Liu, B.; Yang, F.; Huang, D.S.; Chou, K.C. iPromoter-2L: A two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics, 2018, 34(1), 33-40.
[http://dx.doi.org/10.1093/bioinformatics/btx579] [PMID: 28968797]
[148]
Ehsan, A.; Mahmood, K.; Khan, Y.D.; Khan, S.A.; Chou, K.C. A novel modeling in mathematical biology for classification of signal peptides. Sci. Rep., 2018, 8(1), 1039.
[http://dx.doi.org/10.1038/s41598-018-19491-y] [PMID: 29348418]
[149]
Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W.; Chou, K.C. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics, 2018, 111(1), 96-102.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005] [PMID: 29360500]
[150]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J. Theor. Biol., 2015, 377, 47-56.
[151]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C.J.M. iPPBS-Opt: a sequence-based ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets. Molecules, 2016, 21(1), 95.
[152]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. Dynamics, Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition. J. Biomol. Struct. Dyn., 2016, 34(9), 1946-1961.
[153]
Liu, B.; Wang, S.; Long, R.; Chou, K-C. iRSpot-EL: Identify recombination spots with an ensemble learning approach. Bioinformatics, 2017, 33(1), 35-41.
[http://dx.doi.org/10.1093/bioinformatics/btw539] [PMID: 27531102]
[154]
Qiu, W-R.; Xiao, X.; Chou, K-C. iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int. J. Mol. Sci., 2014, 15(2), 1746-1766.
[155]
Song, J.; Wang, Y.; Li, F.; Akutsu, T.; Rawlings, N.D.; Webb, G.I.; Chou, K-C. iProt-Sub: A comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief. Bioinform., 2018, 20(2), 638-658.
[PMID: 29897410]
[156]
Xiao, X.; Ye, H-X.; Liu, Z.; Jia, J-H.; Chou, K-C.J.O. iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget, 2016, 7(23), 34180.
[157]
Yang, H.; Qiu, W-R.; Liu, G.; Guo, F-B.; Chen, W.; Chou, K-C.; Lin, H. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int. J. Biol. Sci., 2018, 14(8), 883.
[158]
Liu, B.; Yang, F.; Chou, K-C. 2L-piRNA: A two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Mol. Ther. Nucleic Acids, 2017, 7, 267-277.
[http://dx.doi.org/10.1016/j.omtn.2017.04.008] [PMID: 28624202]
[159]
Chou, K-C.; Wu, Z-C.; Xiao, X. iLoc-Hum: Using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol. Biosyst., 2012, 8(2), 629-641.
[http://dx.doi.org/10.1039/C1MB05420A] [PMID: 22134333]
[160]
Lin, W-Z.; Fang, J-A.; Xiao, X.; Chou, K-C. iLoc-animal: A multi-label learning classifier for predicting subcellular localization of animal proteins. Mol. Biosyst., 2013, 9(4), 634-644.
[http://dx.doi.org/10.1039/c3mb25466f] [PMID: 23370050]
[161]
Xiao, X.; Wu, Z-C.; Chou, K-C. iLoc-virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J. Theor. Biol., 2011, 284(1), 42-51.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.005] [PMID: 21684290]
[162]
Xiao, X.; Wang, P.; Lin, W-Z.; Jia, J-H.; Chou, K-C. iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal. Biochem., 2013, 436(2), 168-177.
[http://dx.doi.org/10.1016/j.ab.2013.01.019] [PMID: 23395824]
[163]
Chou, K-C. Some remarks on predicting multi-label attributes in molecular biosystems. Mol. Biosyst., 2013, 9(6), 1092-1100.
[http://dx.doi.org/10.1039/c3mb25555g] [PMID: 23536215]
[164]
Cheng, X.; Xiao, X.; Chou, K-C. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics, 2017, 110(1), 50-58.
[PMID: 28818512]
[165]
Cheng, X.; Xiao, X.; Chou, K-C. pLoc-mPlant: Predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Mol. Biosyst., 2017, 13(9), 1722-1727.
[http://dx.doi.org/10.1039/C7MB00267J] [PMID: 28702580]
[166]
Cheng, X.; Xiao, X.; Chou, K-C. pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene, 2017, 628, 315-321.
[http://dx.doi.org/10.1016/j.gene.2017.07.036] [PMID: 28728979]
[167]
Cheng, X.; Xiao, X.; Chou, K-C. pLoc-mHum: Predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics, 2018, 34(9), 1448-1456.
[http://dx.doi.org/10.1093/bioinformatics/btx711] [PMID: 29106451]
[168]
Cheng, X.; Xiao, X.; Chou, K-C. pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics, 2017, 110(4), 231-239.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.002] [PMID: 28989035]
[169]
Cheng, X.; Zhao, S-G.; Lin, W-Z.; Xiao, X.; Chou, K-C. pLoc-mAnimal: Predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics, 2017, 33(22), 3524-3531.
[http://dx.doi.org/10.1093/bioinformatics/btx476] [PMID: 29036535]
[170]
Xiao, X.; Cheng, X.; Su, S.; Mao, Q.; Chou, K-C. pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat. Sci., 2017, 9(09), 330.
[http://dx.doi.org/10.4236/ns.2017.99032]
[171]
Cheng, X.; Zhao, S-G.; Xiao, X.; Chou, K-C. iATC-mISF: A multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics, 2017, 33(3), 341-346.
[http://dx.doi.org/10.1093/bioinformatics/btx387] [PMID: 28172617]
[172]
Cheng, X.; Zhao, S-G.; Xiao, X.; Chou, K-C. iATC-mHyb: A hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget, 2017, 8(5), 58494-346.
[173]
Chou, K-C. Some remarks on predicting multi-label attributes in molecular biosystems. Mol. Biosyst., 2013, 9(6), 1092-1100.
[http://dx.doi.org/10.1039/c3mb25555g]
[174]
Chou, K-C.; Zhang, C-T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30(4), 275-349.
[http://dx.doi.org/10.3109/10409239509083488] [PMID: 7587280]
[175]
Dehzangi, A.; Heffernan, R.; Sharma, A.; Lyons, J.; Paliwal, K.; Sattar, A. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC. J. Theor. Biol., 2015, 364, 284-294.
[http://dx.doi.org/10.1016/j.jtbi.2014.09.029] [PMID: 25264267]
[176]
Dou, Y.; Yao, B.; Zhang, C.; Phospho, S.V.M. PhosphoSVM: Prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine. Amino Acids, 2014, 46(6), 1459-1469.
[http://dx.doi.org/10.1007/s00726-014-1711-5] [PMID: 24623121]
[177]
Feng, K-Y.; Cai, Y-D.; Chou, K-C. Boosting classifier for predicting protein domain structural class. Biochem. Biophys. Res. Commun., 2005, 334(1), 213-217.
[http://dx.doi.org/10.1016/j.bbrc.2005.06.075] [PMID: 15993842]
[178]
Kumar, R.; Srivastava, A.; Kumari, B.; Kumar, M. Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine. J. Theor. Biol., 2015, 365, 96-103.
[http://dx.doi.org/10.1016/j.jtbi.2014.10.008] [PMID: 25454009]
[179]
Mondal, S.; Pai, P.P. Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction. J. Theor. Biol., 2014, 356, 30-35.
[http://dx.doi.org/10.1016/j.jtbi.2014.04.006] [PMID: 24732262]
[180]
Nanni, L.; Brahnam, S.; Lumini, A. Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition. J. Theor. Biol., 2014, 360, 109-116.
[http://dx.doi.org/10.1016/j.jtbi.2014.07.003] [PMID: 25026218]
[181]
Qiu, W-R.; Xiao, X.; Chou, K-C. iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components. Int. J. Mol. Sci., 2014, 15(2), 1746-1766.
[http://dx.doi.org/10.3390/ijms15021746] [PMID: 24469313]
[182]
Shen, H-B.; Yang, J.; Chou, K-C. Euk-PLoc: An ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids, 2007, 33(1), 57-67.
[http://dx.doi.org/10.1007/s00726-006-0478-8] [PMID: 17235453]
[183]
Wu, Z-C.; Xiao, X.; Chou, K-C. iLoc-Plant: A multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol. Biosyst., 2011, 7(12), 3287-3297.
[http://dx.doi.org/10.1039/c1mb05232b] [PMID: 21984117]
[184]
Zhou, G.P.; Doctor, K. Subcellular location prediction of apoptosis proteins. Proteins, 2003, 50(1), 44-48.
[http://dx.doi.org/10.1002/prot.10251] [PMID: 12471598]
[185]
Althaus, I.W.; Chou, J.; Gonzales, A.; Deibel, M.; Chou, K.; Kezdy, F.; Romero, D.; Aristoff, P.; Tarpley, W.; Reusser, F. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J. Biol. Chem., 1993, 268(9), 6119-6124.
[186]
Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Deibel, M.R.; Kuo-Chen, C.; Kezdy, F.J.; Romero, D.L.; Thomas, R.C.; Aristoff, P.A.; Tarpley, W. Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152E. Biochem. Pharmacol., 1994, 47(11), 2017-2028.
[http://dx.doi.org/10.1016/0006-2952(94)90077-9]
[187]
Althaus, I.W.; Gonzales, A.; Chou, J.; Romero, D.; Deibel, M.; Chou, K-C.; Kezdy, F.; Resnick, L.; Busso, M.; So, A. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J. Biol. Chem., 1993, 268(20), 14875-14880.
[188]
Chou, K.; Forsen, S.; Zhou, G. Schematic rules for deriving apparent rate constants. Can. J. Chem., 1980, 16(4), 109-113.
[189]
Chou, K-C.; Forsén, S. Graphical rules for enzyme-catalysed rate laws. Biochem. J., 1980, 187(3), 829-835.
[http://dx.doi.org/10.1042/bj1870829]
[190]
Chou, K-C.; Lin, W-Z.; Xiao, X. Wenxiang: A web-server for drawing wenxiang diagrams. Nat. Sci., 2011, 3(10), 862.
[http://dx.doi.org/10.4236/ns.2011.310111]
[191]
Chou, K-C.J.J.o.B.C. Graphic rules in steady and non-steady state enzyme kinetics. J. Biol. Chem., 1989, 264(20), 12074-12079.
[192]
Chou, K-C. Applications of graph theory to enzyme kinetics and protein folding kinetics: Steady and non-steady-state systems. Biophys. Chem., 1990, 35(1), 1-24.
[193]
Chou, K-C. Graphic rule for drug metabolism systems. Curr. Drug Metab., 2010, 11(4), 369-378.
[http://dx.doi.org/10.2174/138920010791514261]
[194]
Chou, K. Graph theory of enzyme kinetics. Sci. Sin., 1979, 22, 341-358.
[195]
Chen, K-C.; Carter, R.E.; Forsen, S. A new graphical-method for deriving rate-equations for complicated mechanisms. Chem. Scr., 1981, 18(2), 82-86.
[196]
Kuo-Chen, C.; Forsen, S. Graphical rules of steady-state reaction systems. Can. J. Chem., 1981, 59(4), 737-755.
[http://dx.doi.org/10.1139/v81-107]
[197]
Zhou, G.; Deng, M.J.B.J. An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem. J., 1984, 222(1), 169-176.
[http://dx.doi.org/10.1042/bj2220169]
[198]
Zhou, G-P. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J. Theor. Biol., 2011, 284(1), 142-148.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.006]
[199]
Chou, K-c.; Forsén, S. Diffusion-controlled effects in reversible enzymatic fast reaction systems-critical spherical shell and proximity rate constant. Biophys. Chem., 1980, 12(3-4), 255-263.
[http://dx.doi.org/10.1016/0301-4622(80)80002-0]
[200]
Chou, K-c.; Li, T-t.; Forsén, S. The critical spherical shell in enzymatic fast reaction systems. Biophys. Chem., 1980, 12(3-4), 265-269.
[http://dx.doi.org/10.1016/0301-4622(80)80003-2]
[201]
Shen, H-B.; Song, J-N.; Chou, K-C. Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J. Biomed. Sci. Eng., 2009, 2, 136-143.
[202]
Chou, K.; Chen, N.; Forsen, S. The biological functions of low-frequency phonons. 2. Cooperative effects. Biophys. Chem., 1981, 18(3), 126-132.
[203]
Chou, K-C.; Shen, H-B. Recent advances in developing web-servers for predicting protein attributes. Nat. Sci., 2009, 1(02), 63.
[http://dx.doi.org/10.4236/ns.2009.12011]
[204]
Chou, K-C. Low-frequency collective motion in biomacromolecules and its biological functions. Biophys. Chem., 1988, 30(1), 3-48.
[http://dx.doi.org/10.1016/0301-4622(88)85002-6]
[205]
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K. pLoc_bal-mVirus: Predict subcellular localization of multi-label virus proteins by PseAAC and IHTS treatment to balance training dataset. Med. Chem., 2018, 15(5), 496-509.
[206]
Chou, K-C. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr. Top. Med. Chem., 2017, 17(21), 2337-2358.
[http://dx.doi.org/10.2174/1568026617666170414145508] [PMID: 28413951]