ICar-PseCp: Identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC

Oncotarget

Volumn 7, Issue 23, 2016, Pages 34558-34570

  Jia, Jianhua ^a,b   Liu, Zi ^c   Xiao, Xuan ^a,b   Liu, Bingxiang ^a   Chou, Kuo Chen ^b,d  

^a JINGDEZHEN CERAMIC INSTITUTE   (China)

^b GORDON LIFE SCIENCE INSTITUTE   (United States)

^c NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^d KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Carbonylation; Monte Carlo sampling; PseAAC; Random forest algorithm; Sequence coupling model

Indexed keywords

ALGORITHM; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; BIOINFORMATICS; COMPUTER ANALYSIS; COMPUTER INTERFACE; COMPUTER PREDICTION; CONTROLLED STUDY; HUMAN COMPUTER INTERACTION; INTERNET; MATHEMATICAL MODEL; MONTE CARLO METHOD; ONLINE ANALYTICAL PROCESSING; PLOTS AND CURVES; PROCESS DEVELOPMENT; PROTEIN CARBONYLATION; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SEQUENCE COUPLING MODEL; VALIDATION STUDY; WEB BROWSER; ESCHERICHIA COLI; GENETICS; HUMAN; METABOLISM; PHOTOBACTERIUM; PHYSIOLOGY; PROTEIN PROCESSING; SOFTWARE;

BACTERIAL PROTEIN;

ALGORITHMS; AMINO ACID SEQUENCE; BACTERIAL PROTEINS; ESCHERICHIA COLI; HUMANS; MONTE CARLO METHOD; PHOTOBACTERIUM; PROTEIN CARBONYLATION; PROTEIN PROCESSING, POST-TRANSLATIONAL; SOFTWARE;

EID: 84973637727     PISSN: None     EISSN: 19492553     Source Type: Journal    
DOI: 10.18632/oncotarget.9148     Document Type: Article

References (127)

1. Foster MW, Hess DT, Stamler JS. Protein S-nitrosylation in health and disease: a current perspective. Trends Mol Med. 2009; 15:391-404.
2. Uehara T, Nakamura T, Yao D, Shi ZQ, Gu Z, Ma Y, Masliah E, Nomura Y, Lipton SA. S-nitrosylated protein-disulphide isomerase links protein misfolding to neurodegeneration. Nature. 2006; 441:513-517.
3. Kobayashi Y, Absher DM, Gulzar ZG, Young SR, McKenney JK, Peehl DM, Brooks JD, Myers RM, Sherlock G. DNA methylation profiling reveals novel biomarkers and important roles for DNA methyltransferases in prostate cancer. Genome Research. 2011; 21:1017-1027.
4. Cantara WA, Crain PF, Rozenski J, McCloskey JA, Harris KA, Zhang X, Vendeix FA, Fabris D, Agris PF. The RNA Modification Database, RNAMDB: 2011 update. Nucleic Acids Res. 2011; 39:D195-201.
5. Xu Y, Ding J, Wu LY. 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:e55844.
6. Xu Y, Shao XJ, Wu LY, Deng NY. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ. 2013; 1:e171.
7. Qiu WR, Xiao X, Lin WZ. iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach. Biomed Res Int (BMRI). 2014; 2014:947416.
8. Xu Y, Wen X, Shao XJ. iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int J Mol Sci. 2014; 15:7594-7610.
9. Xu Y, Wen X, Wen LS, Wu LY. iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS ONE. 2014; 9:e105018.
10. Qiu WR, Xiao X, Lin WZ. iUbiq-Lys: Prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a grey system model Journal of Biomolecular Structure and Dynamics (JBSD) 2015; 33:1731-1742.
11. Jia J, Liu Z, Xiao X, Liu B. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequencecoupling effects into pseudo components and optimizing imbalanced training dataset Anal Biochem. 2016; 497:48-56.
12. Jia J, Liu Z, Xiao X, Liu B. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach J Theor Biol. 2016; 394:223-230.
13. Xu Y. Recent progress in predicting posttranslational modification sites in proteins. Curr Top Med Chem. 2016; 16:591-603.
14. Liu Z, Xiao X, Qiu WR. iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Analytical Biochemistry (also, Data in Brief, 2015; 4: 87-89). 2015; 474:69-77.
15. Chou KC. Impacts of bioinformatics to medicinal chemistry. Medicinal Chemistry. 2015; 11:218-234.
16. Chen W, Feng P, Ding H. iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition Analytical Biochemistry (also, Data in Brief, 2015, 5: 376-378). 2015; 490:26-33.
17. Liu Z, Xiao X, Yu DJ, Jia J. pRNAm-PC: Predicting N-methyladenosine sites in RNA sequences via physicalchemical properties. Anal Biochem. 2016; 497:60-67.
18. Shacter E. Quantification and significance of protein oxidation in biological samples 1*. Drug metabolism reviews. 2000; 32:307-326.
19. Gianazza E, Crawford J, Miller I. Detecting oxidative posttranslational modifications in proteins. Amino Acids. 2007; 33:51-56.
20. Dalle-Donne I, Giustarini D, Colombo R, Rossi R, Milzani A. Protein carbonylation in human diseases. Trends in Molecular Medicine. 2003; 9:169-176.
21. Moller IM, Rogowska-Wrzesinska A, Rao RSP. Protein carbonylation and metal-catalyzed protein oxidation in a cellular perspective. Journal of proteomics. 2011; 74: 2228-2242.
22. Bota DA, Van Remmen H, Davies KJA. Modulation of Lon protease activity and aconitase turnover during aging and oxidative stress. FEBS letters. 2002; 532:103-106.
23. Frohnert BI, Sinaiko AR, Serrot FJ, Foncea RE, Moran A, Ikramuddin S, Choudry U, Bernlohr DA. Increased adipose protein carbonylation in human obesity. Obesity. 2011; 19:1735-1741.
24. Dalle-Donne I, Rossi R, Giustarini D, Milzani A, Colombo R. Protein carbonyl groups as biomarkers of oxidative stress. Clinica Chimica Acta. 2003; 329:23-38.
25. Dalle-Donne I, Aldini G, Carini M, Colombo R, Rossi R, Milzani A. Protein carbonylation, cellular dysfunction, and disease progression. J Cell Mol Med. 2006; 10:389-406.
26. Bollineni RC, Hoffmann R, Fedorova M. Proteome-wide profiling of carbonylated proteins and carbonylation sites in HeLa cells under mild oxidative stress conditions. Free Radical Biology and Medicine. 2014; 68:186-195.
27. Colzani M, Aldini G, Carini M. Mass spectrometric approaches for the identification and quantification of reactive carbonyl species protein adducts. Journal of proteomics. 2013; 92:28-50.
28. Stadtman ER, Levine RL. Free radical-mediated oxidation of free amino acids and amino acid residues in proteins. Amino Acids. 2003; 25:207-218.
29. Maisonneuve E, Ducret A, Khoueiry P, Lignon S, Longhi S, Talla E, Dukan S. Rules governing selective protein carbonylation. PLoS One. 2009; 4:e7269.
30. Rao R, Moller IM. Pattern of occurrence and occupancy of carbonylation sites in proteins. Proteomics. 11:4166-4173.
31. Bollineni RC, Hoffmann R, Fedorova M. Identification of protein carbonylation sites by two-dimensional liquid chromatography in combination with MALDI-and ESI-MS. Journal of proteomics. 2011; 74:2338-2350.
32. Lv H, Han J, Liu J, Zheng J, Liu R, Zhong D. CarSPred: A Computational Tool for Predicting Carbonylation Sites of Human Proteins. 2014.
33. Xu Y, Wang X, Wang Y, Tian Y, Shao X, Wu LY, Deng N. Prediction of posttranslational modification sites from amino acid sequences with kernel methods. Journal of theoretical biology. 2014; 344:78-87.
34. Chen W, Lin H, Feng PM, Ding C. iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties. PLoS ONE. 2012; 7: e47843.
35. Chen W, Feng PM, Lin H, Chou KC. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition Nucleic Acids Res. 2013; 41:e68.
36. Lin H, Deng EZ, Ding H, Chen W, Chou KC. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res. 2014; 42:12961-12972.
37. Guo SH, Deng EZ, Xu LQ, Ding H. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics. 2014; 30:1522-1529.
38. Liu B, Fang L, Wang S, Wang X. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy Journal of Theoretical Biology. 2015; 385: 153-159.
39. Liu B, Fang L, Long R. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition Bioinformatics. 2016; 32:362-389.
40. Chen W, Ding H, Feng P, Lin H, Chou KC. iACP: a sequencebased tool for identifying anticancer peptides. Oncotarget. 2016; 7:16895-16909. doi: 10.18632/oncotarget.7815.
41. Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J Theor Biol. 2011; 273:236-247.
42. Forsen S. Graphical rules for enzyme-catalyzed rate laws. Biochem J. 1980; 187:829-835.
43. Zhou GP, Deng MH. An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J. 1984; 222:169-176.
44. Chou KC. Graphic rules in steady and non-steady enzyme kinetics. J Biol Chem. 1989; 264:12074-12079.
45. Althaus IW, Chou JJ, Gonzales AJ, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F. Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry. 1993; 32:6548-6554.
46. Althaus IW, Gonzales AJ, Chou JJ, Diebel MR, Romero DL, Aristoff PA, Tarpley WG, Reusser F. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem. 1993; 268:14875-14880.
47. Wu ZC, Xiao X. 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids. J Theor Biol. 2010; 267:29-34.
48. Lin WZ, Xiao X. Wenxiang: a web-server for drawing wenxiang diagrams Natural Science. 2011; 3:862-865
49. Zhou GP. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J Theor Biol. 2011; 284:142-148.
50. Zhou GP. The Structural Determinations of the Leucine Zipper Coiled-Coil Domains of the cGMP-Dependent Protein Kinase I alpha and its Interaction with the Myosin Binding Subunit of the Myosin Light Chains Phosphase. Proteins & Peptide Letters. 2011; 18:966-978.
51. Zhou GP, Huang RB. The pH-Triggered Conversion of the PrP(c) to PrP(sc.). Curr Top Med Chem. 2013; 13:1152-1163.
52. Fawcett JA. An Introduction to ROC Analysis. Pattern Recognition Letters. 2005; 27:861-874.
53. Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning: ACM), 2006. pp. 233-240.
54. Chou KC. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J Biol Chem. 1993; 268:16938-16948.
55. Zhang CT. An alternate-subsite-coupled model for predicting HIV protease cleavage sites in proteins. Protein Eng. 1993; 7:65-73.
56. Tomasselli AL, Reardon IM, Heinrikson RL. Predicting HIV protease cleavage sites in proteins by a discriminant function method. Proteins: Struct, Funct, Genet. 1996; 24:51-72.
57. Chou KC. A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase. Protein Science. 1995; 4:1365-1383.
58. Chou KC. Review: Prediction of protein signal sequences. Current Protein and Peptide Science. 2002; 3:615-622.
59. Chou KC, Shen HB. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Comm (BBRC). 2007; 357: 633-640.
60. Chou KC. Review: Prediction of tight turns and their types in proteins. Anal Biochem. 2000; 286:1-16.
61. Ishii T, Ito S, Kumazawa S, Sakurai T, Yamaguchi S, Mori T, Nakayama T, Uchida K. Site-specific modification of positively-charged surfaces on human serum albumin by malondialdehyde. Biochemical and biophysical research communications. 2008; 371:28-32.
62. Lee S, Young NL, Whetstone PA, Cheal SM, Benner WH, Lebrilla CB, Meares CF. Method to site-specifically identify and quantitate carbonyl end products of protein oxidation using oxidation-dependent element coded affinity tags (O-ECAT) and nanoliquid chromatography Fourier transform mass spectrometry. Journal of proteome research. 2006; 5:539-547.
63. Madian AG, Diaz-Maldonado N, Gao Q, Regnier FE. Oxidative stress induced carbonylation in human plasma. Journal of proteomics. 2011; 74:2395-2416.
64. Madian AG, Regnier FE. Profiling carbonylated proteins in human plasma. Journal of proteome research. 2010; 9:1330-1343.
65. Mirzaei H, Regnier F. Identification and quantification of protein carbonylation using light and heavy isotope labeled Girard's P reagent. Journal of Chromatography A. 2006; 1134:122-133.
66. Mirzaei H, Regnier F. Enrichment of carbonylated peptides using Girard P reagent and strong cation exchange chromatography. Analytical chemistry. 2006; 78: 770-778.
67. Temple A, Yen TY, Gronert S. Identification of specific protein carbonylation sites in model oxidations of human serum albumin. Journal of the American Society for Mass Spectrometry. 2006; 17:1172-1180.
68. Chavez JD, Bisson WH, Maier CS. A targeted mass spectrometry-based approach for the identification and characterization of proteins containing alpha-aminoadipic and gamma-glutamic semialdehyde residues. Anal Bioanal Chem. 2010; 398:2905-2914.
69. Mirzaei H, Regnier F. Affinity chromatographic selection of carbonylated proteins followed by identification of oxidation sites using tandem mass spectrometry. Analytical chemistry. 2005; 77:2386-2392.
70. Chou KC. Using subsite coupling to predict signal peptides. Protein Eng. 2001; 14:75-79.
71. Jia J, Liu Z, Xiao X. 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.
72. Jia J, Liu Z, Xiao X, Liu B. Identification of proteinprotein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC) Journal of Biomolecular Structure & Dynamics. 2015; doi:10.1080/07391102.2015.1095116.
73. Chou KC, Shen HB. Review: Recent progresses in protein subcellular location prediction. Anal Biochem. 2007; 370:1-16.
74. Chou KC. Prediction of signal peptides using scaled window. Peptides. 2001; 22:1973-1979.
75. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012; 28:3150-3152.
76. Chou KC. Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid, 2001, Vol44, 60). 2001; 43:246-255.
77. Chou KC. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics. 2005; 21:10-19.
78. Du P, Wang X, Xu C, Gao Y. PseAAC-Builder: A crossplatform stand-alone program for generating various special Chou's pseudo-amino acid compositions. Anal Biochem. 2012; 425:117-119.
79. Cao DS, Xu QS, Liang YZ. propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics. 2013; 29: 960-962.
80. Lin SX, Lapointe J. Theoretical and experimental biology in one-A symposium in honour of Professor Kuo-Chen Chou's 50th anniversary and Professor Richard Giegé's 40th anniversary of their scientific careers. J Biomedical Science and Engineering. 2013; 6:435-442.
81. Zhong WZ, Zhou SF. Molecular science for drug development and biomedicine. Intenational Journal of Molecular Sciences. 2014; 15:20072-20078.
82. Zhou GP. Current progress in structural bioinformatics of protein-biomolecule interactions. Medicinal Chemistry. 2015; 11:216-216.
83. Zhou GP, Zhong WZ. Perspectives in Medicinal Chemistry. Current Topics in Medicinal Chemistry. 2016; 16:381-382.
84. Khan ZU, Hayat M, Khan MA. Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model. J Theor Biol. 2015; 365:197-203.
85. 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.
86. Kumar R, Srivastava A, Kumari B, Kumar M. Prediction of beta-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. J Theor Biol. 2015; 365:96-103.
87. Mondal S, Pai PP. Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction. J Theor Biol. 2014; 356:30-35.
88. Wang X, Zhang W, Zhang Q, Li GZ. MultiP-SChlo: multilabel protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multilabel classifier. Bioinformatics. 2015; 31:2639-2645.
89. Kabir M, Hayat M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples. Molecular genetics and genomics: MGG. 2016; 291: 285-296.
90. Ahmad K, Waris M, Hayat M. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition. J Membr Biol. 2016:10.1007/s00232-00015-09868-00238.
91. Tang H, Chen W, Lin H. Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique. Mol Biosyst. 2016; 12:1269-1275.
92. 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. International Journal of Molecular Sciences. 2014; 15:3495-3506.
93. Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst. 2015; 11:2620-2634.
94. Chen W, Lei TY, Jin DC, Lin H. PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition. Anal Biochem. 2014; 456:53-60.
95. Chen W, Zhang X, Brooker J, Lin H. PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics. 2015; 31:119-120.
96. Liu B, Liu F, Fang L, Wang X. repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics. 2015; 31:1307-1309.
97. Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences Nucleic Acids Res. 2015; 43:W65-W71.
98. Chou KC. Review: Prediction of human immunodeficiency virus protease cleavage sites in proteins. Anal Biochem. 1996; 233:1-14.
99. Zhang CT. Monte Carlo simulation studies on the prediction of protein folding types from amino acid composition. Biophys J. 1992; 63:1523-1529.
100. Zhang CT. Monte Carlo simulation studies on the prediction of protein folding types from amino acid composition. II. correlative effect. J Protein Chem. 1995; 14:251-258.
101. Kandaswamy KK, Moller S, Suganthan PN, Sridharan S, Pugalenthi G. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J Theor Biol. 2011; 270:56-62.
102. Lin WZ, Fang JA, Xiao X. iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model. PLoS ONE. 2011; 6:e24756.
103. Pugalenthi G, Kandaswamy KK, Kolatkar P. RSARF: Prediction of Residue Solvent Accessibility from Protein Sequence Using Random Forest Method. Protein & Peptide Letters. 2012; 19:50-56.
104. Jia J, Liu Z, Xiao X, Liu B. iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules. 2016; 21:95.
105. Breiman L. Random forests. Machine learning. 2001; 45: 5-32.
106. Chen J, Liu H, Yang J. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids. 2007; 33:423-428.
107. Chou KC. Prediction of protein signal sequences and their cleavage sites. Proteins: Struct, Funct, Genet. 2001; 42:136-139.
108. Chen W, Feng PM, Deng EZ. iTIS-PseTNC: a sequencebased predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem. 2014; 462:76-83.
109. Chen W, Feng PM, Lin H. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. Biomed Research International (BMRI). 2014; 2014:623149.
110. Ding H, Deng EZ, Yuan LF, Liu L. iCTX-Type: A sequencebased predictor for identifying the types of conotoxins in targeting ion channels. BioMed Research International (BMRI). 2014; 2014:286419.
111. Liu B, Fang L, Liu F, Wang X. Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS ONE. 2015; 10:e0121501.
112. Liu B, Liu F, Fang L, Wang X. repRNA: a web server for generating various feature vectors of RNA sequences. Molecular Genetics and Genomics. 2016; 291:473-481.
113. Xiao X, Min JL, Lin WZ, Liu Z. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via the benchmark dataset optimization approach. Journal of Biomolecular Structure & Dynamics. 2015; 33:2221-2233.
114. Chou KC, Wu ZC, Xiao X. iLoc-Hum: Using accumulationlabel scale to predict subcellular locations of human proteins with both single and multiple sites. Molecular Biosystems. 2012; 8:629-641.
115. Lin WZ, Fang JA, Xiao X. iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins Molecular BioSystems. 2013; 9:634-644.
116. Xiao X, Wu ZC. 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; 284:42-51.
117. Xiao X, Wang P, Lin WZ, Jia JH. iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem. 2013; 436: 168-177.
118. Chou KC. Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems. 2013; 9:1092-1100.
119. Chou KC, Zhang CT. Review: Prediction of protein structural classes. Crit Rev Biochem Mol Biol. 1995; 30:275-349.
120. Zhou GP. An intriguing controversy over protein structural class prediction. J Protein Chem. 1998; 17:729-738.
121. Zhou GP, Assa-Munt N. Some insights into protein structural class prediction. Proteins: Struct, Funct, Genet. 2001; 44:57-59.
122. Cai YD, Zhou GP. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J. 2003; 84:3257-3263.
123. Zhou GP, Doctor K. Subcellular location prediction of apoptosis proteins. Proteins: Struct, Funct, Genet. 2003; 50:44-48.
124. Shen HB, Yang J. Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids. 2007; 33:57-67.
125. Chou KC, Cai YD. Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition. Journal of Cellular Biochemistry (Addendum, ibid 2004, 91, 1085). 2003; 90:1250-1260.
126. Chou KC, Cai YD. Prediction of membrane protein types by incorporating amphipathic effects. Journal of Chemical Information and Modeling. 2005; 45:407-413.
127. Fan GL, Zhang XY, Liu YL, Nang Y, Wang H. DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns. J Comput Chem. 2015; 36:2317-2327.