IHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC

Oncotarget

Volumn 7, Issue 28, 2016, Pages 44310-44321

  Qiu, Wang Ren ^a,b   Sun, Bi Qian ^a   Xiao, Xuan ^a,c   Xu, Zhao Chun ^a   Chou, Kuo Chen ^c,d,e  

^a JINGDEZHEN CERAMIC INSTITUTE   (China)

^b University of Missouri   (United States)

^c GORDON LIFE SCIENCE INSTITUTE   (United States)

^d KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^e UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

General PseAAC; Hydroxylysine; Hydroxyproline; PTMs; Sequence coupling model

Indexed keywords

HYDROXYLYSINE; HYDROXYPROLINE; PROTEIN;

AMINO ACID ANALYSIS; AMINO ACID COMPOSITION; AREA UNDER THE CURVE; ARTICLE; BIOINFORMATICS; CALCULATION; COMPUTER PROGRAM; CONTROLLED STUDY; GENE SEQUENCE; HYDROXYLATION; INTERMETHOD COMPARISON; JACKKNIFE TEST; MEASUREMENT ACCURACY; PREDICTION; PSEUDO AMINO ACID COMPOSITION; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; ALGORITHM; AMINO ACID SEQUENCE; CHEMICAL MODEL; CHEMISTRY; HUMAN; INFORMATION PROCESSING; METABOLISM; PROTEIN PROCESSING;

ALGORITHMS; AMINO ACID SEQUENCE; DATASETS AS TOPIC; HUMANS; HYDROXYLATION; HYDROXYLYSINE; HYDROXYPROLINE; MODELS, CHEMICAL; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEINS;

EID: 84978634483     PISSN: None     EISSN: 19492553     Source Type: Journal    
DOI: 10.18632/oncotarget.10027     Document Type: Article

References (123)

1. Krane SM. The importance of proline residues in the structure, stability and susceptibility to proteolytic degradation of collagens. Amino Acids. 2008; 35:703-710.
2. PálfiVK, András P. How stable is a collagen triple helix? An ab initio study on various collagen and beta-sheet forming sequences. J Comput Chem 2008; 29:1374-1386.
3. Guszczyn T, Sobolewski K. Deregulation of collagen metabolism in human stomach cancer. Pathobiology. 2004; 71:308-313.
4. Sunila ES, Kuttan G. A preliminary study on antimetastatic activity of Thuja occidentalis L. in mice model. Immunopharmacol. Immunotoxicol. 2006; 28:269-280.
5. Chandrasekharan G, Girija K. Anti-metastatic effect of Biophytum sensitivum is exerted through its cytokine and immunomodulatory activity and its regulatory effect on the activation and nuclear translocation of transcription factors in B16F-10 melanoma cells. J Exp Ther Oncol. 2008; 7:325-326.
6. Yamauchi M, Shiiba M. Lysine Hydroxylation and Crosslinking of Collagen. Methods Mol Biol. 2008; 446:277-290.
7. Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J Theor Biol. 2011; 273:236-247.
8. Chou KC, 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.
9. Chou KC. Review: Prediction of human immunodeficiency virus protease cleavage sites in proteins. Anal Biochem. 1996; 233:1-14.
10. Xu Y, Wen X, Shao XJ, Deng NY. 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.
11. Chen W, Ding H, Feng P, Lin H, Chou KC. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget. 2016; 7:16895-16909. doi: 10.18632/oncotarget.7815.
12. Chen W, Tang H, Ye J, Lin H. iRNA-PseU: Identifying RNA pseudouridine sites. Molecular Therapy-Nucleic Acids. 2016; 5:e1. (doi:101038/mtna201637).
13. Jia J, Liu Z, Xiao X. iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules. 2016; 21:95.
14. 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.
15. Jia J, Liu Z, Xiao X. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J Theor Biol. 2016; 394:223-230.
16. Jia J, Liu Z, Xiao X, Liu B, Chou KC. iCar-PseCp: identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget. 2016; doi:10.18632/oncotarget.9148.
17. Liu B, Fang L, Liu F, Wang X. iMiRNA-PseDPC: microRNA precursor identification with a pseudo distancepair composition approach. J Biomol Struct Dyn. 2016; 34:223-235.
18. Liu B, Fang L, Long R, Lan X. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics. 2016; 32:362-389.
19. Liu B, Long R. iDHS-EL: Identifying DNase I hypersensi-tivesites by fusing three different modes of pseudo nucleotide composition into an en-semble learning framework. Bioinformatics. 2016; doi:10.1093/bioinformatics/btw186.
20. 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.
21. Qiu WR, Sun BQ, Xiao X, Xu D. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Molecular Informatics. 2016; doi:10.1002/minf.201600010.
22. Qiu WR, Xiao X, Xu ZH, Chou KC. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget. 2016. doi: 10.18632/oncotarget.9987
23. Xiao X, Ye HX, Liu Z, Jia JH, Chou KC. iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget. 2016; doi:10.18632/oncotarget.9057.
24. Chou KC. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J Biol Chem. 1993; 268:16938-16948.
25. Jiang SP, Liu WM, Fee CH. Graph theory of enzyme kinetics: 1. Steady-state reaction system. Scientia Sinica. 1979; 22:341-358.
26. Cornish-Bowden A. Fundamentals of Enzyme Kinetics, Chapter 4. (London: Butterworths). 1979.
27. Forsen S. Graphical rules for enzyme-catalyzed rate laws. Biochem J. 1980; 187:829-835.
28. Chou KC. A new schematic method in enzyme kinetics. Eur J Biochem. 1980; 113:195-198.
29. Liu WM. Graphical rules for non-steady state enzyme kinetics. J Theor Biol. 1981; 91:637-654.
30. Chou KC. Two new schematic rules for rate laws of enzymecatalyzed reactions. J Theor Biol. 1981; 89:581-592.
31. 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.
32. Chou KC, Shen HB. FoldRate: A web-server for predicting protein folding rates from primary sequence. The Open Bioinformatics Journal. 2009; 3:31-50
33. Shen HB, Song JN. Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J Biomed Sci Eng. (JBiSE). 2009; 2: 136-143.
34. Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Romero DL, AristoffPA, Tarpley WG, Reusser F. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J Biol Chem. 1993; 268:6119-6124.
35. Althaus IW, Gonzales AJ, Romero DL, AristoffPA, Tarpley WG, Reusser F. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem. 1993; 268:14875-14880.
36. Gonzales AJ, Diebel MR, Kezdy FJ, Romero DL, AristoffPA, Tarpley WG, Reusser F. Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry. 1993; 32:6548-6554.
37. Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Kezdy FJ, Romero DL, AristoffPA, Tarpley WG, Reusser F. Steadystate kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Experientia. 1994; 50:23-28.
38. Diebel MR, Romero DL, Thomas RC, AristoffPA, Tarpley WG, Reusser F. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-90152E. Biochem Pharmacol. 1994; 47:2017-2028.
39. Franks KM, Diebel MR, Kezdy FJ, Romero DL, Thomas RC, AristoffPA, Tarpley WG, Reusser F. The benzylthiopyrididine U-31, 355 is a potent inhibitor of HIV-1 reverse transcriptase. Biochem Pharmacol. 1996; 51:743-750.
40. Kezdy FJ, Reusser F. Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal Biochem. 1994; 221:217-230.
41. Forsen S. Graphical rules of steady-state reaction systems. Can J Chem. 1981; 59:737-755.
42. Chou KC. Graphic rule for drug metabolism systems. Curr Drug Metab. 2010; 11:369-378.
43. 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.
44. Chou KC, Zhang CT. Diagrammatization of codon usage in 339 HIV proteins and its biological implication. AIDS AIDS Res Hum Retroviruses. 1992; 8:1967-1976.
45. Zhang CT. Graphic analysis of codon usage strategy in 1490 human proteins. J Protein Chem. 1993; 12:329-335.
46. Zhang CT. Analysis of codon usage in 1562 E. Coli protein coding sequences. J Mol Biol. 1994; 238:1-8.
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. Xiao X, Shao SH. A probability cellular automaton model for hepatitis B viral infections. Biochem Biophys Res Comm. 2006; 342:605-610.
49. Xiao X, Shao S, Ding Y, Huang Z. An Application of Gene Comparative Image for Predicting the Effect on Replication Ratio by HBV Virus Gene Missense Mutation. J Theor Biol. 2005; 235:555-565.
50. Xiao X, Shao S, Ding Y, Huang Z. Using cellular automata to generate Image representation for biological sequences. Amino Acids. 2005; 28:29-35.
51. Fawcett JA. An Introduction to ROC Analysis. Pattern Recognition Letters. 2005; 27:861-874.
52. Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning (ICML), pp. 233-240.
53. Zhang CT. Prediction of beta-turns in proteins by 1-4 and 2-3 correlation model. Biopolymers. 1997; 41:673-702.
54. Chou KC. Prediction and classification of alpha-turn types. Biopolymers. 1997; 42:837-853.
55. Chou KC. Review: Prediction of tight turns and their types in proteins. Anal Biochem. 2000; 286:1-16.
56. Chou KC. A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase. Protein Sci. 1995; 4:1365-1383.
57. Zhang CT. An alternate-subsite-coupled model for predicting HIV protease cleavage sites in proteins. Protein Eng. 1993; 7:65-73.
58. Chou KC. Using subsite coupling to predict signal peptides. Protein Eng. 2001; 14:75-79.
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. Shen HB. Signal-3L: a 3-layer approach for predicting signal peptide. Biochem Biophys Res Comm (BBRC). 2007; 363:297-303.
61. Chou KC. Prediction of signal peptides using scaled window. Peptides. 2001; 22:1973-1979.
62. 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.
63. 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.
64. 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.
65. Jia J, Liu Z, Xiao X. Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC). J Biomol Struct. Dyn. 2015; doi:10.1080/07391102.2015.1095116.
66. Chou KC, Shen HB. Review: Recent progresses in protein subcellular location prediction. Anal Biochem. 2007; 370:1-16.
67. Chou KC. Impacts of bioinformatics to medicinal chemistry. Med. Chem. 2015; 11:218-234.
68. Chou KC. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct Funct Genet. (Erratum: ibid, 2001, Vol44, 60). 2001; 43:246-255.
69. Chou KC. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics. 2005; 21:10-19.
70. 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.
71. Cao DS, Xu QS, Liang YZ. propy: a tool to generate various modes of Chou's PseAAC. Bioinformatics. 2013; 29: 960-962.
72. 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 Biomed Sci Eng. 2013; 6:435-442.
73. 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.
74. Dehzangi A, Heffernan R, Sharma A, Lyons J, Paliwal K, Sattar A. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionarybased descriptors into Chou's general PseAAC. J Theor Biol. 2015; 364:284-294.
75. 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.
76. Mondal S, Pai PP. Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction. J Theor Biol. 2014; 356:30-35.
77. 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.
78. 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: MGG. 2016; 291:285-296.
79. 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.
80. 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.
81. 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; 15:3495-3506.
82. Chen W, Lin H. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst. 2015; 11:2620-2634.
83. Zhong WZ, Zhou SF. Molecular science for drug development and biomedicine. Int J Mol Sci. 2014; 15:20072-20078.
84. Chou KC. An unprecedented revolution in medicinal science (doi:10.3390/MOL2NET-1-b040). Proceedings of the MOL2NET (International Conference on Multidisciplinary Sciences) 2015; 1:1-10.
85. Zhou GP. Current progress in structural bioinformatics of protein-biomolecule interactions. Med Chem. 2015; 11:216-216.
86. Zhou GP, Zhong WZ. Perspectives in Medicinal Chemistry. Curr Top Med Chem. 2016; 16:381-382.
87. 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.
88. Chen W, Zhang X, Brooker J. PseKNC-General: a crossplatform package for generating various modes of pseudo nucleotide compositions. Bioinformatics. 2015; 31:119-120.
89. 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.
90. Liu B, Liu F, Wang X, Chen J. 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.
91. 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.
92. Lin WZ, Fang JA, Xiao X. iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model. PLoS ONE. 2011; 6:e24756.
93. Pugalenthi G, Kandaswamy KK, Kolatkar P. RSARF: Prediction of Residue Solvent Accessibility from Protein Sequence Using Random Forest Method. Protein Pept Lett. 2012; 19:50-56.
94. Breiman L. Random forests. Machine learning. 2001; 45:5-32.
95. Chen J, Liu H, Yang J. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids. 2007; 33: 423-428.
96. Chou KC. Prediction of protein signal sequences and their cleavage sites. Proteins: Struct, Funct, Genet. 2001; 42:136-139.
97. 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.
98. Chen W, Feng PM, Lin H. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 2013; 41:e68.
99. Chen W, Feng PM, Deng EZ, Lin H. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem. 2014; 462:76-83.
100. Chen W, Feng PM, Lin H. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BioMed Res Int. (BMRI). 2014; 2014:623149.
101. Ding H, Deng EZ, Yuan LF, Liu L. iCTX-Type: A sequencebased predictor for identifying the types of conotoxins in targeting ion channels. BioMed Res Int. (BMRI). 2014; 2014:286419.
102. Chen W, Feng P, Ding H. iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition Anal Biochem. (also, Data in Brief, 2015, 5: 376-378). 2015; 490:26-33.
103. Liu B, Fang L, Liu F, Wang X, Chen J. Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS ONE. 2015; 10:e0121501.
104. Liu B, Liu F, Fang L, Wang X. repRNA: a web server for generating various feature vectors of RNA sequences. Mol Genet Genomics. 2016; 291:473-481.
105. Liu B, Fang L, Wang S, Wang X. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy J Theor Biol. 2015; 385:153-159.
106. 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. J Biomol Struct Dyn. (JBSD). 2015; 33:2221-2233.
107. Liu Z, Xiao X, Qiu WR. iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Anal. Biochem. (also, Data in Brief, 2015, 4: 87-89). 2015; 474:69-77.
108. Chen W, Feng P, Ding H. Using deformation energy to analyze nucleosome positioning in genomes. Genomics. 2016; 107:69-75.
109. Chou KC, Wu ZC, Xiao X. iLoc-Hum: Using accumulationlabel scale to predict subcellular locations of human proteins with both single and multiple sites. Mol. BioSyst. 2012; 8:629-641.
110. Lin WZ, Fang JA, Xiao X. iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins. Mol BioSyst. 2013; 9:634-644.
111. 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.
112. 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.
113. Chou KC. Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Mol BioSyst. 2013; 9:1092-1100.
114. Chou KC, Zhang CT. Review: Prediction of protein structural classes. Crit Rev Biochem Mol Biol. 1995; 30:275-349.
115. Zhou GP. An intriguing controversy over protein structural class prediction. J Protein Chem. 1998; 17:729-738.
116. Zhou GP, Assa-Munt N. Some insights into protein structural class prediction. Proteins: Struct, Funct, Genet. 2001; 44:57-59.
117. Cai YD, Zhou GP. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J. 2003; 84:3257-3263.
118. Zhou GP, Doctor K. Subcellular location prediction of apoptosis proteins. Proteins: Struct, Funct, Genet. 2003; 50:44-48.
119. Shen HB, Yang J. Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids. 2007; 33:57-67.
120. Cai YD. Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition. J Cell Biochem. (Addendum, ibid 2004, 91, 1085). 2003; 90:1250-1260.
121. Cai YD. Prediction of membrane protein types by incorporating amphipathic effects. J Chem Inf Model. 2005; 45:407-413.
122. 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.
123. Ju Z, Cao JZ, 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.