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Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses

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Abstract

In this minireview paper it has been elucidated that the proposal of pseudo amino acid components represents a very important milestone for the disciplines of proteome and genome. This has been concluded by observing and analyzing the developments in the following six different sub-disciplines: (1) proteome analysis; (2) genome analysis; (3) protein structural classification; (4) protein subcellular location prediction; (5) post-translational modification (PTM) site prediction; (6) stimulating the birth of the renowned and very powerful 5-steps rule.

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References

  • Adilina S, Farid DM, Shatabda S (2019) Effective DNA binding protein prediction by using key features via Chou’s general PseAAC. J Theor Biol 460:64–78

    CAS  PubMed  Google Scholar 

  • Ahmad J, Hayat M (2018) MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. J Theor Biol 463:99–109

    PubMed  Google Scholar 

  • Ahmad J, Hayat M (2019) MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. J Theor Biol 463:99–109

    CAS  PubMed  Google Scholar 

  • Ahmad S, Kabir M, Hayat M (2015) Identification of heat shock protein families and J-protein types by incorporating dipeptide composition into Chou’s general PseAAC. Comput Methods Programs Biomed 122:165–174

    PubMed  Google Scholar 

  • Ahmad K, Waris M, Hayat M (2016) Prediction of protein submitochondrial locations by incorporating dipeptide composition into Chou’s general pseudo amino acid composition. J Membr Biol 249:293–304

    CAS  PubMed  Google Scholar 

  • Akbar S, Hayat M (2018) iMethyl-STTNC: identification of N(6)-methyladenosine sites by extending the Idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J Theor Biol 455:205–211

    CAS  PubMed  Google Scholar 

  • Ali F, Hayat M (2015) Classification of membrane protein types using Voting Feature Interval in combination with Chou’s Pseudo Amino Acid Composition. J Theor Biol 384:78–83

    CAS  PubMed  Google Scholar 

  • Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Chou KC, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F (1993a) Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J Biol Chem 268:6119–6124

    CAS  PubMed  Google Scholar 

  • Althaus IW, Gonzales AJ, Chou JJ, Diebel MR, Chou KC, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F (1993b) The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem 268:14875–14880

    CAS  PubMed  Google Scholar 

  • Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Chou KC, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F (1993c) Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry 32:6548–6554

    CAS  PubMed  Google Scholar 

  • Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Chou KC, Kezdy FJ, Romero DL, Aristoff PA, Tarpley WG, Reusser F (1994a) Steady-state kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Cell Mol Life Sci (Experientia) 50:23–28

    CAS  Google Scholar 

  • Althaus IW, Chou JJ, Gonzales AJ, Diebel MR, Chou KC, Kezdy FJ, Romero DL, Thomas RC, Aristoff PA, Tarpley WG, Reusser F (1994b) Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152e. Biochem Pharmacol 47:2017–2028

    CAS  PubMed  Google Scholar 

  • Althaus IW, Chou KC, Franks KM, Diebel MR, Kezdy FJ, Romero DL, Thomas RC, Aristoff PA, Tarpley WG, Reusser F (1996) The benzylthio-pyrididine U-31,355, a potent inhibitor of HIV-1 reverse transcriptase. Biochem Pharmacol 51:743–750

    CAS  PubMed  Google Scholar 

  • Andraos J (2008) Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs. Can J Chem 86:342–357

    CAS  Google Scholar 

  • Arif M, Hayat M, Jan Z (2018) iMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition. J Theor Biol 442:11–21

    CAS  PubMed  Google Scholar 

  • Awais M, Hussain W, Khan YD, Rasool N, Khan SA, Chou KC (2019) 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. https://doi.org/10.1109/tcbb.2019.2919025

    Article  Google Scholar 

  • Behbahani M, Mohabatkar H, Nosrati M (2016) 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 411:1–5

    CAS  PubMed  Google Scholar 

  • Butt AH, Rasool N, Khan YD (2018) Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC. Mol Biol Rep. https://doi.org/10.1007/s11033-018-4391-5

    Article  PubMed  Google Scholar 

  • Butt AH, Rasool N, Khan YD (2019) Prediction of antioxidant proteins by incorporating statistical moments based features into Chou’s PseAAC. J Theor Biol 473:1–8

    CAS  PubMed  Google Scholar 

  • Cai YD, Chou KC (2003) Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudon amino acid composition. Biochem Biophys Res Commun (BBRC) 305:407–411

    CAS  Google Scholar 

  • Cai YD, Chou KC (2005) Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. J Proteome Res 4:967–971

    CAS  PubMed  Google Scholar 

  • Cai YD, Chou KC (2006) Predicting membrane protein type by functional domain composition and pseudo amino acid composition. J Theor Biol 238:395–400

    CAS  PubMed  Google Scholar 

  • Cai YD, Liu XJ, Xu XB, Chou KC (2002) Prediction of protein structural classes by support vector machines. Comput Chem 26:293–296

    CAS  PubMed  Google Scholar 

  • Cai YD, Zhou GP, Chou KC (2005) Predicting enzyme family classes by hybridizing gene product composition and pseudo amino acid composition. J Theor Biol 234:145–149

    CAS  PubMed  Google Scholar 

  • Cai YD, Feng KY, Lu WC, Chou KC (2006) Using LogitBoost classifier to predict protein structural classes. J Theor Biol 238:172–176

    CAS  PubMed  Google Scholar 

  • Cao JZ, Liu WQ, Gu H (2012) Predicting viral protein subcellular localization with Chou’s pseudo amino acid composition and imbalance-weighted multi-label K-nearest neighbor algorithm. Protein Pept Lett 19:1163–1169

    CAS  PubMed  Google Scholar 

  • Cao DS, Xu QS, Liang YZ (2013) propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29:960–962

    CAS  PubMed  Google Scholar 

  • Chandra A, Sharma A, Dehzangi A, Ranganathan S, Jokhan A, Chou KC, Tsunoda T (2018) PhoglyStruct: prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Sci Rep 8:17923

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chang TH, Wu LC, Lee TY, Chen SP, Huang HD, Horng JT (2013) EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou’s PseAAC. J Comput Aided Mol Des 27:91–103

    CAS  PubMed  Google Scholar 

  • Chen YL, Li QZ (2007) Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo amino acid composition. J Theor Biol 248:377–381

    CAS  PubMed  Google Scholar 

  • Chen YK, Li KB (2013) Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition. J Theor Biol 318:1–12

    CAS  PubMed  Google Scholar 

  • Chen C, Tian YX, Zou XY, Cai PX, Mo JY (2006a) Using pseudo amino acid composition and support vector machine to predict protein structural class. J Theor Biol 243:444–448

    CAS  PubMed  Google Scholar 

  • Chen C, Zhou X, Tian Y, Zou X, Cai P (2006b) Predicting protein structural class with pseudo amino acid composition and support vector machine fusion network. Anal Biochem 357:116–121

    CAS  PubMed  Google Scholar 

  • Chen C, Chen L, Zou X, Cai P (2009) Prediction of protein secondary structure content by using the concept of Chou’s pseudo amino acid composition and support vector machine. Protein Pept Lett 16:27–31

    PubMed  Google Scholar 

  • Chen C, Shen ZB, Zou XY (2012a) Dual-layer wavelet SVM for predicting protein structural class via the general form of Chou’s pseudo amino acid composition. Protein Pept Lett 19:422–429

    CAS  PubMed  Google Scholar 

  • Chen YL, Li QZ, Zhang LQ (2012b) Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo amino acid composition and structural alphabet. Amino Acids 42:1309–1316

    CAS  PubMed  Google Scholar 

  • Chen W, Feng PM, Lin H, Chou KC (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41:e68

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chen W, Lei TY, Jin DC, Lin H, Chou KC (2014a) PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition. Anal Biochem 456:53–60

    CAS  PubMed  Google Scholar 

  • Chen W, Feng PM, Deng EZ, Lin H, Chou KC (2014b) iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem 462:76–83

    CAS  PubMed  Google Scholar 

  • Chen L, Chu C, Huang T, Kong X, Cai YD (2015a) Prediction and analysis of cell-penetrating peptides using pseudo amino acid composition and random forest models. Amino Acids. https://doi.org/10.1007/s00726-015-1974-5

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen W, Lin H, Chou KC (2015b) Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst 11:2620–2634

    CAS  PubMed  Google Scholar 

  • Chen W, Zhang X, Brooker J, Lin H, Zhang L, Chou KC (2015c) PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31:119–120

    CAS  PubMed  Google Scholar 

  • Chen W, Feng P, Ding H, Lin H, Chou KC (2015d) iRNA-Methyl: identifying N6-methyladenosine sites using pseudo nucleotide composition. Anal Biochem 490:26–33

    CAS  PubMed  Google Scholar 

  • Chen W, Tang H, Ye J, Lin H, Chou KC (2016) iRNA-PseU: identifying RNA pseudouridine sites. Mol Ther Nucleic Acids 5:e332

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC (2017) iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget 8:4208–4217

    PubMed  Google Scholar 

  • Chen W, Ding H, Zhou X, Lin H, Chou KC (2018a) iRNA(m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition. Anal Biochem 561–562:59–65

    PubMed  Google Scholar 

  • Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC (2018b) iRNA-3typeA: identifying 3-types of modification at RNA’s adenosine sites. Mol Ther Nucleic Acids 11:468–474

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chen Z, Liu X, Li F, Li C, Marquez-Lago T, Leier A, Akutsu T, Webb GI, Xu D, Smith AI, Li L, Chou KC, Song J (2018c) Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Brief Bioinform. https://doi.org/10.1093/bib/bby089

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen G, Cao M, Yu J, Guo X, Shi S (2019) Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou’s general PseAAC. J Theor Biol 461:92–101

    CAS  PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou KC (2017a) pLoc-mPlant: predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC. Mol BioSyst 13:1722–1727

    CAS  PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou KC (2017) pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene 628:315–321; Erratum in Gene 2018, 644:156

  • Cheng X, Zhao SG, Lin WZ, Xiao X, Chou KC (2017c) pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics 33:3524–3531

    CAS  PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou KC (2018a) pLoc-mEuk: predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 110:50–58

    CAS  PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou KC (2018b) pLoc-mGneg: predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics 110:231–239

    CAS  Google Scholar 

  • Cheng X, Xiao X, Chou KC (2018c) pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 34:1448–1456

    CAS  PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou KC (2018d) pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. J Theor Biol 458:92–102

    CAS  PubMed  Google Scholar 

  • Cheng X, Xiao X, Chou KC (2018e) pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. Curr Pharm Des 24:4013–4022

    CAS  PubMed  Google Scholar 

  • Cheng X, Lin WZ, Xiao X, Chou KC (2019) pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics 35:398–406

    CAS  PubMed  Google Scholar 

  • Chou KC (1983a) Low-frequency vibrations of helical structures in protein molecules. Biochem J 209:573–580

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chou KC (1983b) Identification of low-frequency modes in protein molecules. Biochem J 215:465–469

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chou KC (1984a) Biological functions of low-frequency vibrations (phonons). 3. Helical structures and microenvironment. Biophys J 45:881–889

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chou KC (1984b) The biological functions of low-frequency phonons. 4. Resonance effects and allosteric transition. Biophys Chem 20:61–71

    CAS  PubMed  Google Scholar 

  • Chou KC (1984c) Low-frequency vibrations of DNA molecules. Biochem J 221:27–31

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chou KC (1985a) Prediction of a low-frequency mode in BPTI. Int J Biol Macromol 7:77–80

    CAS  Google Scholar 

  • Chou KC (1985b) Low-frequency motions in protein molecules: beta-sheet and beta-barrel. Biophys J 48:289–297

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chou KC (1986) Origin of low-frequency motion in biological macromolecules: a view of recent progress of quasi-continuity model. Biophys Chem 25:105–116

    CAS  PubMed  Google Scholar 

  • Chou KC (1987) The biological functions of low-frequency phonons. 6. A possible dynamic mechanism of allosteric transition in antibody molecules. Biopolymers 26:285–295

    CAS  PubMed  Google Scholar 

  • Chou KC (1988) Review: low-frequency collective motion in biomacromolecules and its biological functions. Biophys Chem 30:3–48

    CAS  PubMed  Google Scholar 

  • Chou KC (1989a) Low-frequency resonance and cooperativity of hemoglobin. Trends Biochem Sci 14:212–213

    CAS  PubMed  Google Scholar 

  • Chou KC (1989b) Graphic rules in steady and non-steady enzyme kinetics. J Biol Chem 264:12074–12079

    CAS  PubMed  Google Scholar 

  • Chou KC (1990) Review: applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophys Chem 35:1–24

    CAS  PubMed  Google Scholar 

  • Chou KC (1993) Mini review: prediction of protein folding types from amino acid composition by correlation angles. Amino Acids 6:231–246

    Google Scholar 

  • Chou KC (1995) Does the folding type of a protein depend on its amino acid composition? FEBS Lett 363:127–131

    CAS  PubMed  Google Scholar 

  • Chou KC (1999) A key driving force in determination of protein structural classes. Biochem Biophys Res Commun (BBRC) 264:216–224

    CAS  Google Scholar 

  • Chou KC (2000) Review: prediction of protein structural classes and subcellular locations. Curr Protein Pept Sci 1:171–208

    CAS  PubMed  Google Scholar 

  • Chou KC (2001) Prediction of protein cellular attributes using pseudo amino acid composition. Proteins 43:246–255; Erratum in Proteins 2001, 44:60

  • Chou KC (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19

    CAS  PubMed  Google Scholar 

  • Chou KC (2009) Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr Proteomics 6:262–274

    CAS  Google Scholar 

  • Chou KC (2010) Graphic rule for drug metabolism systems. Curr Drug Metab 11:369–378

    CAS  PubMed  Google Scholar 

  • Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition (50th anniversary year review, 5-steps rule). J Theor Biol 273:236–247

    CAS  PubMed  Google Scholar 

  • Chou KC (2013) Some remarks on predicting multi-label attributes in molecular biosystems. Mol BioSyst 9:1092–1100

    CAS  PubMed  Google Scholar 

  • Chou KC (2015) Impacts of bioinformatics to medicinal chemistry. Med Chem 11:218–234

    CAS  PubMed  Google Scholar 

  • Chou KC (2017) An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr Top Med Chem 17:2337–2358

    CAS  PubMed  Google Scholar 

  • Chou KC (2019a) Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr Med Chem. https://doi.org/10.2174/0929867326666190507082559

    Article  PubMed  Google Scholar 

  • Chou KC (2019b) Progresses in predicting post-translational modification. Int J Pept Res Ther (IJPRT). https://doi.org/10.1007/s10989-019-09893-5

    Article  Google Scholar 

  • Chou KC, Cai YD (2003a) Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition. J Cell Biochem 90:1250–1260; Addendum in J Cell Biochem 2004, 91:1085

  • Chou KC, Cai YD (2003b) Predicting protein quaternary structure by pseudo amino acid composition. Proteins 53:282–289

    CAS  PubMed  Google Scholar 

  • Chou KC, Cai YD (2004) Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo amino acid composition. J Cell Biochem 91:1197–1203

    CAS  PubMed  Google Scholar 

  • Chou KC, Elrod DW (2002) Bioinformatical analysis of G-protein-coupled receptors. J Proteome Res 1:429–433

    CAS  PubMed  Google Scholar 

  • Chou KC, Forsen S (1980a) Diffusion-controlled effects in reversible enzymatic fast reaction system: critical spherical shell and proximity rate constants. Biophys Chem 12:255–263

    CAS  PubMed  Google Scholar 

  • Chou KC, Forsen S (1980b) Graphical rules for enzyme-catalyzed rate laws. Biochem J 187:829–835

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chou KC, Forsen S (1981) Graphical rules of steady-state reaction systems. Can J Chem 59:737–755

    Google Scholar 

  • Chou KC, Kiang YS (1985) The biological functions of low-frequency phonons: 5. A phenomenological theory. Biophys Chem 22:219–235

    CAS  PubMed  Google Scholar 

  • Chou KC, Maggiora GM (1988) The biological functions of low-frequency phonons: 7. The impetus for DNA to accommodate intercalators. Brit Polym J 20:143–148

    CAS  Google Scholar 

  • Chou KC, Shen HB (2007) Recent progresses in protein subcellular location prediction. Anal Biochem 370:1–16

    CAS  PubMed  Google Scholar 

  • Chou KC, Shen HB (2009) FoldRate: a web-server for predicting protein folding rates from primary sequence. Open Bioinform J 3:31–50

    CAS  Google Scholar 

  • Chou KC, Zhang CT (1992) A correlation coefficient method to predicting protein structural classes from amino acid compositions. Eur J Biochem 207:429–433

    CAS  PubMed  Google Scholar 

  • Chou JJ, Zhang CT (1993a) A joint prediction of the folding types of 1490 human proteins from their genetic codons. J Theor Biol 161:251–262

    CAS  PubMed  Google Scholar 

  • Chou KC, Zhang CT (1993b) A new approach to predicting protein folding types. J Protein Chem 12:169–178

    CAS  PubMed  Google Scholar 

  • Chou KC, Zhang CT (1994) Predicting protein folding types by distance functions that make allowances for amino acid interactions. J Biol Chem 269:22014–22020

    CAS  PubMed  Google Scholar 

  • Chou KC, Forsen S, Zhou GQ (1980a) Three schematic rules for deriving apparent rate constants. Chem Scr 16:109–113

    Google Scholar 

  • Chou KC, Li TT, Forsen S (1980b) The critical spherical shell in enzymatic fast reaction systems. Biophys Chem 12:265–269

    CAS  PubMed  Google Scholar 

  • Chou KC, Carter RE, Forsen S (1981a) A new graphical method for deriving rate equations for complicated mechanisms. Chem Scr 18:82–86

    CAS  Google Scholar 

  • Chou KC, Chen NY, Forsen S (1981b) The biological functions of low-frequency phonons: 2. Cooperative effects. Chem Scr 18:126–132

    CAS  Google Scholar 

  • Chou KC, Maggiora GM, Mao B (1989) Quasi-continuum models of twist-like and accordion-like low-frequency motions in DNA. Biophys J 56:295–305

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chou KC, Kezdy FJ, Reusser F (1994a) Review: kinetics of processive nucleic acid polymerases and nucleases. Anal Biochem 221:217–230

    CAS  PubMed  Google Scholar 

  • Chou KC, Zhang CT, Maggiora GM (1994b) Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth. Biopolymers 34:143–153

    CAS  PubMed  Google Scholar 

  • Chou KC, Liu W, Maggiora GM, Zhang CT (1998) Prediction and classification of domain structural classes. Proteins 31:97–103

    CAS  PubMed  Google Scholar 

  • Chou KC, Lin WZ, Xiao X (2011) Wenxiang: a web-server for drawing Wenxiang diagrams. Nat Sci 3:862–865

    CAS  Google Scholar 

  • Chou KC, Cheng X, Xiao X (2018a) pLoc_bal-mHum: predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics. https://doi.org/10.1016/j.ygeno.2018.08.007

    Article  PubMed  PubMed Central  Google Scholar 

  • Chou KC, Cheng X, Xiao X (2018b) pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med Chem 15:472–485

    Google Scholar 

  • Contreras-Torres E (2018) Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC. J Theor Biol 454:139–145

    CAS  PubMed  Google Scholar 

  • Dehzangi A, Heffernan R, Sharma A, Lyons J, Paliwal K, Sattar A (2015) Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou’s general PseAAC. J Theor Biol 364:284–294

    CAS  PubMed  Google Scholar 

  • Diao Y, Ma D, Wen Z, Yin J, Xiang J, Li M (2008) Using pseudo amino acid composition to predict transmembrane regions in protein: cellular automata and Lempel-Ziv complexity. Amino Acids 34:111–117

    CAS  PubMed  Google Scholar 

  • Ding YS, Zhang TL (2008) Using Chou’s pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier. Pattern Recogn Lett 29:1887–1892

    CAS  Google Scholar 

  • Ding YS, Zhang TL, Chou KC (2007) Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Pept Lett 14:811–815

    CAS  PubMed  Google Scholar 

  • Ding H, Luo L, Lin H (2009) Prediction of cell wall lytic enzymes using Chou’s amphiphilic pseudo amino acid composition. Protein Pept Lett 16:351–355

    CAS  PubMed  Google Scholar 

  • Ding H, Liu L, Guo FB, Huang J, Lin H (2011) Identify Golgi protein types with modified mahalanobis discriminant algorithm and pseudo amino acid composition. Protein Pept Lett 18:58–63

    CAS  PubMed  Google Scholar 

  • Ding H, Deng EZ, Yuan LF, Liu L, Lin H, Chen W, Chou KC (2014) iCTX-Type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Res Int (BMRI) 2014:286419

    Google Scholar 

  • Du P, Li Y (2006) Prediction of protein submitochondria locations by hybridizing pseudo amino acid composition with various physicochemical features of segmented sequence. BMC Bioinformatics 7:518

    PubMed  PubMed Central  Google Scholar 

  • Du QS, Jiang ZQ, He WZ, Li DP, Chou KC (2006) Amino acid principal component analysis (AAPCA) and its applications in protein structural class prediction. J Biomol Struct Dyn (JBSD) 23:635–640

    CAS  Google Scholar 

  • Du P, Cao S, Li Y (2009) SubChlo: predicting protein subchloroplast locations with pseudo amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. J Theor Biol 261:330–335

    CAS  PubMed  Google Scholar 

  • Du P, Wang X, Xu C, Gao Y (2012) PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo amino acid compositions. Anal Biochem 425:117–119

    CAS  PubMed  Google Scholar 

  • Du P, Gu S, Jiao Y (2014) 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 15:3495–3506

    CAS  PubMed  PubMed Central  Google Scholar 

  • Ehsan A, Mahmood MK, Khan YD, Barukab OM, Khan SA, Chou KC (2019) iHyd-PseAAC (EPSV): identify hydroxylation sites in proteins by extracting enhanced position and sequence variant feature via Chou’s 5-step rule and general pseudo amino acid composition. Curr Genomics 20:124–133

    CAS  PubMed  PubMed Central  Google Scholar 

  • Esmaeili M, Mohabatkar H, Mohsenzadeh S (2010) Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses. J Theor Biol 263:203–209

    CAS  PubMed  Google Scholar 

  • Fan GL, Li QZ (2012a) Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou’s pseudo amino acid composition. J Theor Biol 304:88–95

    CAS  PubMed  Google Scholar 

  • Fan GL, Li QZ (2012b) Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition. Amino Acids 43:545–555

    CAS  PubMed  Google Scholar 

  • Fan GL, Li QZ (2013) Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou’s pseudo amino acid composition. J Theor Biol 334:45–51

    CAS  PubMed  Google Scholar 

  • Fan G-L, Li Q-Z, Zuo Y-C (2013) Predicting acidic and alkaline enzymes by incorporating the average chemical shift and gene ontology informations into the general form of Chou’s PseAAC. Process Biochem 48:1048–1053

    CAS  Google Scholar 

  • Fan GL, Liu YL, Wang H (2016) Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou’s general pseudo amino acid composition. J Theor Biol 407:138–142

    CAS  PubMed  Google Scholar 

  • Fang Y, Guo Y, Feng Y, Li M (2008) Predicting DNA-binding proteins: approached from Chou’s pseudo amino acid composition and other specific sequence features. Amino Acids 34:103–109

    CAS  PubMed  Google Scholar 

  • Feng PM, Chen W, Lin H, Chou KC (2013) iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal Biochem 442:118–125

    CAS  PubMed  Google Scholar 

  • Feng P, Ding H, Yang H, Chen W, Lin H, Chou KC (2017) iRNA-PseColl: identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol Ther Nucleic Acids 7:155–163

    CAS  PubMed  PubMed Central  Google Scholar 

  • Feng P, Yang H, Ding H, Lin H, Chen W, Chou KC (2019) iDNA6 mA-PseKNC: identifying DNA N(6)-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 111:96–102

    CAS  PubMed  Google Scholar 

  • Fu X, Zhu W, Liso B, Cai L, Peng L, Yang J (2018) Improved DNA-binding protein identification by incorporating evolutionary information into the Chou’s PseAAC. IEEE Access. https://doi.org/10.1109/access.2018.2876656

    Article  Google Scholar 

  • Gao Y, Shao SH, Xiao X, Ding YS, Huang YS, Huang ZD, Chou KC (2005) Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter. Amino Acids 28:373–376

    CAS  PubMed  Google Scholar 

  • Gao QB, Jin ZC, Ye XF, Wu C, He J (2009) Prediction of nuclear receptors with optimal pseudo amino acid composition. Anal Biochem 387:54–59

    CAS  PubMed  Google Scholar 

  • Gao QB, Ye XF, Jin ZC, He J (2010) Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition. Anal Biochem 398:52–59

    CAS  PubMed  Google Scholar 

  • Gao QB, Zhao H, Ye X, He J (2012) Prediction of pattern recognition receptor family using pseudo amino acid composition. Biochem Biophys Res Commun 417:73–77

    CAS  PubMed  Google Scholar 

  • Georgiou DN, Karakasidis TE, Megaritis AC (2013) A short survey on genetic sequences, Chou’s pseudo amino acid composition and its combination with fuzzy set theory. Open Bioinform J 7:41–48

    CAS  Google Scholar 

  • Ghauri AW, Khan YD, Rasool N, Khan SA, Chou KC (2018) pNitro-Tyr-PseAAC: predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC. Curr Pharm Des 24:4034–4043

    CAS  PubMed  Google Scholar 

  • Gordon G (2007) Designed electromagnetic pulsed therapy: clinical applications. J Cell Physiol 212:579–582

    CAS  PubMed  Google Scholar 

  • Gordon G (2008) Extrinsic electromagnetic fields, low frequency (phonon) vibrations, and control of cell function: a non-linear resonance system. J Biomed Sci Eng (JBiSE) 1:152–156

    CAS  Google Scholar 

  • Gu Q, Ding Y, Zhang T (2008) Prediction of G-protein-coupled receptor classes with pseudo amino acid composition. IEEE Xplore, iCBBE, Shanghai, China

  • Gu Q, Ding Y, Zhang T, Shen Y (2010a) Prediction of G-protein-coupled receptor classes with pseudo amino acid composition]. J Biomed Eng 27:500–504

    CAS  Google Scholar 

  • Gu Q, Ding YS, Zhang TL (2010b) Prediction of G-protein-coupled receptor classes in low homology using Chou’s pseudo amino acid composition with approximate entropy and hydrophobicity patterns. Protein Pept Lett 17:559–567

    CAS  PubMed  Google Scholar 

  • Guo ZM (2002) Prediction of membrane protein types by using pattern recognition method based on pseudo amino acid composition. Master Thesis, Bio-X Life Science Research Center, Shanghai Jiaotong University

  • Guo J, Rao N, Liu G, Yang Y, Wang G (2011) Predicting protein folding rates using the concept of Chou’s pseudo amino acid composition. J Comput Chem 32:1612–1617

    CAS  PubMed  Google Scholar 

  • Guo SH, Deng EZ, Xu LQ, Ding H, Lin H, Chen W, Chou KC (2014) iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 30:1522–1529

    CAS  PubMed  Google Scholar 

  • Gupta MK, Niyogi R, Misra M (2013) An alignment-free method to find similarity among protein sequences via the general form of Chou’s pseudo amino acid composition. SAR QSAR Environ Res 24:597–609

    CAS  PubMed  Google Scholar 

  • Hajisharifi Z, Piryaiee M, Mohammad Beigi M, Behbahani M, Mohabatkar H (2014) Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via Ames test. J Theor Biol 341:34–40

    CAS  PubMed  Google Scholar 

  • Han GS, Yu ZG, Anh V (2014) A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou’s PseAAC. J Theor Biol 344:31–39

    CAS  PubMed  Google Scholar 

  • Hayat M, Iqbal N (2014) Discriminating protein structure classes by incorporating pseudo average chemical shift to Chou’s general PseAAC and support vector machine. Comput Methods Programs Biomed 116:184–192

    PubMed  Google Scholar 

  • Hayat M, Khan A (2011) Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. J Theor Biol 271:10–17

    CAS  PubMed  Google Scholar 

  • Hayat M, Khan A (2012) Discriminating outer membrane proteins with fuzzy K-nearest neighbor algorithms based on the general form of Chou’s PseAAC. Protein Pept Lett 19:411–421

    CAS  PubMed  Google Scholar 

  • Hu L, Huang T, Shi X, Lu WC, Cai YD, Chou KC (2011a) Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS ONE 6:e14556

    CAS  PubMed  PubMed Central  Google Scholar 

  • Hu L, Zheng L, Wang Z, Li B, Liu L (2011b) Using pseudo amino acid composition to predict protease families by incorporating a series of protein biological features. Protein Pept Lett 18:552–558

    CAS  PubMed  Google Scholar 

  • Huang C, Yuan J (2013a) Using radial basis function on the general form of Chou’s pseudo amino acid composition and PSSM to predict subcellular locations of proteins with both single and multiple sites. Biosystems 113:50–57

    CAS  PubMed  Google Scholar 

  • Huang C, Yuan JQ (2013b) A multilabel model based on Chou’s pseudo amino acid composition for identifying membrane proteins with both single and multiple functional types. J Membr Biol 246:327–334

    CAS  PubMed  Google Scholar 

  • Huang C, Yuan JQ (2013c) Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions. J Theor Biol 335:205–212

    CAS  PubMed  Google Scholar 

  • Huang C, Yuan JQ (2015) Simultaneously identify three different attributes of proteins by fusing their three different modes of Chou’s pseudo amino acid compositions. Protein Pept Lett 22:547–556

    CAS  PubMed  Google Scholar 

  • Huang Y, Yang L, Wang T (2011) Phylogenetic analysis of DNA sequences based on the generalized pseudo amino acid composition. J Theor Biol 269:217–223

    CAS  PubMed  Google Scholar 

  • Hussain W, Khan SD, Rasool N, Khan SA, Chou KC (2019a) 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 568:14–23

    CAS  PubMed  Google Scholar 

  • Hussain W, Khan YD, Rasool N, Khan SA, Chou KC (2019b) 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 468:1–11

    CAS  PubMed  Google Scholar 

  • Javed F, Hayat M (2018) Predicting subcellular localizations of multi-label proteins by incorporating the sequence features into Chou’s PseAAC. Genomics. https://doi.org/10.1016/j.ygeno.2018.09.004

    Article  PubMed  Google Scholar 

  • Jia C, Lin X, Wang Z (2014) 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 15:10410–10423

    CAS  PubMed  PubMed Central  Google Scholar 

  • Jia J, Liu Z, Xiao X, Chou KC (2015) iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J Theor Biol 377:47–56

    CAS  PubMed  Google Scholar 

  • Jia J, Liu Z, Xiao X, Liu B, Chou KC (2016a) 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 (JBSD) 34:1946–1961

    CAS  Google Scholar 

  • Jia J, Liu Z, Xiao X, Liu B, Chou KC (2016b) pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J Theor Biol 394:223–230

    CAS  PubMed  Google Scholar 

  • Jia J, Liu Z, Xiao X, Liu B, Chou KC (2016c) iCar-PseCp: identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget 7:34558–34570

    PubMed  PubMed Central  Google Scholar 

  • Jia J, Zhang L, Liu Z, Xiao X, Chou KC (2016d) pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics 32:3133–3141

    CAS  PubMed  Google Scholar 

  • Jia J, Liu Z, Xiao X, Liu B, Chou KC (2016e) iSuc-PseOpt: identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal Biochem 497:48–56

    CAS  PubMed  Google Scholar 

  • Jia J, Li X, Qiu W, Xiao X, Chou KC (2019) iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC. J Theor Biol 460:195–203

    CAS  PubMed  Google Scholar 

  • Jiang X, Wei R, Zhang TL, Gu Q (2008a) Using the concept of Chou’s pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein Pept Lett 15:392–396

    CAS  PubMed  Google Scholar 

  • Jiang X, Wei R, Zhao Y, Zhang T (2008b) Using Chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location. Amino Acids 34:669–675

    CAS  PubMed  Google Scholar 

  • Jiao YS, Du PF (2016) Prediction of Golgi-resident protein types using general form of Chou’s pseudo amino acid compositions: approaches with minimal redundancy maximal relevance feature selection. J Theor Biol 402:38–44

    CAS  PubMed  Google Scholar 

  • Jiao YS, Du PF (2017) Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou’s general pseudo-amino acid compositions. J Theor Biol 416:81–87

    CAS  PubMed  Google Scholar 

  • Jingbo X, Silan Z, Feng S, Huijuan X, Xuehai H, Xiaohui N, Zhi L (2011) Using the concept of pseudo amino acid composition to predict resistance gene against Xanthomonas oryzae pv. oryzae in rice: an approach from chaos games representation. J Theor Biol 284:16–23

    PubMed  Google Scholar 

  • Ju Z, He JJ (2017a) Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou’s PseAAC. J Mol Graph Model 76:356–363

    CAS  PubMed  Google Scholar 

  • Ju Z, He JJ (2017b) Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou’s general PseAAC. J Mol Graph Model 77:200–204

    CAS  PubMed  Google Scholar 

  • Ju Z, Wang SY (2018) Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition. Gene 664:78–83

    CAS  PubMed  Google Scholar 

  • Ju Z, Cao JZ, Gu H (2015) iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chous general PseAAC. J Theor Biol 385:50–57

    PubMed  Google Scholar 

  • Ju Z, Cao JZ, Gu H (2016) Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou’s general PseAAC. J Theor Biol 397:145–150

    CAS  PubMed  Google Scholar 

  • Kabir M, Hayat M (2016) iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol Genet Genomics 291:285–296

    CAS  PubMed  Google Scholar 

  • Kabir M, Ahmad S, Iqbal M, Hayat M (2019) iNR-2L: a two-level sequence-based predictor developed via Chou’s 5-steps rule and general PseAAC for identifying nuclear receptors and their families. Genomics. https://doi.org/10.1016/j.ygeno.2019.02.006

    Article  PubMed  Google Scholar 

  • Kandaswamy KK, Pugalenthi G, Moller S, Hartmann E, Kalies KU, Suganthan PN, Martinetz T (2010) Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition. Protein Pept Lett 17:1473–1479

    CAS  PubMed  Google Scholar 

  • Khan ZU, Hayat M, Khan MA (2015) Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model. J Theor Biol 365:197–203

    CAS  PubMed  Google Scholar 

  • Khan M, Hayat M, Khan SA, Iqbal N (2017) Unb-DPC: identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou’s general PseAAC. J Theor Biol 415:13–19

    CAS  PubMed  Google Scholar 

  • Khan YD, Rasool N, Hussain W, Khan SA, Chou KC (2018a) iPhosT-PseAAC: identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Anal Biochem 550:109–116

    CAS  PubMed  Google Scholar 

  • Khan YD, Rasool N, Hussain W, Khan SA, Chou KC (2018b) iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol Biol Rep 45:2501–2509

    CAS  PubMed  Google Scholar 

  • Khan YD, Jamil M, Hussain W, Rasool N, Khan SA, Chou KC (2019a) pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J Theor Biol 463:47–55

    CAS  PubMed  Google Scholar 

  • Khan YD, Batool A, Rasool N, Khan A, Chou KC (2019b) Prediction of nitrosocysteine sites using position and composition variant features. Lett Org Chem 16:283–293

    CAS  Google Scholar 

  • Khan S, Khan M, Iqbal N, Hussain T, Afzal S, Chou KC (2019c) A two-level computation model based on deep learning algorithm for identification of piRNA and their functions via Chou’s 5-steps rule. Int J Pept Res Ther (IJPRT). https://doi.org/10.1007/s10989-019-09887-3

    Article  Google Scholar 

  • Khosravian M, Faramarzi FK, Beigi MM, Behbahani M, Mohabatkar H (2013) Predicting antibacterial peptides by the concept of Chou’s pseudo amino acid composition and machine learning methods. Protein Pept Lett 20:180–186

    CAS  PubMed  Google Scholar 

  • Kikuchi T (1993) Discrimination of folding types of globular proteins based on average distance maps constructed from their sequences. J Protein Chem 12:515–523

    CAS  PubMed  Google Scholar 

  • Kong L, Zhang L, Lv J (2014) Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou’s pseudo amino acid composition. J Theor Biol 344:12–18

    CAS  PubMed  Google Scholar 

  • Krishnan MS (2018) Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. J Theor Biol 445:62–74

    Google Scholar 

  • Kumar R, Srivastava A, Kumari B, Kumar M (2015) Prediction of beta-lactamase and its class by Chou’s pseudo amino acid composition and support vector machine. J Theor Biol 365:96–103

    CAS  PubMed  Google Scholar 

  • Li FM, Li QZ (2008a) Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach. Amino Acids 34:119–125

    CAS  PubMed  Google Scholar 

  • Li FM, Li QZ (2008b) Predicting protein subcellular location using Chou’s pseudo amino acid composition and improved hybrid approach. Protein Pept Lett 15:612–616

    PubMed  Google Scholar 

  • Li TT, Chou KC, Forsen S (1980) The flow of substrate molecules in fast enzyme-catalyzed reaction systems. Chem Scr 16:192–196

    CAS  Google Scholar 

  • Li ZC, Zhou XB, Dai Z, Zou XY (2009) Prediction of protein structural classes by Chou’s pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis. Amino Acids 37:415–425

    PubMed  Google Scholar 

  • Li J, Wei DQ, Wang JF, Yu ZT, Chou KC (2012a) Molecular dynamics simulations of CYP2E1. Med Chem 8:208–221

    CAS  PubMed  Google Scholar 

  • Li LQ, Zhang Y, Zou LY, Zhou Y, Zheng XQ (2012b) prediction of protein subcellular multi-localization based on the general form of Chou’s pseudo amino acid composition. Protein Pept Lett 19:375–387

    CAS  PubMed  Google Scholar 

  • Li L, Yu S, Xiao W, Li Y, Li M, Huang L, Zheng X, Zhou S, Yang H (2014) Prediction of bacterial protein subcellular localization by incorporating various features into Chou’s PseAAC and a backward feature selection approach. Biochimie 104:100–107

    CAS  PubMed  Google Scholar 

  • Li JX, Wang SQ, Du QS, Wei H, Li XM, Meng JZ, Wang QY, Xie NZ, Huang RB, Chou KC (2018) Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from Bacillus deramificans. Curr Pharm Des 24:4023–4033

    CAS  PubMed  Google Scholar 

  • Li F, Zhang Y, Purcell AW, Webb GI, Chou KC, Lithgow T, Li C, Song J (2019) Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinformatics 20:112

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lian P, Wei DQ, Wang JF, Chou KC (2011) An allosteric mechanism inferred from molecular dynamics simulations on phospholamban pentamer in lipid membranes. PLoS ONE 6:e18587

    CAS  PubMed  PubMed Central  Google Scholar 

  • Liang Y, Zhang S (2017) Predict protein structural class by incorporating two different modes of evolutionary information into Chou’s general pseudo amino acid composition. J Mol Graph Model 78:110–117

    CAS  PubMed  Google Scholar 

  • Liang Y, Zhang S (2018) Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback–Leibler divergence. J Theor Biol 454:22–29

    CAS  PubMed  Google Scholar 

  • Liao QH, Gao QZ, Wei J, Chou KC (2011a) Docking and molecular dynamics study on the inhibitory activity of novel inhibitors on epidermal growth factor receptor (EGFR). Med Chem 7:24–31

    CAS  PubMed  Google Scholar 

  • Liao B, Jiang JB, Zeng QG, Zhu W (2011b) Predicting apoptosis protein subcellular location with PseAAC by incorporating tripeptide composition. Protein Pept Lett 18:1086–1092

    CAS  PubMed  Google Scholar 

  • Liao B, Xiang Q, Li D (2012) Incorporating Secondary features into the general form of Chou’s PseAAC for predicting protein structural class. Protein Pept Lett 19:1133–1138

    CAS  PubMed  Google Scholar 

  • Lin H (2008) The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition. J Theor Biol 252:350–356

    CAS  PubMed  Google Scholar 

  • Lin H, Ding H (2011) Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J Theor Biol 269:64–69

    CAS  PubMed  Google Scholar 

  • Lin H, Li QZ (2007a) Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. Biochem Biophys Res Commun 354:548–551

    CAS  PubMed  Google Scholar 

  • Lin H, Li QZ (2007b) Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components. J Comput Chem 28:1463–1466

    CAS  PubMed  Google Scholar 

  • Lin J, Wang Y (2011) Using a novel AdaBoost algorithm and Chou’s pseudo amino acid composition for predicting protein subcellular localization. Protein Pept Lett 18:1219–1225

    CAS  PubMed  Google Scholar 

  • Lin H, Ding H, Guo FB, Zhang AY, Huang J (2008) Predicting subcellular localization of mycobacterial proteins by using Chou’s pseudo amino acid composition. Protein Pept Lett 15:739–744

    CAS  PubMed  Google Scholar 

  • Lin H, Wang H, Ding H, Chen YL, Li QZ (2009) Prediction of subcellular localization of apoptosis protein using chou’s pseudo amino acid composition. Acta Biotheor 57:321–330

    PubMed  Google Scholar 

  • Lin J, Wang Y, Xu X (2011) A novel ensemble and composite approach for classifying proteins based on Chou’s pseudo amino acid composition. Afr J Biotech 10:16963–16968

    CAS  Google Scholar 

  • Lin WZ, Fang JA, Xiao X, Chou KC (2012) predicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system model. PLoS ONE 7:e49040

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lin H, Ding C, Yuan LF, Chen W, Ding H, Li ZQ, Guo FB, Hung J, Rao NN (2013) Predicting subchloroplast locations of proteins based on the general form of Chou’s pseudo amino acid composition: approached from optimal tripeptide composition. Int J Biomath 6:Article Number: 1350003

  • Lin H, Deng EZ, Ding H, Chen W, Chou KC (2014) iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res 42:12961–12972

    CAS  PubMed  PubMed Central  Google Scholar 

  • Liu W, Chou KC (1998) Prediction of protein structural classes by modified Mahalanobis discriminant algorithm. J Protein Chem 17:209–217

    CAS  PubMed  Google Scholar 

  • Liu H, Wang M, Chou KC (2005a) Low-frequency Fourier spectrum for predicting membrane protein types. Biochem Biophys Res Commun (BBRC) 336:737–739

    CAS  Google Scholar 

  • Liu H, Yang J, Wang M, Xue L, Chou KC (2005b) Using Fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types. Protein J 24:385–389

    CAS  PubMed  Google Scholar 

  • Liu T, Zheng X, Wang C, Wang J (2010) Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition: an approach from auto covariance transformation. Protein Pept Lett 17:1263–1269

    CAS  PubMed  Google Scholar 

  • Liu XL, Lu JL, Hu XH (2011) Predicting thermophilic proteins with pseudo amino acid composition: approached from chaos game representation and principal component analysis. Protein Pept Lett 18:1244–1250

    CAS  PubMed  Google Scholar 

  • Liu L, Hu XZ, Liu XX, Wang Y, Li SB (2012) Predicting protein fold types by the general form of Chou’s pseudo amino acid composition: approached from optimal feature extractions. Protein Pept Lett 19:439–449

    CAS  PubMed  Google Scholar 

  • Liu B, Wang X, Zou Q, Dong Q, Chen Q (2013) Protein remote homology detection by combining Chou’s pseudo amino acid composition and profile-based protein representation. Mol Inform 32:775–782

    CAS  PubMed  Google Scholar 

  • Liu B, Xu J, Lan X, Xu R, Zhou J, Wang X, Chou KC (2014) iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS ONE 9:e106691

    PubMed  PubMed Central  Google Scholar 

  • Liu B, Chen J, Wang X (2015a) Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis. Mol Genet Genomics 290:1919–1931

    CAS  PubMed  Google Scholar 

  • Liu B, Xu J, Fan S, Xu R, Zhou J, Wang X (2015b) PseDNA-Pro: DNA-binding protein identification by combining Chou’s PseAAC and physicochemical distance transformation. Mol Inform 34:8–17

    PubMed  Google Scholar 

  • Liu B, Liu F, Wang X, Chen J, Fang L, Chou KC (2015c) Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 43:W65–W71

    CAS  PubMed  PubMed Central  Google Scholar 

  • Liu B, Fang L, Wang S, Wang X, Li H, Chou KC (2015d) Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J Theor Biol 385:153–159

    CAS  PubMed  Google Scholar 

  • Liu Z, Xiao X, Qiu WR, Chou KC (2015e) iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem 474:69–77

    CAS  PubMed  Google Scholar 

  • Liu Z, Xiao X, Yu DJ, Jia J, Qiu WR, Chou KC (2016a) pRNAm-PC: predicting N-methyladenosine sites in RNA sequences via physical-chemical properties. Anal Biochem 497:60–67

    CAS  PubMed  Google Scholar 

  • Liu B, Fang L, Long R, Lan X, Chou KC (2016b) iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics 32:362–369

    CAS  PubMed  Google Scholar 

  • Liu LM, Xu Y, Chou KC (2017a) 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 13:552–559

    CAS  PubMed  Google Scholar 

  • Liu B, Wu H, Chou KC (2017b) Pse-in-One 2.0: an improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nat Sci 9:67–91

    CAS  Google Scholar 

  • Liu B, Weng F, Huang DS, Chou KC (2018a) iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC. Bioinformatics 34:3086–3093

    CAS  PubMed  Google Scholar 

  • Liu B, Yang F, Huang DS, Chou KC (2018b) iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics 34:33–40

    CAS  PubMed  Google Scholar 

  • Lu Y, Wang S, Wang J, Zhou G, Zhang Q, Zhou X, Niu B, Chen Q, Chou KC (2019) An Epidemic avian influenza prediction model based on Google Trends. Lett Org Chem 16:303–310

    CAS  Google Scholar 

  • Madkan A, Blank M, Elson E, Chou KC, Geddis MS, Goodman R (2009) Steps to the clinic with ELF EMF. Nat Sci 1:157–165

    CAS  Google Scholar 

  • Maggiora GM, Zhang CT, Chou KC, Elrod DW (1996) Combining fuzzy clustering and neural networks to predict protein structural classes. In: Devillers J (ed) Neural networks in QSAR and drug design. Academic Press, London, pp 255–279

    Google Scholar 

  • Mahdavi A, Jahandideh S (2011) Application of density similarities to predict membrane protein types based on pseudo amino acid composition. J Theor Biol 276:132–137

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mandal M, Mukhopadhyay A, Maulik U (2015) Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou’s PseAAC. Med Biol Eng Comput 53:331–344

    PubMed  Google Scholar 

  • Meher PK, Sahu TK, Saini V, Rao AR (2017) Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci Rep 7:42362

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mei S (2012a) Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization. J Theor Biol 293:121–130

    CAS  PubMed  Google Scholar 

  • Mei S (2012b) Predicting plant protein subcellular multi-localization by Chou’s PseAAC formulation based multi-label homolog knowledge transfer learning. J Theor Biol 310:80–87

    CAS  PubMed  Google Scholar 

  • Mei J, Zhao J (2018a) Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers. Sci Rep 8:2359

    PubMed  PubMed Central  Google Scholar 

  • Mei J, Zhao J (2018b) Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou’s general pseudo amino acid composition and motif features. J Theor Biol 427:147–153

    Google Scholar 

  • Mei J, Fu Y, Zhao J (2018) Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition. J Theor Biol 456:41–48

    CAS  PubMed  Google Scholar 

  • Mohabatkar H (2010) Prediction of cyclin proteins using Chou’s pseudo amino acid composition. Protein Pept Lett 17:1207–1214

    CAS  PubMed  Google Scholar 

  • Mohabatkar H, Mohammad Beigi M, Esmaeili A (2011) Prediction of GABA(A) receptor proteins using the concept of Chou’s pseudo amino acid composition and support vector machine. J Theor Biol 281:18–23

    CAS  PubMed  Google Scholar 

  • Mohabatkar H, Beigi MM, Abdolahi K, Mohsenzadeh S (2013) Prediction of allergenic proteins by means of the concept of Chou’s pseudo amino acid composition and a machine learning approach. Med Chem 9:133–137

    CAS  PubMed  Google Scholar 

  • Mohammad BM, Behjati M, Mohabatkar H (2011) Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach. J Struct Funct Genomics 12:191–197

    Google Scholar 

  • Mondal S, Pai PP (2014) Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction. J Theor Biol 356:30–35

    CAS  PubMed  Google Scholar 

  • Mondal S, Bhavna R, Mohan Babu R, Ramakumar S (2006) Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification. J Theor Biol 243:252–260

    CAS  PubMed  Google Scholar 

  • Mousavizadegan M, Mohabatkar H (2018) Computational prediction of antifungal peptides via Chou’s PseAAC and SVM. J Bioinform Comput Biol 16(4):1850016

    PubMed  Google Scholar 

  • Mundra P, Kumar M, Kumar KK, Jayaraman VK, Kulkarni BD (2007) Using pseudo amino acid composition to predict protein subnuclear localization: approached with PSSM. Pattern Recogn Lett 28:1610–1615

    Google Scholar 

  • Nanni L, Brahnam S, Lumini A (2010) High performance set of PseAAC and sequence based descriptors for protein classification. J Theor Biol 266:1–10

    CAS  PubMed  Google Scholar 

  • Nanni L, Brahnam S, Lumini A (2012a) Wavelet images and Chou’s pseudo amino acid composition for protein classification. Amino Acids 43:657–665

    CAS  PubMed  Google Scholar 

  • Nanni L, Lumini A, Gupta D, Garg A (2012b) Identifying bacterial virulent proteins by fusing a set of classifiers based on variants of Chou’s pseudo amino acid composition and on evolutionary information. IEEE-ACM Trans Comput Biol Bioinform 9:467–475

    Google Scholar 

  • Nanni L, Brahnam S, Lumini A (2014) Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition. J Theor Biol 360:109–116

    CAS  PubMed  Google Scholar 

  • Ning Q, Ma Z, Zhao X (2019) dForml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components. J Theor Biol 470:43–49

    CAS  PubMed  Google Scholar 

  • Niu XH, Li NN, Shi F, Hu XH, Xia JB, Xiong HJ (2010) Predicting protein solubility with a hybrid approach by pseudo amino acid composition. Protein Pept Lett 17:1466–1472

    CAS  Google Scholar 

  • Niu XH, Hu XH, Shi F, Xia JB (2012) predicting protein solubility by the general form of Chou’s pseudo amino acid composition: approached from chaos game representation and fractal dimension. Protein Pept Lett 19:940–948

    CAS  PubMed  Google Scholar 

  • Pacharawongsakda E, Theeramunkong T (2013) Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou’s PseAAC. IEEE Trans Nanobiosci 12:311–320

    Google Scholar 

  • Pan YX, Zhang ZZ, Guo ZM, Feng GY, Huang ZD, He L (2003) Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. J Protein Chem 22:395–402

    CAS  PubMed  Google Scholar 

  • Pan Y, Wang S, Zhang Q, Lu Q, Su D, Zuo Y, Yang L (2019) Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions. J Theor Biol 462:221–229

    CAS  PubMed  Google Scholar 

  • Qin YF, Wang CH, Yu XQ, Zhu J, Liu TG, Zheng XQ (2012) predicting protein structural class by incorporating patterns of over- represented k-mers into the general form of Chou’s PseAAC. Protein Pept Lett 19:388–397

    CAS  PubMed  Google Scholar 

  • Qin YF, Zheng L, Huang J (2013) Locating apoptosis proteins by incorporating the signal peptide cleavage sites into the general form of Chou’s pseudo amino acid composition. Int J Quantum Chem 113:1660–1667

    CAS  Google Scholar 

  • Qiu JD, Huang JH, Liang RP, Lu XQ (2009) Prediction of G-protein-coupled receptor classes based on the concept of Chou’s pseudo amino acid composition: an approach from discrete wavelet transform. Anal Biochem 390:68–73

    CAS  PubMed  Google Scholar 

  • Qiu JD, Huang JH, Shi SP, Liang RP (2010) Using the concept of Chou’s pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform. Protein Pept Lett 17:715–722

    CAS  PubMed  Google Scholar 

  • Qiu JD, Sun XY, Suo SB, Shi SP, Huang SY, Liang RP, Zhang L (2011a) Predicting homo-oligomers and hetero-oligomers by pseudo amino acid composition: an approach from discrete wavelet transformation. Biochimie 93:1132–1138

    CAS  PubMed  Google Scholar 

  • Qiu JD, Suo SB, Sun XY, Shi SP, Liang RP (2011b) OligoPred: a web-server for predicting homo-oligomeric proteins by incorporating discrete wavelet transform into Chou’s pseudo amino acid composition. J Mol Graph Model 30:129–134

    CAS  PubMed  Google Scholar 

  • Qiu WR, Xiao X, Chou KC (2014a) iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci (IJMS) 15:1746–1766

    Google Scholar 

  • Qiu WR, Xiao X, Lin WZ, Chou KC (2014b) iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach. Biomed Res Int (BMRI) 2014:947416

    Google Scholar 

  • Qiu WR, Xiao X, Lin WZ, Chou KC (2015) iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a grey system model. J Biomol Struct Dyn (JBSD) 33:1731–1742

    CAS  Google Scholar 

  • Qiu WR, Sun BQ, Xiao X, Xu ZC, Chou KC (2016a) iHyd-PseCp: identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget 7:44310–44321

    PubMed  PubMed Central  Google Scholar 

  • Qiu WR, Sun BQ, Xiao X, Xu ZC, Chou KC (2016b) iPTM-mLys: identifying multiple lysine PTM sites and their different types. Bioinformatics 32:3116–3123

    CAS  PubMed  Google Scholar 

  • Qiu WR, Xiao X, Xu ZC, Chou KC (2016c) iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget 7:51270–51283

    PubMed  PubMed Central  Google Scholar 

  • Qiu WR, Sun BQ, Xiao X, Xu D, Chou KC (2017a) iPhos-PseEvo: identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Mol Inform. https://doi.org/10.1002/minf.201600010

    Article  PubMed  Google Scholar 

  • Qiu WR, Zheng QS, Sun BQ, Xiao X (2017b) Multi-iPPseEvo: a multi-label classifier for identifying human phosphorylated proteins by incorporating evolutionary information into Chou’s general PseAAC via grey system theory. Mol Inform. https://doi.org/10.1002/minf.201600085

    Article  PubMed  Google Scholar 

  • Qiu WR, Jiang SY, Sun BQ, Xiao X, Cheng X, Chou KC (2017c) iRNA-2methyl: identify RNA 2′-O-methylation sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier. Med Chem 13:734–743

    CAS  PubMed  Google Scholar 

  • Qiu WR, Jiang SY, Xu ZC, Xiao X, Chou KC (2017d) iRNAm 5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget 8:41178–41188

    PubMed  PubMed Central  Google Scholar 

  • Qiu W, Li S, Cui X, Yu Z, Wang M, Du J, Peng Y, Yu B (2018a) Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition. J Theor Biol 450:86–103

    CAS  PubMed  Google Scholar 

  • Qiu WR, Sun BQ, Xiao X, Xu ZC, Jia JH, Chou KC (2018b) iKcr-PseEns: identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics 110:239–246

    CAS  PubMed  Google Scholar 

  • Rahimi M, Bakhtiarizadeh MR, Mohammadi-Sangcheshmeh A (2017) OOgenesis_Pred: a sequence-based method for predicting oogenesis proteins by six different modes of Chou’s pseudo amino acid composition. J Theor Biol 414:128–136

    CAS  PubMed  Google Scholar 

  • Rahman SM, Shatabda S, Saha S, Kaykobad M, Sohel Rahman M (2018) DPP-PseAAC: a DNA-binding protein prediction model using Chou’s general PseAAC. J Theor Biol 452:22–34

    CAS  PubMed  Google Scholar 

  • Ren LY, Zhang YS, Gutman I (2012) Predicting the classification of transcription factors by incorporating their binding site properties into a novel mode of Chou’s pseudo amino acid composition. Protein Pept Lett 19:1170–1176

    CAS  PubMed  Google Scholar 

  • Sabooh MF, Iqbal N, Khan M, Khan M, Maqbool HF (2018) Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC. J Theor Biol 452:1–9

    CAS  PubMed  Google Scholar 

  • Sahu SS, Panda G (2010) A novel feature representation method based on Chou’s pseudo amino acid composition for protein structural class prediction. Comput Biol Chem 34:320–327

    CAS  PubMed  Google Scholar 

  • Sanchez V, Peinado AM, Perez-Cordoba JL, Gomez AM (2015) A new signal characterization and signal-based Chou’s PseAAC representation of protein sequences. J Bioinform Comput Biol 13:1550024

    CAS  PubMed  Google Scholar 

  • Sankari ES, Manimegalai DD (2018) Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC. J Theor Biol 455:319–328

    CAS  PubMed  Google Scholar 

  • Sarangi AN, Lohani M, Aggarwal R (2013) Prediction of essential proteins in prokaryotes by incorporating various physico-chemical features into the general form of Chou’s pseudo amino acid composition. Protein Pept Lett 20:781–795

    CAS  PubMed  Google Scholar 

  • Sharma R, Dehzangi A, Lyons J, Paliwal K, Tsunoda T, Sharma A (2015) Predict Gram-positive and Gram-negative subcellular localization via incorporating evolutionary information and physicochemical features into Chou’s general PseAAC. IEEE Trans Nanobiosci 14:915–926

    Google Scholar 

  • Shen HB, Chou KC (2005a) Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo amino acid composition to predict membrane protein types. Biochem Biophys Res Commun (BBRC) 334:288–292

    CAS  Google Scholar 

  • Shen HB, Chou KC (2005b) Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem Biophys Res Comm. (BBRC) 337:752–756

    CAS  Google Scholar 

  • Shen HB, Chou KC (2008) PseAAC: a flexible web-server for generating various kinds of protein pseudo amino acid composition. Anal Biochem 373:386–388

    CAS  PubMed  Google Scholar 

  • Shen HB, Yang J, Liu XJ, Chou KC (2005) Using supervised fuzzy clustering to predict protein structural classes. Biochem Biophys Res Commun (BBRC) 334:577–581

    CAS  Google Scholar 

  • Shen HB, Yang J, Chou KC (2006) Fuzzy KNN for predicting membrane protein types from pseudo amino acid composition. J Theor Biol 240:9–13

    CAS  PubMed  Google Scholar 

  • Shen HB, Song JN, Chou KC (2009) Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J Biomed Sci Eng (JBiSE) 2:136–143

    CAS  Google Scholar 

  • Shen Y, Tang J, Guo F (2019) Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC. J Theor Biol 462:230–239

    CAS  PubMed  Google Scholar 

  • Shi R, Xu C (2011) Prediction of rat protein subcellular localization with pseudo amino acid composition based on multiple sequential features. Protein Pept Lett 18:625–633

    CAS  PubMed  Google Scholar 

  • Shi JY, Zhang SW, Pan Q, Cheng Y-M, Xie J (2007) Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition. Amino Acids 33:69–74

    CAS  PubMed  Google Scholar 

  • Shi JY, Zhang SW, Pan Q, Zhou GP (2008) Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution. Amino Acids 35:321–327

    CAS  PubMed  Google Scholar 

  • Shu M, Cheng X, Zhang Y, Wang Y, Lin Y, Wang L, Lin Z (2011) Predicting the activity of ACE inhibitory peptides with a novel mode of pseudo amino acid composition. Protein Pept Lett 18:1233–1243

    CAS  PubMed  Google Scholar 

  • Shyamili VK, Vellaichamy A (2019) Sequence and structure-based characterization of human and yeast ubiquitination sites by using Chou’s sample formulation. Proteins. https://doi.org/10.1002/prot.25689

    Article  Google Scholar 

  • Su ZD, Huang Y, Zhang ZY, Zhao YW, Wang D, Chen W, Chou KC, Lin H (2018) iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics 34:4196–4204

    CAS  PubMed  Google Scholar 

  • Sun XY, Shi SP, Qiu JD, Suo SB, Huang SY, Liang RP (2012) Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou’s PseAAC via discrete wavelet transform. Mol BioSyst 8:3178–3184

    CAS  PubMed  Google Scholar 

  • Tahir M, Hayat M (2016) iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou’s PseAAC. Mol BioSyst 12:2587–2593

    CAS  PubMed  Google Scholar 

  • Tahir M, Hayat M, Khan SA (2019a) 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 294:199–210

    CAS  PubMed  Google Scholar 

  • Tahir M, Tayara H, Chong KT (2019b) iRNA-PseKNC(2methyl): identify RNA 2′-O-methylation sites by convolution neural network and Chou’s pseudo components. J Theor Biol 465:1–6

    CAS  PubMed  Google Scholar 

  • Tang H, Chen W, Lin H (2016) Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique. Mol BioSyst 12:1269–1275

    CAS  PubMed  Google Scholar 

  • Tiwari AK (2016) Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into Chou’s general PseAAC. Comput Methods Programs Biomed 134:197–213

    PubMed  Google Scholar 

  • Tripathi P, Pandey PN (2017) A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou’s pseudo amino acid composition. J Theor Biol 424:49–54

    CAS  PubMed  Google Scholar 

  • Wan S, Mak MW, Kung SY (2013) GOASVM: a subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou’s pseudo amino acid composition. J Theor Biol 323:40–48

    CAS  PubMed  Google Scholar 

  • Wang JF, Chou KC (2009) Insight into the molecular switch mechanism of human Rab5a from molecular dynamics simulations. Biochem Biophys Res Commun (BBRC) 390:608–612

    CAS  Google Scholar 

  • Wang JF, Chou KC (2012) Recent advances in computational studies on influenza a virus m2 proton channel. Mini Rev Med Chem 12:971–978

    CAS  PubMed  Google Scholar 

  • Wang M, Yang J, Liu GP, Xu ZJ, Chou KC (2004) Weighted-support vector machines for predicting membrane protein types based on pseudo amino acid composition. Protein Eng Des Sel 17:509–516

    CAS  PubMed  Google Scholar 

  • Wang SQ, Yang J, Chou KC (2006) Using stacked generalization to predict membrane protein types based on pseudo amino acid composition. J Theor Biol 242:941–946

    CAS  PubMed  Google Scholar 

  • Wang YC, Wang XB, Yang ZX, Deng NY (2010) Prediction of enzyme subfamily class via pseudo amino acid composition by incorporating the conjoint triad feature. Protein Pept Lett 17:1441–1449

    PubMed  Google Scholar 

  • Wang D, Yang L, Fu Z, Xia J (2011a) Prediction of thermophilic protein with pseudo amino acid composition: an approach from combined feature selection and reduction. Protein Pept Lett 18:684–689

    CAS  PubMed  Google Scholar 

  • Wang W, Geng XB, Dou Y, Liu T, Zheng X (2011b) Predicting protein subcellular localization by pseudo amino acid composition with a segment-weighted and features-combined approach. Protein Pept Lett 18:480–487

    CAS  PubMed  Google Scholar 

  • Wang J, Li Y, Wang Q, You X, Man J, Wang C, Gao X (2012) ProClusEnsem: predicting membrane protein types by fusing different modes of pseudo amino acid composition. Comput Biol Med 42:564–574

    CAS  PubMed  Google Scholar 

  • Wang X, Li GZ, Lu WC (2013) Virus-ECC-mPLoc: a multi-label predictor for predicting the subcellular localization of virus proteins with both single and multiple sites based on a general form of Chou’s pseudo amino acid composition. Protein Pept Lett 20:309–317

    CAS  PubMed  Google Scholar 

  • Wang X, Zhang W, Zhang Q, Li GZ (2015) MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou’s pseudo amino acid composition and a novel multi-label classifier. Bioinformatics 31:2639–2645

    CAS  PubMed  Google Scholar 

  • Wang L, Zhang R, Mu Y (2019) Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC. J Theor Biol 461:51–58

    CAS  PubMed  Google Scholar 

  • Wu J, Li ML, Yu LZ, Wang C (2010) An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo amino acid composition. Protein J 29:62–67

    PubMed  Google Scholar 

  • Xiao X, Chou KC (2011) Using pseudo amino acid composition to predict protein attributes via cellular automata and other approaches. Curr Bioinform 6:251–260

    CAS  Google Scholar 

  • Xiao X, Shao SH, Ding YS, Huang ZD, Chou KC (2006a) Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids 30:49–54

    CAS  PubMed  Google Scholar 

  • Xiao X, Shao SH, Huang ZD, Chou KC (2006b) Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. J Comput Chem 27:478–482

    PubMed  Google Scholar 

  • Xiao X, Lin WZ, Chou KC (2008a) Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes. J Comput Chem 29:2018–2024

    CAS  PubMed  Google Scholar 

  • Xiao X, Wang P, Chou KC (2008b) Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. J Theor Biol 254:691–696

    CAS  PubMed  Google Scholar 

  • Xiao X, Wang P, Chou KC (2009) Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition. J Appl Crystallogr 42:169–173

    CAS  Google Scholar 

  • Xiao X, Wang P, Chou KC (2011) GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Mol BioSyst 7:911–919

    CAS  PubMed  Google Scholar 

  • Xiao X, Min JL, Wang P, Chou KC (2013) iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. J Theor Biol 337C:71–79

    Google Scholar 

  • Xiao X, Min JL, Lin WZ, Liu Z, Cheng X, Chou KC (2015) 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) 33:2221–2233

    CAS  Google Scholar 

  • Xiao X, Ye HX, Liu Z, Jia JH, Chou KC (2016) iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget 7:34180–34189

    PubMed  PubMed Central  Google Scholar 

  • Xiao X, Cheng X, Su S, Nao Q, Chou KC (2017) pLoc-mGpos: incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat Sci 9:331–349

    Google Scholar 

  • Xiao X, Xu ZC, Qiu WR, Wang P, Ge HT, Chou KC (2018a) iPSW(2L)-PseKNC: a two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition. Genomics. https://doi.org/10.1016/j.ygeno.2018.12.001

    Article  PubMed  PubMed Central  Google Scholar 

  • Xiao X, Cheng X, Chen G, Mao Q, Chou KC (2018b) pLoc_bal-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics. https://doi.org/10.1016/j.ygeno.2018.05.017

    Article  PubMed  PubMed Central  Google Scholar 

  • Xiao X, Cheng X, Chen G, Mao Q, Chou KC (2018c) pLoc_bal-mVirus: predict subcellular localization of multi-label virus proteins by PseAAC and IHTS treatment to balance training dataset. Med Chem 15:496–509

    Google Scholar 

  • Xiaohui N, Nana L, Jingbo X, Dingyan C, Yuehua P, Yang X, Weiquan W, Dongming W, Zengzhen W (2013) Using the concept of Chou’s pseudo amino acid composition to predict protein solubility: an approach with entropies in information theory. J Theor Biol 332:211–217

    PubMed  Google Scholar 

  • Xie HL, Fu L, Nie XD (2013) Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou’s PseAAC. Protein Eng Des Sel 26:735–742

    CAS  PubMed  Google Scholar 

  • Xu Y, Chou KC (2016) Recent progress in predicting posttranslational modification sites in proteins. Curr Top Med Chem 16:591–603

    CAS  PubMed  Google Scholar 

  • Xu Y, Ding J, Wu LY, Chou KC (2013a) iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS ONE 8:e55844

    CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Y, Shao XJ, Wu LY, Deng NY, Chou KC (2013b) iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 1:e171

    PubMed  PubMed Central  Google Scholar 

  • Xu Y, Wen X, Shao XJ, Deng NY, Chou KC (2014a) iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int J Mol Sci (IJMS) 15:7594–7610

    CAS  Google Scholar 

  • Xu Y, Wen X, Wen LS, Wu LY, Deng NY, Chou KC (2014b) iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS ONE 9:e105018

    PubMed  PubMed Central  Google Scholar 

  • Xu R, Zhou J, Liu B, He YA, Zou Q, Wang X, Chou KC (2015) Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach. J Biomol Struct Dyn (JBSD) 33:1720–1730

    CAS  Google Scholar 

  • Xu C, Sun D, Liu S, Zhang Y (2016) Protein sequence analysis by incorporating modified chaos game and physicochemical properties into Chou’s general pseudo amino acid composition. J Theor Biol 406:105–115

    CAS  PubMed  Google Scholar 

  • Xu C, Ge L, Zhang Y, Dehmer M, Gutman I (2017a) Prediction of therapeutic peptides by incorporating q-Wiener index into Chou’s general PseAAC. J Biomed Inform. https://doi.org/10.1016/j.jbi.2017.09.011

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu Y, Li C, Chou KC (2017b) iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC. Med Chem 13:544–551

    CAS  PubMed  Google Scholar 

  • Yang H, Qiu WR, Liu G, Guo FB, Chen W, Chou KC, Lin H (2018) iRSpot-Pse6NC: identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int J Biol Sci 14:883–891

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yu L, Guo Y, Li Y, Li G, Li M, Luo J, Xiong W, Qin W (2010) SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo amino acid composition. J Theor Biol 267:1–6

    CAS  PubMed  Google Scholar 

  • Yu X, Zheng X, Liu T, Dou Y, Wang J (2012) Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation. Amino Acids 42:1619–1625

    CAS  PubMed  Google Scholar 

  • Yu B, Li S, Qiu WY, Chen C, Chen RX, Wang L, Wang MH, Zhang Y (2017a) Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising. Oncotarget 8:107640–107665

    PubMed  PubMed Central  Google Scholar 

  • Yu B, Lou L, Li S, Zhang Y, Qiu W, Wu X, Wang M, Tian B (2017b) Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising. J Mol Graph Model 76:260–273

    CAS  PubMed  Google Scholar 

  • Zeng YH, Guo YZ, Xiao RQ, Yang L, Yu LZ, Li ML (2009) Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J Theor Biol 259:366–372

    CAS  PubMed  Google Scholar 

  • Zhang SL (2015) Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou’s general PseAAC. Chemom Intell Lab Syst (CHEMOLAB) 142:28–35

    CAS  Google Scholar 

  • Zhang CT, Chou KC (1992a) An optimization approach to predicting protein structural class from amino acid composition. Protein Sci 1:401–408

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang CT, Chou KC (1992b) Monte Carlo simulation studies on the prediction of protein folding types from amino acid composition. Biophys J 63:1523–1529

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang CT, Chou KC (1995a) Monte Carlo simulation studies on the prediction of protein folding types from amino acid composition. II. Correlative effect. J Protein Chem 14:251–258

    CAS  PubMed  Google Scholar 

  • Zhang CT, Chou KC (1995b) An eigenvalue-eigenvector approach to predicting protein folding types. J Protein Chem 14:309–326

    CAS  PubMed  Google Scholar 

  • Zhang CT, Chou KC (1995c) An analysis of protein folding type prediction by seed-propagated sampling and jackknife test. J Protein Chem 14:583–593

    CAS  PubMed  Google Scholar 

  • Zhang TL, Ding YS (2007) Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes. Amino Acids 33:623–629

    CAS  PubMed  Google Scholar 

  • Zhang S, Duan X (2018) Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC. J Theor Biol 437:239–250

    CAS  PubMed  Google Scholar 

  • Zhang GY, Fang BS (2008) Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou’s amphiphilic pseudo amino acid composition. J Theor Biol 253:310–315

    CAS  PubMed  Google Scholar 

  • Zhang S, Liang Y (2018) Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC. J Theor Biol 457:163–169

    CAS  PubMed  Google Scholar 

  • Zhang CT, Chou KC, Maggiora GM (1995) Predicting protein structural classes from amino acid composition: application of fuzzy clustering. Protein Eng 8:425–435

    CAS  PubMed  Google Scholar 

  • Zhang SW, Pan Q, Zhang HC, Shao ZC, Shi JY (2006) Prediction protein homo-oligomer types by pseudo amino acid composition: approached with an improved feature extraction and naive Bayes feature fusion. Amino Acids 30:461–468

    CAS  PubMed  Google Scholar 

  • Zhang GY, Li HC, Gao JQ, Fang BS (2008a) Predicting lipase types by improved Chou’s pseudo amino acid composition. Protein Pept Lett 15:1132–1137

    CAS  PubMed  Google Scholar 

  • Zhang SW, Chen W, Yang F, Pan Q (2008b) Using Chou’s pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach. Amino Acids 35:591–598

    PubMed  Google Scholar 

  • Zhang SW, Zhang YL, Yang HF, Zhao CH, Pan Q (2008c) Using the concept of Chou’s pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies. Amino Acids 34:565–572

    CAS  PubMed  Google Scholar 

  • Zhang TL, Ding YS, Chou KC (2008d) Prediction protein structural classes with pseudo amino acid composition: approximate entropy and hydrophobicity pattern. J Theor Biol 250:186–193

    CAS  PubMed  Google Scholar 

  • Zhang T, Wei DQ, Chou KC (2012) A pharmacophore model specific to active site of CYP1A2 with a novel molecular modeling explorer and CoMFA. Med Chem 8:198–207

    CAS  PubMed  Google Scholar 

  • Zhang J, Sun P, Zhao X, Ma Z (2014a) PECM: prediction of extracellular matrix proteins using the concept of Chou’s pseudo amino acid composition. J Theor Biol 363:412–418

    CAS  PubMed  Google Scholar 

  • Zhang J, Zhao X, Sun P, Ma Z (2014b) PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou’s PseAAC. Int J Mol Sci 15:11204–11219

    PubMed  PubMed Central  Google Scholar 

  • Zhang L, Zhao X, Kong L (2014c) Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou’s pseudo amino acid composition. J Theor Biol 355:105–110

    CAS  PubMed  Google Scholar 

  • Zhang M, Zhao B, Liu X (2015) Predicting industrial polymer melt index via incorporating chaotic characters into Chou’s general PseAAC. Chemom Intell Lab Syst (CHEMOLAB) 146:232–240

    CAS  Google Scholar 

  • Zhang Y, Xie R, Wang J, Leier A, Marquez-Lago TT, Akutsu T, Webb GI, Chou KC, Song J (2018) Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Brief Bioinform. https://doi.org/10.1093/bib/bby079

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhao XW, Ma ZQ, Yin MH (2012) Predicting protein–protein interactions by combing various sequence- derived features into the general form of Chou’s pseudo amino acid composition. Protein Pept Lett 19:492–500

    CAS  PubMed  Google Scholar 

  • Zhao W, Li GP, Wang J, Zhou YK, Gao Y, Du PF (2019) Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions. J Theor Biol 473:38–43

    CAS  PubMed  Google Scholar 

  • Zhou GP (2011) The disposition of the LZCC protein residues in Wenxiang diagram provides new insights into the protein–protein interaction mechanism. J Theor Biol 284:142–148

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou GP, Cai YD (2006) Predicting protease types by hybridizing gene ontology and pseudo amino acid composition. Proteins 63:681–684

    CAS  PubMed  Google Scholar 

  • Zhou GP, Deng MH (1984) An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J 222:169–176

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou XB, Chen C, Li ZC, Zou XY (2007) Using Chou’s amphiphilic pseudo amino acid composition and support vector machine for prediction of enzyme subfamily classes. J Theor Biol 248:546–551

    CAS  PubMed  Google Scholar 

  • Zhu PP, Li WC, Zhong ZJ, Deng EZ, Ding H, Chen W, Lin H (2015) Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition. Mol BioSyst 11:558–563

    CAS  PubMed  Google Scholar 

  • Zia-ur-Rehman AK (2012) Identifying GPCRs and their types with Chou’s pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix. Protein Pept Lett 19:890–903

    CAS  PubMed  Google Scholar 

  • Zia-Ur-Rehman R, Khan A (2011) Prediction of GPCRs with pseudo amino acid composition: employing composite features and grey incidence degree based classification. Protein Pept Lett 18:872–878

    CAS  PubMed  Google Scholar 

  • Zou HL, Xiao X (2016a) Predicting the functional types of singleplex and multiplex eukaryotic membrane proteins via different models of Chou’s pseudo amino acid compositions. J Membr Biol 249:23–29

    CAS  PubMed  Google Scholar 

  • Zou HL, Xiao X (2016b) Classifying multifunctional enzymes by incorporating three different models into Chou’s general pseudo amino acid composition. J Membr Biol 249:561–567. https://doi.org/10.1007/s00232-016-9904-3

    Article  CAS  Google Scholar 

  • Zou D, He Z, He J, Xia Y (2011) Supersecondary structure prediction using Chou’s pseudo amino acid composition. J Comput Chem 32:271–278

    CAS  PubMed  Google Scholar 

  • Zuo YC, Peng Y, Liu L, Chen W, Yang L, Fan GL (2014) Predicting peroxidase subcellular location by hybridizing different descriptors of Chou’s pseudo amino acid patterns. Anal Biochem 458:14–19

    CAS  PubMed  Google Scholar 

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Chou, KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 26, 1085–1098 (2020). https://doi.org/10.1007/s10989-019-09910-7

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