Acta Scientific Pharmaceutical Sciences (ASPS)(ISSN: 2581-5423)

Short Communication Volume 4 Issue 12

The Significant and Profound Impacts of Protein Subcellular Localization Prediction (Short Communication)

Kuo-Chen Chou*

Gordon Life Science Institute, Boston, Massachusetts, United States of America

*Corresponding Author: Kuo-Chen Chou, Gordon Life Science Institute, Boston, Massachusetts, United States of America.

Received: October 03, 2020; Published: November 07, 2020

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  The first paper introducing the protein subcellular location prediction was in 2005 [1]. It has stimulated a series of followed-up publications [2-10], particularly for those proteins with multiple location sites [11-18], as well as the eight master pieces of papers from the then Chairman of Nobel Prize Committee Sture Forsen [19-26].

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References

  1. HB Shen and KC Chou. “Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition”. Biochemical and Biophysical Research Communications 337 (2005): 752-756.
  2. KC Chou and HB Shen. “Predicting protein subcellular location by fusing multiple classifiers”. Journal of Cellular Biochemistry 99 (2006): 517-527.
  3. KC Chou and HB Shen. “Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers”. Journal of Proteome Research 5 (2006): 1888-1897.
  4. KC Chou and HB Shen. “Large-scale predictions of Gram-negative bacterial protein subcellular locations”. Journal of Proteome Research 5 (2006): 3420-3428.
  5. KC Chou and HB Shen. “Large-scale plant protein subcellular location prediction”. Journal of Cellular Biochemistry 100 (2007): 665-678.
  6. KC Chou and HB Shen. “Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites”. Journal of Proteome Research 6 (2007): 1728-1734.
  7. KC Chou and HB Shen. “Recent progresses in protein subcellular location prediction”. Annals of Biochemistry 370 (2007): 1-16.
  8. HB Shen and KC Chou. “Hum-mPLoc: An ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites”. Biochemical and Biophysical Research Communications 355 (2007): 1006-1011.
  9. HB Shen., et al. “Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction”. Amino Acids 33 (2007): 57-67.
  10. HB Shen and KC Chou. “Virus-mPLoc: A Fusion Classifier for Viral Protein Subcellular Location Prediction by Incorporating Multiple Sites”. Journal of Biomolecular Structure and Dynamics 28 (2010): 175-186.
  11. HB Shen., et al. “Using supervised fuzzy clustering to predict protein structural classes”. Biochemical and Biophysical Research Communications 334 (2005): 577-581.
  12. KC Chou and HB Shen. “Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization”. Biochemical and Biophysical Research Communications 347 (2006): 150-157.
  13. KC Chou and HB Shen. “Addendum to “Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization”. Biochemical and Biophysical Research Communications 348 (2006): 1479.
  14. KC Chou and HB Shen. “Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides”. Biochemical and Biophysical Research Communications 357 (2007): 633-640.
  15. KC Chou and HB Shen. “MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM”. Biochemical and Biophysical Research Communications 360 (2007): 339-345.
  16. HB Shen and KC Chou. “Signal-3L: a 3-layer approach for predicting signal peptide”. Biochemical and Biophysical Research Communications 363 (2007): 297-303.
  17. HB Shen and KC Chou. “EzyPred: A top-down approach for predicting enzyme functional classes and subclasses”. Biochemical and Biophysical Research Communications 364 (2007): 53-59.
  18. KC Chou and HB Shen. “ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information”. Biochemical and Biophysical Research Communications 376 (2008): 321-325.
  19. KC Chou and S Forsen. “Diffusion-controlled effects in reversible enzymatic fast reaction system: Critical spherical shell and proximity rate constants”. Biophysical Chemistry 12 (1980): 255-263.
  20. KC Chou and S Forsen. “Graphical rules for enzyme-catalyzed rate laws”. Biochemical Journal 187 (1980): 829-835.
  21. KC Chou., et al. “Three schematic rules for deriving apparent rate constants”. Chemica Scripta 16 (1980): 109-113.
  22. KC Chou., et al. “The critical spherical shell in enzymatic fast reaction systems”. Biophysical Chemistry 12 (1980): 265-269.
  23. TT Li., et al. “The flow of substrate molecules in fast enzyme-catalyzed reaction systems”. Chemica Scripta 16 (1980): 192-196.
  24. KC Chou., et al. “A new graphical method for deriving rate equations for complicated mechanisms”. Chemica Scripta 18 (1981): 82-86.
  25. KC Chou., et al. “The biological functions of low-frequency phonons: 2. Cooperative effects”. Chemica Scripta 18 (1981): 126-132.
  26. KC Chou and S Forsen. “Graphical rules of steady-state reaction systems”. Canadian Journal of Chemistry 59 (1981): 737-755.
  27. JZ Cao., et al. “Predicting Viral Protein Subcellular Localization with Chou's Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm”. Protein and Peptide Letters 19 (2012): 1163-1169.
  28. LQ Li., et al. “Prediction of Protein Subcellular Multi-Localization Based on the General form of Chou's Pseudo Amino Acid Composition”. Protein and Peptide Letters 19 (2012): 375-387.
  29. S Mei. “Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization”. Journal of Theoretical Biology 293 (2012): 121-130.
  30. S Mei. “Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning”. Journal of Theoretical Biology 310 (2012): 80-87.
  31. Zia-ur-Rehman and A Khan. “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 and Peptide Letters 19 (2012): 890-903.
  32. C Huang and J Yuan. “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 (2013): 50-57.
  33. C Huang and JQ Yuan. “A multilabel model based on Chou's pseudo amino acid composition for identifying membrane proteins with both single and multiple functional types”. The Journal of Membrane Biology 246 (2013): 327-334.
  34. C Huang and JQ Yuan. “Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions”. Journal of Theoretical Biology 335 (2013): 205-212.
  35. E Pacharawongsakda and T Theeramunkong. “Predict Subcellular Locations of Singleplex and Multiplex Proteins by Semi-Supervised Learning and Dimension-Reducing General Mode of Chou's PseAAC”. IEEE Transactions on Nanobioscience 12 (2013): 311-320.
  36. X Wang., et al. “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 and Peptide Letters 20 (2013): 309-317.
  37. M Mandal., et al. “Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou's PseAAC”. Medical and Biological Engineering and Computing 53 (2015): 331-344.
  38. X Wang., et al. “MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier”. Bioinformatics 31 (2015): 2639-2645.
  39. HL Zou and X Xiao. “Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou's Pseudo Amino Acid Compositions”. The Journal of Membrane Biology 249 (2016): 23-29.
  40. HL Zou and X Xiao. “Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition”. The Journal of Membrane Biology 249 (2016): 561-567.
  41. WR Qiu., et al. “Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory”. Molecular Informatics 36 (2017):: UNSP 1600085.
  42. J Ahmad., et al. “MFSC: Multi-voting based Feature Selection for Classification of Golgi Proteins by Adopting the General form of Chou's PseAAC components”. Journal of Theoretical Biology 463 (2018): 99-109.
  43. F Javed and M Hayat. “Predicting subcellular localizations of multi-label proteins by incorporating the sequence features into Chou's PseAAC”. Genomics 17 (2018): 793-821.
  44. J Ahmad and M Hayat. “MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components”. Journal of Theoretical Biology 463 (2019): 99-109.
  45. X Du., et al. “MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule”. Journal of Proteome Research 18 (2019): 3119-3132.
  46. L Du., et al. “Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou's 5-Steps Rule”. IEEE Access 8 (2020): 56452-56461.
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Citation

Citation: Kuo-Chen Chou. “The Significant and Profound Impacts of Protein Subcellular Localization Prediction (Short Communication)". Acta Scientific Pharmaceutical Sciences 4.12 (2020): 11-13.



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