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ISSN: 2638-6070

Scholarly Journal of Food and Nutrition

Short Communication(ISSN: 2638-6070)

Revisiting the paper on “Prediction of Tight Turns and their Types in Proteins”

Volume 3 - Issue 3

Kuo-Chen Chou*

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

Received: September 19, 2020;   Published: October 06, 2020

*Corresponding author: Kuo-Chen Chou, Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America

DOI: 10.32474/SJFN.2020.03.000164

 

Abstract PDF

Abstract

In this short report, the works on predicting the tight turns in proteins and their types are recalled.

Keywords: δ-Turn; γ-Turn; β-Turn; α-Turn; Π-Turn; Proteins

Short Communication

About 20 years ago a very important paper on prediction of tight turns and their types in proteins [1] was published. According to the definition given in that paper, a tight turn in protein structure is a site where [1] a polypeptide chain reverses its overall direction, i.e., leads the chain to fold back on itself by nearly 180°, and [2] the amino acid residues directly involved in forming the turn are no more than six. In the same paper, various types of tight turns and how to predict them have been systematically reviewed. Ever since then, this paper has played very important role for building protein three dimensional (3D) models for drug development [2-14].

References

  1. Chou KC (2000) Review: Prediction of tight turns and their types in proteins. Anal Biochem 286(1): 1-16.
  2. Chou KC, Howe WJ (2002) Prediction of the tertiary structure of the beta-secretase zymogen. Biochem Biophys Res Commun 292(3): 702-708.
  3. Chou KC (2004) Modelling extracellular domains of GABA-A receptors: subtypes 1, 2, 3, and 5. Biochemical and Biophysical Research Communications 316: 636-642.
  4. Chou KC (2004) Insights from modelling the 3D structure of the extracellular domain of a7 nicotinic acetylcholine receptor. Biochem Biophys Res Commun 319: 433-438.
  5. Chou KC (2004) Insights from modelling three-dimensional structures of the human potassium and sodium channels. Journal of Proteome Research 3(4): 856-861.
  6. Chou KC (2004) Insights from modelling the tertiary structure of BACE2. Journal of Proteome Research 3: 1069-1072.
  7. Chou KC (2004) Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor. Biochemical and Biophysical Research Communication 319(2): 433-438.
  8. Chou KC (2005) Modeling the tertiary structure of human cathepsin-E. Biochem Biophys Res Commun 331(1): 56-60.
  9. Wei DQ, Du QS, Sun H, Chou KC (2006) Insights from modeling the 3D structure of H5N1 influenza virus neuraminidase and its binding interactions with ligands. Biochem Biophys Res Comm 344:1048-1055.
  10. Wang JF, Wei DQ, Li L, Zheng SY, Li YX, et al. (2007) 3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design. Biochem Biophys Res Commun 355(2): 513-519.
  11. Wang JF, Wei DQ, Lin Y, Wang YH, Du HL, et al. (2007) Insights from modeling the 3D structure of NAD(P)H-dependent D-xylose reductase of Pichia stipitis and its binding interactions with NAD and NADP. Biochem Biophys Res Comm 359(2): 323-329.
  12. Wang SQ, Du QS, Chou KC (2007) Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases. Biochem Biophys Res Comm 354(3): 634-640.
  13. Shen HB, Yi DL, Yao LX, Yang J, Chou KC (2008) Knowledge-based computational intelligence development for predicting protein secondary structures from sequences. Expert Rev Proteomics 5(5): 653-662.
  14. Du QS, Wang SQ, Huang RB, Chou KC (2010) Computational 3D structures of drug-targeting proteins in the 2009-H1N1 influenza A virus. Chem Phys Lett 485: 191-195.
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