Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A correlation-coefficient method to predicting protein-structural classes from amino acid compositions.

K C Chou1, C T Zhang

  • 1Computational Chemistry, Upjohn Research Laboratories, Kalamazoo.

European Journal of Biochemistry
|July 15, 1992
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[An analysis of clinical and genetic feature in pulmonary artery sarcoma].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases·2025
Same author

[The preliminary application of mNGS in the diagnosis of invasive fungal sinusitis].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2024
Same author

[Mechanical circulatory support combined with immunomodulation treatment for patients with fulminant myocarditis: a single-center real-world study].

Zhonghua xin xue guan bing za zhi·2022
Same author

Overexpression of miR-101-2 in donor cells improves the early development of Holstein cow somatic cell nuclear transfer embryos.

Journal of dairy science·2019
Same author

Protective effects of trehalose on frozen-thawed ovarian granulosa cells of cattle.

Animal reproduction science·2018
Same author

[Molecular detection and genotyping of human papillomavirus].

Zhonghua zhong liu za zhi [Chinese journal of oncology]·2018
Same journal

Comparison of expression patterns and cell adhesion properties of the mouse biliary glycoproteins Bgp1 and Bgp2.

European journal of biochemistry·2020
Same journal

AB 3.1.1.1 (or EC 3.1.1.?).

European journal of biochemistry·2020
Same journal

Cdk5.

European journal of biochemistry·2018
Same journal

Structure of the core oligosaccharide of a rough-type lipopolysaccharide of Pseudomonas syringae pv. phaseolicola.

European journal of biochemistry·2004
Same journal

Monitoring ligand-mediated nuclear receptor-coregulator interactions by noncovalent mass spectrometry.

European journal of biochemistry·2004
Same journal

Solution structure of long neurotoxin NTX-1 from the venom of Naja naja oxiana by 2D-NMR spectroscopy.

European journal of biochemistry·2004
See all related articles

A new method uses amino acid composition to predict protein structural classes (all alpha, all beta, alpha/beta, or alpha+beta). This approach significantly improves prediction accuracy compared to existing techniques.

Area of Science:

  • Biochemistry
  • Structural Biology
  • Bioinformatics

Background:

  • Proteins are classified into four main structural classes: all alpha, all beta, (alpha + beta), and alpha/beta.
  • Accurate prediction of these structural classes is crucial for understanding protein function and evolution.

Purpose of the Study:

  • To propose a novel formulation for predicting protein structural class based on amino acid composition.
  • To evaluate the effectiveness and generalizability of the proposed prediction method.

Main Methods:

  • Utilized the maximum correlation-coefficient principle for developing the prediction formulation.
  • Employed a development set to derive class-specific amino acid compositions.
  • Validated the method on an independent set of proteins to assess extrapolating effectiveness.

Related Experiment Videos

Main Results:

  • The new method demonstrated a considerably higher rate of correct prediction compared to previous methods.
  • Self-consistency tests on the development set confirmed the method's robustness.
  • Independent set validation indicated strong extrapolating effectiveness.

Conclusions:

  • The maximum correlation-coefficient principle offers a significant improvement in protein structural class prediction.
  • The proposed method provides a more accurate and reliable approach for classifying protein structures based on amino acid composition.