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

Some insights into protein structural class prediction.

G P Zhou1, N Assa-Munt

  • 1Department of Structural Biology, Burnham Institute, La Jolla, California, USA. gpzhou@burnham-inst.org

Proteins
|May 17, 2001
PubMed
Summary

This study clarifies relationships between protein structural prediction algorithms. It proves the Bayes decision rule is identical to the component-coupled algorithm, and the least Mahalanobis distance algorithm is an approximation.

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

[Progress in basic research and clinical application of hepatocyte transplantation].

Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology·2023
Same author

[A case report of TAFRO syndrome].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences·2021
Same author

Impact of depression mood disorder on the adverse drug reaction incidence rate of anticancer drugs in cancer patients.

The Journal of international medical research·2011
Same author

Support vector machines for predicting protein structural class.

BMC bioinformatics·2001
Same author

Modification of erythrocyte hydration in the treatment of sickle cell disease.

Blood cells, molecules & diseases·2001
Same author

Solution structures and integrin binding activities of an RGD peptide with two isomers.

Biochemistry·2001

Area of Science:

  • Computational biology
  • Protein structure prediction

Background:

  • Predicting protein structural class benefits from algorithms considering amino acid coupling effects.
  • Confusion exists regarding relationships between advanced algorithms like least Mahalanobis distance, component-coupled, and Bayes decision rule.

Purpose of the Study:

  • To rigorously derive and clarify the relationships between key algorithms used in protein structural class prediction.
  • To resolve confusion surrounding the component-coupled algorithm, Bayes decision rule, and least Mahalanobis distance algorithm.

Main Methods:

  • Mathematical derivation to establish algorithmic equivalencies and approximations.
  • Comparative analysis of the Bayes decision rule and component-coupled algorithm.
  • Examination of the least Mahalanobis distance algorithm as an approximation.

Related Experiment Videos

Main Results:

  • The Bayes decision rule is mathematically proven to be identical to the component-coupled algorithm.
  • The least Mahalanobis distance algorithm is demonstrated to be an approximation of the component-coupled algorithm.
  • The component-coupled algorithm is also identified as the covariant-discriminant algorithm.

Conclusions:

  • Clarifying these algorithmic relationships enhances effective use and interpretation of protein prediction results.
  • This understanding facilitates advancements in protein structural prediction and broader protein science.
  • Accurate interpretation of algorithms like the Bayes decision rule and component-coupled algorithm is crucial.