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

An algorithm for prediction of structural elements in small proteins

A Kolinski1, J Skolnick, A Godzik

  • 1Scripps Research Institute, Department of Molecular Biology, La Jolla, California 92037, USA. kolinski@chem.uw.edu.pl

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 1, 1996
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

Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.

Nature communications·2026
Same author

Combining structural modeling and deep learning to calculate the <i>E. coli</i> protein interactome and functional networks.

bioRxiv : the preprint server for biology·2025
Same author

Oligomerization of FVFLM peptides and their ability to inhibit beta amyloid peptides aggregation: consideration as a possible model.

Physical chemistry chemical physics : PCCP·2017
Same author

Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure.

Journal of molecular modeling·2013
Same author

Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction.

Journal of molecular modeling·2012
Same author

Structure-based classification of 45 FK506-binding proteins.

Proteins·2008
Same journal

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

The Evolving Cyberinfrastructure at the National Institutes of Health to Support Data and AI in Biomedical Research.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Applications of AI & ML in Biomanufacturing of Cell and Gene Therapies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

AI for Health: Leveraging Artificial Intelligence to Revolutionize Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
See all related articles

A new method accurately predicts surface loops and secondary structures in protein linkers. This computational approach aids in understanding protein folding and tertiary structure prediction.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Structure Prediction

Background:

  • Accurate prediction of protein secondary structure is crucial for understanding protein function.
  • Identifying surface loops and transglobular linkers presents a challenge in protein structure prediction.

Purpose of the Study:

  • To develop and validate a novel method for predicting surface loops/turns and secondary structures within transglobular linkers.
  • To assess the accuracy of this method for small, single-domain globular proteins.

Main Methods:

  • Development of a predictive method focusing on transglobular linkers.
  • Application and testing of the method on a dataset of 10 proteins with known structures.

Main Results:

Related Experiment Videos

  • The method demonstrates high accuracy in predicting loop and linker secondary structures.
  • Secondary structure assignment in transglobular connections is correct in over 85% of cases.
  • Loop prediction accuracy shows a similar error rate.

Conclusions:

  • The developed method provides accurate predictions for surface loops and transglobular linker secondary structures.
  • This approach offers complementary information to existing secondary structure prediction methods.
  • The tool is potentially valuable for early-stage tertiary structure prediction and determining protein structural class and folding topologies.