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 Concept Videos

Globular and Fibrous Proteins02:21

Globular and Fibrous Proteins

Many proteins can be classified into two distinct subtypes - globular or fibrous. These two types differ in their shapes and solubilities.
Globular proteins are also known as spheroproteins and typically are approximately round in shape. They contain a mix of amino acid types and contain differing sequences in their primary structures. Globular proteins have many different functions, such as enzymes, cellular messengers, and molecular transporters. These roles often require the proteins to be...
Globular and Fibrous Proteins02:21

Globular and Fibrous Proteins

Many proteins can be classified into two distinct subtypes - globular or fibrous. These two types differ in their shapes and solubilities.
Globular proteins are also known as spheroproteins and typically are approximately round in shape. They contain a mix of amino acid types and contain differing sequences in their primary structures. Globular proteins have many different functions, such as enzymes, cellular messengers, and molecular transporters. These roles often require the proteins to be...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Conservation of Protein Domains02:26

Conservation of Protein Domains

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...

You might also read

Related Articles

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

Sort by
Same author

Improving hit discovery by integrating activity cliff sensitivity into active learning.

Bioinformatics (Oxford, England)·2026
Same author

Validation of a 2-Gene Blood Test for Kawasaki Disease in Febrile Children.

JAMA network open·2026
Same author

Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors.

NPJ precision oncology·2026
Same author

Effects of Maternal Tetramethyl Bisphenol F Exposure on Neurodevelopment and Behavior in Mouse Offspring.

International journal of molecular sciences·2026
Same author

Enacted practices and developmental experiences of senior medical student tutors in a structured peer tutoring program.

Korean journal of medical education·2026
Same author

EnsDTI: Predicting Drug-Target Interaction With Mixture-of-Experts and Confidence Assessment.

IEEE transactions on computational biology and bioinformatics·2026

Related Experiment Video

Updated: Jul 18, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

iGibbs: improving Gibbs motif sampler for proteins by sequence clustering and iterative pattern sampling.

Sun Kim1, Zhiping Wang, Mehmet Dalkilic

  • 1School of Informatics, Indiana University, Indiana 47408, USA. sunkim2@indiana.edu

Proteins
|November 23, 2006
PubMed
Summary

The iGibbs framework improves protein motif prediction accuracy by integrating sequence clustering and pattern refinement. This novel approach enhances the speed and reliability of motif discovery compared to existing methods.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Related Experiment Videos

Last Updated: Jul 18, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Motif prediction is crucial for understanding protein function and identifying conserved biological patterns.
  • Existing algorithms like Gibbs offer speed but can lack accuracy in motif prediction.
  • Improving motif prediction accuracy while maintaining computational efficiency is a significant challenge.

Purpose of the Study:

  • To develop an enhanced motif prediction framework for proteins that improves accuracy over existing methods.
  • To integrate novel techniques for guiding motif search and refining predicted patterns.
  • To create a user-friendly framework that automatically handles motif prediction across sequence subsets.

Main Methods:

  • Development of iGibbs, an integrated motif search framework for proteins.
  • Implementation of a novel double clustering approach combining sequence clustering and pattern refinement.
  • Automatic clustering of input sequences to predict motifs from distinct subsets without user-defined motif counts.

Main Results:

  • iGibbs demonstrated significant improvements in motif prediction accuracy on the PROSITE database.
  • The framework successfully predicted true motifs in cases where Gibbs alone might fail.
  • iGibbs achieved higher accuracy than exhaustive methods like MEME while being an order of magnitude faster.

Conclusions:

  • The iGibbs framework offers a substantial advancement in protein motif prediction.
  • The double clustering approach effectively guides motif search, enhancing accuracy and speed.
  • iGibbs provides a more accurate and efficient alternative for motif discovery in protein sequences.