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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
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...

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Related Experiment Video

Updated: Jun 5, 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

Discovering approximate-associated sequence patterns for protein-DNA interactions.

Tak-Ming Chan1, Ka-Chun Wong, Kin-Hong Lee

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong.

Bioinformatics (Oxford, England)
|January 4, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces approximate rules for transcription factor (TF) and transcription factor binding site (TFBS) interactions, improving upon exact rules. These new rules better capture biological variations and enhance the discovery of informative protein-DNA binding patterns.

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DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
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DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

Published on: October 8, 2019

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Transcription factors (TFs) and transcription factor binding sites (TFBSs) are crucial for gene regulation through protein-DNA interactions.
  • Existing methods using exact TF-TFBS rules struggle with biological variations, limiting their informativeness.
  • There is a need for generalized rules that accommodate the inherent variability in biological systems.

Purpose of the Study:

  • To generalize exact TF-TFBS rules into approximate ones to better represent biological variations.
  • To develop a progressive and computationally efficient approach for discovering these approximate rules.
  • To enhance the accuracy and informativeness of TF-TFBS binding rule discovery.

Main Methods:

  • Grouping similar TFBSs from the TRANSFAC database.
  • Discovering approximate and conserved binding cores from TF sequences using a customized algorithm.
  • Associating grouped TFBS consensuses with TF cores to derive approximate TF-TFBS rules.
  • Validating discovered rules against protein-DNA binding pairs in the Protein Data Bank (PDB).

Main Results:

  • Approximate TF-TFBS rules yielded significantly more verified rules and up to 300% higher verification ratios compared to exact rules.
  • The customized algorithm outperformed traditional methods by over 73% in verification ratios.
  • Statistically significant approximate rules (64-79%) accurately reflect flexible and specific protein-DNA interactions, confirmed by variation analysis and NCBI records.

Conclusions:

  • Approximate TF-TFBS rules offer a more generalized and informative approach to understanding protein-DNA interactions.
  • The developed method effectively handles biological variations, leading to improved accuracy in rule discovery.
  • These findings pave the way for exploring a wider range of informative binding rules in transcriptional regulation.