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Related Concept Videos

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...
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...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:

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Related Experiment Video

Updated: May 17, 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 simple iterative method to optimize protein-ligand-binding residue prediction.

Zhijun Qiu1, Cuili Qin, Min Jiu

  • 1College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471023, China. qiuzj2003@163.com

Journal of Theoretical Biology
|November 6, 2012
PubMed
Summary

An iterative method (IM) enhances protein-ligand-binding residue prediction by refining residue definitions. This approach significantly improves classifier performance, offering a more effective and broadly applicable solution.

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

Published on: July 14, 2015

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Machine Learning in Biology

Background:

  • Accurate prediction of protein-ligand-binding residues is crucial for understanding molecular interactions.
  • Existing methods for binding residue prediction may have limitations in performance and applicability.
  • Iterative refinement of prediction parameters can potentially enhance accuracy.

Purpose of the Study:

  • To introduce and evaluate an iterative method (IM) for improving protein-ligand-binding residue prediction.
  • To compare the performance of the IM against previous methods, such as the threshold-altering method (TAM).
  • To assess the generalizability and advantages of the IM across different models and algorithms.

Main Methods:

  • Development of an iterative method (IM) that progressively modifies the binding residue definition.
  • Utilizing a balanced assessment index (BAI) to quantify classifier performance.
  • Statistical analysis of mean per-instance BAI scores to compare the IM with other methods.

Main Results:

  • The IM-optimized classifier achieved a BAI score of 80.4, significantly outperforming the initial classifier's score of 66.9.
  • The IM demonstrated statistically significant performance improvement compared to the threshold-altering method (TAM).
  • The IM is independent of specific residue characterization models and learning algorithms, indicating broad applicability.

Conclusions:

  • Optimizing the binding residue definition through iterative refinement is an effective strategy for enhancing protein-ligand-binding residue prediction.
  • The developed iterative method (IM) offers a robust and broadly applicable approach to improve prediction accuracy.
  • The findings suggest that iterative approaches can be a valuable tool in computational biology for refining predictive models.