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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...
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
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: Jun 27, 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

PDA-MutPred: Reliable prediction of binding affinity change upon mutation in protein-DNA complexes.

K Harini1, N Yasuo2, M Sekijima2

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamil Nadu, India.

International Journal of Biological Macromolecules
|June 25, 2026
PubMed
Summary

Predicting how mutations affect protein-DNA binding affinity is crucial. This study identifies key features and develops machine learning models to accurately forecast binding affinity changes (ΔΔG), aiding complex analysis and design.

Keywords:
Binding affinity changesLinear regressionPoint mutationsProtein-DNA complexesXGBoost

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Last Updated: Jun 27, 2026

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

Background:

  • Mutations in protein-DNA complexes disrupt cellular functions by altering structural integrity and binding affinities.
  • Accurately predicting changes in binding affinity (ΔΔG) due to mutations is a significant challenge in computational biology.

Purpose of the Study:

  • To analyze sequence, structural, and network features influencing binding affinity changes in protein-DNA complexes.
  • To develop and validate machine learning models for predicting mutation-induced ΔΔG in protein-DNA interactions.
  • To provide a web server resource for predicting these affinity changes.

Main Methods:

  • Compiled a dataset of 1169 mutations from 256 protein-DNA complexes using the ProNAB database.
  • Analyzed sequence, structural (atom contacts, hydrogen bonding), and network-based features, including amino acid properties and unfolding energy.
  • Developed and evaluated machine learning models, including classification based on DNA strand and protein functional class, using 10-fold cross-validation and blind testing.

Main Results:

  • Identified atom contacts, side-chain hydrogen bonding, amino acid properties, and unfolding energy as critical predictors of binding affinity changes.
  • Achieved an average correlation of 0.69 and MAE of 0.73 kcal/mol in cross-validation.
  • Demonstrated strong performance in a blind test with a correlation of 0.65 and MAE of 0.60 kcal/mol, outperforming existing methods.

Conclusions:

  • Key sequence, structural, and network features significantly impact binding affinity changes upon mutation in protein-DNA complexes.
  • Machine learning models effectively predict ΔΔG, offering improved accuracy over existing methods.
  • The developed web server serves as a valuable resource for researchers studying protein-DNA interactions and designing novel complexes.