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Conserved Binding Sites01:49

Conserved Binding Sites

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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.
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Ligand Binding Sites

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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.
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The Equilibrium Binding Constant and Binding Strength02:18

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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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PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural

K Harini1, Daisuke Kihara2, M Michael Gromiha3

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

Methods (San Diego, Calif.)
|March 16, 2023
PubMed
Summary
This summary is machine-generated.

Predicting protein-DNA binding affinity is crucial for understanding biological processes. This study identifies key structural features influencing binding and develops a predictive model, PDA-PreD, achieving high accuracy on experimental data.

Keywords:
Binding free energyContact potentialsStructure-based featuresprotein–DNA complex

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Area of Science:

  • Molecular Biology
  • Biophysics
  • Bioinformatics

Background:

  • Protein-DNA interactions are fundamental to gene expression, replication, and transcription.
  • Understanding binding affinity determinants is key to elucidating molecular recognition mechanisms.

Purpose of the Study:

  • To identify structure-based features governing protein-DNA binding affinity.
  • To develop a predictive model for protein-DNA binding affinity.

Main Methods:

  • Collected experimental binding free energy (ΔG) for 391 protein-DNA complexes.
  • Derived structure-based features including interaction energy, contact potentials, and binding site properties.
  • Developed multiple regression equations for affinity prediction.

Main Results:

  • Binding affinity is influenced by DNA strand number, protein class, atom contacts, binding site volume, interaction energies, and contact potentials.
  • The developed model achieved an average correlation of 0.78 and mean absolute error of 0.98 kcal/mol on a jack-knife test.
  • A webserver, PDA-PreD, was created for predicting protein-DNA binding affinity.

Conclusions:

  • Key structural and interaction features significantly impact protein-DNA binding affinity.
  • The developed regression models provide accurate predictions for binding affinity across different protein classes.
  • The PDA-PreD webserver offers a valuable tool for researchers studying protein-DNA interactions.