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Ligand Binding Sites02:40

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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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...
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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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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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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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bindNode24: Competitive binding residue prediction with 60 % smaller model.

Kyra Erckert1,2, Franz Birkeneder1, Burkhard Rost1,3,4

  • 1TUM School of Computation, Information and Technology, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, Garching, Munich 85748, Germany.

Computational and Structural Biotechnology Journal
|April 1, 2025
PubMed
Summary
This summary is machine-generated.

bindNode24, a new Graph Neural Network method, predicts protein binding residues for small molecules, metal ions, and nucleic macromolecules. It integrates structural data for improved protein function prediction with fewer parameters.

Keywords:
Binding residue predictionBinding residuesEmbeddingsGraph neural networksMachine learningProtein bindingProtein language model

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Area of Science:

  • Structural biology
  • Computational biology
  • Bioinformatics

Background:

  • Protein ligand binding is crucial for function, but experimental data is scarce.
  • Existing methods predict binding residues using protein Language Model embeddings.
  • The AlphaFold Protein Structure Database provides reliable 3D structure predictions.

Purpose of the Study:

  • To introduce bindNode24, a novel Graph Neural Network method for predicting protein residue binding.
  • To predict binding for three major ligand classes: small molecules, metal ions, and nucleic macromolecules.
  • To evaluate bindNode24 against state-of-the-art methods.

Main Methods:

  • Utilizing Graph Neural Networks (GNNs) for residue-level binding prediction.
  • Integrating 3D structural features from AlphaFold2 predictions.
  • Training and evaluating the bindNode24 model on diverse protein datasets.

Main Results:

  • bindNode24 accurately predicts binding residues across three ligand classes.
  • The method achieves comparable performance to existing approaches.
  • bindNode24 significantly reduces the number of free parameters by nearly 60% compared to state-of-the-art.
  • Secondary and tertiary structure features from AlphaFold2 are effectively integrated.

Conclusions:

  • bindNode24 offers an efficient and effective approach for predicting protein binding sites.
  • Integrating structural information enhances GNN-based protein function prediction.
  • This method advances the prediction of protein-ligand interactions with limited experimental data.