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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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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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Updated: Jun 23, 2025

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Surface-based multimodal protein-ligand binding affinity prediction.

Shiyu Xu1, Lian Shen2, Menglong Zhang2

  • 1National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.

Bioinformatics (Oxford, England)
|June 21, 2024
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Summary
This summary is machine-generated.

Predicting protein-ligand binding affinity is improved by a new multimodal feature extraction (MFE) framework. This approach integrates protein surface, 3D structure, and sequence data for enhanced drug discovery.

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Area of Science:

  • Computational Biology
  • Drug Discovery
  • Bioinformatics

Background:

  • Accurate prediction of protein-ligand binding affinity is vital for drug discovery and optimization.
  • Current methods often rely on sequence or structural data, with limited exploration of protein surface information crucial for interactions.
  • Existing multimodal approaches may fail to effectively leverage complementary information due to simplistic feature concatenation.

Purpose of the Study:

  • To introduce a novel multimodal feature extraction (MFE) framework for improved protein-ligand binding affinity prediction.
  • To integrate diverse protein data modalities including surface, 3D structure, and sequence information.
  • To address the limitations of traditional multimodal feature fusion by incorporating cross-attention mechanisms.

Main Methods:

  • Developed a multimodal feature extraction (MFE) framework.
  • Incorporated protein surface, 3D structure, and sequence data.
  • Utilized a cross-attention mechanism for feature alignment across modalities.

Main Results:

  • Achieved state-of-the-art performance in predicting protein-ligand binding affinity.
  • Demonstrated the effectiveness of incorporating protein surface information through ablation studies.
  • Validated the necessity of multimodal feature alignment for enhanced prediction accuracy.

Conclusions:

  • The proposed MFE framework significantly advances protein-ligand binding affinity prediction.
  • Integrating multimodal protein data, especially surface information, with cross-attention is crucial for optimal performance.
  • The framework offers a promising direction for more effective drug screening and optimization.