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Related Concept Videos

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 hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
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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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MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions.

Dejun Jiang1,2,3, Zhaofeng Ye2, Chang-Yu Hsieh1

  • 1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China tingjunhou@zju.edu.cn.

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We developed MetalProGNet, a deep graph model for predicting metalloprotein-ligand interactions. This approach accurately identifies high-affinity ligands, crucial for developing treatments for diseases like cancer and HIV infection.

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Area of Science:

  • Biochemistry and Structural Biology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Metalloproteins are vital in biological processes and disease, making high-affinity ligand discovery essential for therapeutic development.
  • Existing in silico methods for ligand identification often overlook the unique properties of metalloproteins.
  • There is a need for specialized computational tools to accurately predict metalloprotein-ligand interactions.

Purpose of the Study:

  • To evaluate existing docking tools for metalloprotein-ligand complex prediction.
  • To develop and validate a novel structure-based deep graph model, MetalProGNet, for predicting metalloprotein-ligand interactions.
  • To interpret the MetalProGNet model to gain insights into metalloprotein-ligand binding mechanisms.

Main Methods:

  • Compiled the largest dataset of 3079 high-quality metalloprotein-ligand complex structures.
  • Systematically evaluated the performance of PLANTS, AutoDock Vina, and Glide SP for metalloprotein docking.
  • Developed MetalProGNet, a deep graph model utilizing graph convolution to explicitly model metal ion coordination and noncovalent interactions.

Main Results:

  • MetalProGNet demonstrated superior performance compared to baseline methods on internal, ChEMBL, and virtual screening datasets.
  • The model accurately predicted interactions for 22 different metalloproteins.
  • A noncovalent atom-atom interaction masking technique successfully interpreted the model, aligning learned knowledge with physical principles.

Conclusions:

  • MetalProGNet represents a significant advancement in computational prediction of metalloprotein-ligand interactions.
  • The model's accuracy and interpretability offer a powerful tool for accelerating the discovery of therapeutic ligands for metalloprotein-related diseases.
  • Explicitly modeling metal ion coordination is crucial for accurate metalloprotein-ligand binding prediction.