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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’ 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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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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Related Experiment Video

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.

Liangzhen Zheng1,2, Jintao Meng1,3, Kai Jiang4

  • 1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.

Briefings in Bioinformatics
|March 15, 2022
PubMed
Summary

This study introduces OnionNet-SFCT, a novel scoring function correction term that significantly improves molecular docking and screening accuracy when combined with traditional methods like Vina score for drug discovery.

Keywords:
machine learningmolecular dockingreversal virtual screeningscoring functionvirtual screening

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Area of Science:

  • Computational Chemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Scoring functions are crucial for structure-based drug discovery, aiding in hit identification and lead optimization.
  • Traditional empirical and force field-based scoring functions are reliable but can be limited in accuracy.
  • While advanced machine learning and deep learning models show promise, their direct application in large-scale docking and screening remains challenging.

Purpose of the Study:

  • To develop a reliable protein-ligand scoring function by augmenting the Vina score with a data-driven correction term.
  • To enhance the prediction accuracy of molecular docking and screening tasks.
  • To explore the utility of the combined scoring strategy for drug discovery and target fishing.

Main Methods:

  • Developed OnionNet-SFCT, a correction term based on an AdaBoost random forest model.
  • Utilized multiple layers of protein residue-ligand atom contacts to train the model.
  • Integrated OnionNet-SFCT with the Vina score to create an augmented scoring function.
  • Validated the enhanced scoring function on various benchmarks including cross-docking, CASF-2016, DUD-E, and DUD-AD.

Main Results:

  • The augmented scoring function (Vina score + OnionNet-SFCT) significantly improved AutoDock Vina's prediction accuracy for docking and screening.
  • The model demonstrated enhanced performance across diverse benchmark datasets.
  • The combined strategy increased pose selection accuracy and screening abilities in multiple docking applications.
  • Successfully identified known plant hormone receptors using the reverse practice approach.

Conclusions:

  • Combining data-driven models like OnionNet-SFCT with empirical scoring functions (Vina score) offers a robust strategy for structure-based drug discovery.
  • The augmented scoring function enhances the reliability and accuracy of molecular docking and screening.
  • This approach holds potential for broader applications in drug discovery, including target fishing.