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Related Concept Videos

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Updated: Jun 27, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

Linyuan Guo1,2, Jianxin Wang1,2

  • 1School of Computer Science and Engineering, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China.

Briefings in Bioinformatics
|May 6, 2024
PubMed
Summary
This summary is machine-generated.

GSScore, a new deep learning method, accurately predicts protein-ligand docking poses. It uses a Graphormer and shell-like graph architecture to identify near-native conformations, improving drug discovery computational approaches.

Keywords:
GraphormerProtein–ligand dockingShell-like architecturescoring method

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Protein-ligand interactions (PLIs) are crucial for biological processes and drug development.
  • Experimental determination of PLIs is complex and costly, driving demand for computational methods like protein-ligand docking.
  • Existing machine learning models for predicting docking pose accuracy (RMSD) require improved scoring functions.

Purpose of the Study:

  • To develop a novel deep learning-based scoring method for enhanced accuracy in predicting the root mean square deviation (RMSD) of protein-ligand docking poses.
  • To introduce GSScore, a method leveraging Graphormer and a shell-like graph architecture for improved recognition of near-native docking conformations.

Main Methods:

  • GSScore models the protein-ligand docking interface as multiple bipartite graphs within defined atomic shells.
  • Atoms are represented as nodes, and interactions are captured using a Graphormer framework combined with a shell-like graph structure.
  • The method operates directly on the atomic structure without requiring additional input features.

Main Results:

  • GSScore demonstrated significant improvements in RMSD prediction accuracy compared to existing methods.
  • Evaluations on PDBBind 2019, CASF2016, and DUD-E datasets showed enhanced performance in terms of RMSE, Pearson correlation coefficient (R), Spearman correlation coefficient, and Docking Power.
  • The Graphormer and shell-like graph architecture effectively distinguished between favorable near-native and unfavorable non-native poses.

Conclusions:

  • GSScore offers a powerful new deep learning approach for accurate RMSD prediction in protein-ligand docking.
  • The method's ability to capture subtle structural differences enhances the reliability of computational screening in drug discovery.
  • GSScore represents a significant advancement in scoring functions for protein-ligand docking pose evaluation.