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Related Experiment Video

Updated: Jan 19, 2026

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms
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Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0.

Woong-Hee Shin1,2, Daisuke Kihara3,4,5,6

  • 1Department of Biological Science, Purdue University, West Lafayette, IN, 47907, USA.

Journal of Computer-Aided Molecular Design
|September 12, 2019
PubMed
Summary

PL-PatchSurfer2.0, a computational method, accurately predicts protein-ligand interactions using 3D Zernike descriptors for drug discovery. It was successfully applied in the Grand Challenge 4 (GC4) for BACE-1 and CatS targets.

Keywords:
BACE-1CatSD3R Grand ChallengePL-PatchSurferProtein–ligand interactionVirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Computational prediction of protein-ligand interactions is crucial for drug discovery.
  • Existing computational tools require objective performance assessment.
  • The Drug Design Data Resource Grand Challenge (GC4) provides such a platform.

Purpose of the Study:

  • To evaluate the performance of the PL-PatchSurfer2.0 method in predicting protein-ligand interactions.
  • To assess PL-PatchSurfer2.0's capabilities in the GC4 for BACE-1 and CatS targets.

Main Methods:

  • PL-PatchSurfer2.0, a molecular surface-based virtual screening method.
  • Utilizes 3D Zernike descriptors for quantifying local shape complementarity.
  • Integrates molecular flexibility and provides stable affinity assessment.
  • Employs an external docking program (AutoDock Vina) to generate ligand poses for evaluation.

Main Results:

  • PL-PatchSurfer2.0 was applied to the BACE-1 and CatS targets in GC4.
  • The method demonstrated its performance in predicting binding affinities and poses.
  • Detailed performance results for GC4 are reported.

Conclusions:

  • PL-PatchSurfer2.0 is a robust method for computational prediction of protein-ligand interactions.
  • The 3D Zernike descriptor effectively captures molecular shape complementarity.
  • The method shows promise for accelerating the drug discovery process.