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Related Concept Videos

A Protocol for Computer-Based Protein Structure and Function Prediction16:41

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Related Experiment Video

Updated: Jan 20, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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Improving ligand 3D shape similarity-based pose prediction with a continuum solvent model.

Ashutosh Kumar1, Kam Y J Zhang2

  • 1Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.

Journal of Computer-Aided Molecular Design
|August 30, 2019
PubMed
Summary

We improved protein-ligand binding pose prediction using a shape similarity method (PoPSS). The new PoPSS-PB approach incorporates solvation effects, significantly enhancing prediction accuracy over previous versions.

Keywords:
D3RD3R Grand Challenge 4Drug design data resourceLigand 3D shape similarityMolecular dockingPose prediction

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Last Updated: Jan 20, 2026

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Accurate prediction of ligand binding poses is crucial for drug discovery.
  • Previous methods like PoPSS and PoPSS-Lite showed promise but could be further improved.

Purpose of the Study:

  • To develop and evaluate a novel pose prediction method, PoPSS-PB, incorporating continuum solvent models.
  • To assess the performance of PoPSS-PB against existing methods in a prospective study.

Main Methods:

  • Developed PoPSS-PB, refining ligand conformation using shape similarity and Poisson-Boltzmann electrostatics for solvation.
  • Prospectively evaluated PoPSS-PB, PoPSS, and PoPSS-Lite in the D3R GC4 challenge.
  • Compared PoPSS-PB performance against other D3R GC4 pose prediction submissions.

Main Results:

  • PoPSS-PB achieved excellent prospective performance with mean and median RMSDs of 1.20 Å and 1.13 Å, respectively.
  • PoPSS-PB demonstrated improved accuracy over PoPSS and PoPSS-Lite.
  • The method successfully predicted binding poses for ligands with unknown binding modes.

Conclusions:

  • Ligand 3D shape similarity to known crystallographic ligands is effective for predicting binding poses.
  • Incorporating solvation effects significantly improves protein-ligand pose prediction accuracy.
  • PoPSS-PB represents a substantial advancement in computational drug design.