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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry

Yat T Tang1, Garland R Marshall

  • 1Center for Computational Biology, Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA.

Journal of Chemical Information and Modeling
|January 11, 2011
PubMed
Summary

This study developed PHOENIX, a scoring function for predicting protein-ligand binding affinity by separating enthalpy and entropy. It uses high-resolution crystal structures and thermodynamic data for improved accuracy in drug design.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Structural biology and biophysics
  • Drug discovery and medicinal chemistry

Background:

  • Binding affinity prediction is crucial for structure-based drug design.
  • Empirical scoring functions are widely used but have limitations in accuracy.
  • Advances in structural and thermodynamic data enable improved scoring function development.

Purpose of the Study:

  • To develop and validate PHOENIX, a novel scoring function for predicting protein-ligand binding affinities.
  • To evaluate the effectiveness of using high-resolution crystallographic data and thermodynamic parameters for scoring function development.
  • To assess strategies for improving binding affinity predictions by separating enthalpic and entropic contributions.

Main Methods:

  • Model training and testing using high-resolution X-ray crystallographic data.
  • Independent modeling of enthalpic (ΔH) and entropic (TΔS) contributions using thermodynamic parameters from isothermal titration calorimetry.
  • Utilizing shape and volume descriptors to capture entropic contributions.
  • Partial least-squares regression for deriving scoring functions.

Main Results:

  • The PHOENIX scoring function achieved a predictive r² (r(pred)²) of 0.55 and a standard error (SE) of 1.34 kcal/mol on the training set.
  • External validation using the PDBbind refined set yielded a Pearson correlation coefficient (R(p)) of 0.575 and a mean error (ME) of 1.41 pK(d).
  • Individual enthalpy and entropy predictions showed limited accuracy, but their combination improved binding free energy prediction.

Conclusions:

  • Separating enthalpy and entropy contributions, alongside using high-resolution structures and relevant descriptors, enhances binding affinity prediction accuracy.
  • The study validates the utility of high-quality thermodynamic data and crystallographic structures for developing robust scoring functions.
  • These strategies offer a promising approach for rapid and accurate binding affinity calculations in molecular design.