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Accounting for Intraligand Interactions in Flexible Ligand Docking with a PMF-Based Scoring Function.

A Y Lizunov1,2, A L Gonchar2, N I Zaitseva3

  • 1Department of Mathematics, Moscow Institute of Physics and Technology , Moscow 117303, Russia.

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Flexible ligands commonly form hydrophobic intraligand contacts, but rarely hydrogen bonds. Enhancing scoring functions with intraligand interactions improves protein-ligand docking and scoring accuracy.

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Area of Science:

  • Computational chemistry and molecular modeling.
  • Drug discovery and development.

Background:

  • Intraligand contacts within protein-ligand complexes influence binding affinity and pose.
  • Flexible ligands exhibit distinct interaction patterns compared to rigid ones.

Purpose of the Study:

  • To analyze intraligand contact frequencies in protein-ligand complexes.
  • To develop and validate a method for enhancing scoring functions by incorporating intraligand interactions.
  • To investigate the impact of intraligand interactions on molecular docking and scoring performance.

Main Methods:

  • Analysis of 1300 protein-ligand complexes to determine intraligand contact frequencies.
  • Implementation of a universal method to enhance a potential of mean force (PMF)-based scoring function.
  • Utilizing an in-house program with the Algo_score scoring function and Tarasov-Muryshev PMF.
  • Evaluating docking and scoring quality using parameters for intraligand interaction estimation.

Main Results:

  • Flexible ligands predominantly form intraligand hydrophobic contacts; intraligand hydrogen bonds are infrequent.
  • The enhanced scoring function significantly improved docking and scoring accuracy for flexible ligands.
  • Correlation analysis revealed the influence of intraligand interaction parameters on docking outcomes.

Conclusions:

  • Incorporating intraligand interactions into scoring functions is a viable strategy for improving molecular docking.
  • Understanding intraligand contact patterns, particularly hydrophobic interactions, is crucial for accurate protein-ligand binding predictions.
  • The developed method offers a universal approach to enhance scoring functions for better drug design and discovery.