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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation.

Dejun Jiang1,2, Huifeng Zhao1,2, Hongyan Du1

  • 1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

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Summary
This summary is machine-generated.

This study comprehensively evaluates molecular docking programs for nucleic acid (NA)-ligand interactions, crucial for drug discovery. PLANTS and LeDock show strong potential for predicting binding poses, outperforming specialized NA-ligand programs.

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Area of Science:

  • Computational chemistry and structural biology
  • Drug discovery and development
  • Molecular modeling and simulation

Background:

  • Nucleic acid (NA)-ligand interactions are vital for biological processes and are key drug targets.
  • Understanding these interactions at the atomic level is essential for drug discovery.
  • Molecular docking is a primary computational method for predicting NA-ligand interactions, but program performance is not well-characterized.

Purpose of the Study:

  • To systematically evaluate the performance of popular molecular docking programs for NA-ligand systems.
  • To identify the most effective programs for predicting binding poses and affinities in NA-ligand complexes.
  • To provide guidance for selecting appropriate computational tools in NA-targeted drug discovery.

Main Methods:

  • Compiled the largest structure-based NA-ligand binding dataset to date (800 noncovalent complexes).
  • Evaluated eight docking programs (six protein-ligand, two NA-ligand specific) on binding pose and affinity prediction.
  • Compared top-performing programs (PLANTS, LeDock) against a newer NA-ligand program (NLDock) on established datasets.

Main Results:

  • PLANTS and LeDock demonstrated promising or comparable results to specialized NA-ligand programs.
  • PLANTS, rDock, and LeDock excelled in binding pose prediction, with PLANTS achieving the highest success rates.
  • Binding affinity prediction was challenging for most programs; PLANTS showed the best correlation (Rp = -0.461).

Conclusions:

  • PLANTS and LeDock show significant potential for NA-ligand docking, outperforming NLDock in binding pose prediction.
  • This study offers the most comprehensive evaluation of molecular docking programs for NA-ligand systems to date.
  • The findings can guide researchers in selecting optimal computational tools for NA-targeted drug discovery.