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Protein Target Prediction and Validation of Small Molecule Compound
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The interprotein scoring noises in glide docking scores.

Wei Wang1, Xi Zhou, Wanlin He

  • 1State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou, People's Republic of China.

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Optimizing scoring functions in reverse docking improves drug target prediction. A correction term enhanced accuracy by 27%, addressing interprotein biases in Glide scores for better drug development.

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Area of Science:

  • Computational biology
  • Drug discovery
  • cheminformatics

Background:

  • Small molecule drugs often have off-target interactions, causing side effects but also revealing new therapeutic uses.
  • Identifying these off-targets is crucial for evaluating a drug's developmental potential.
  • Reverse docking is a computational strategy to predict drug targets by screening compounds against protein libraries.

Purpose of the Study:

  • To investigate methods for improving target prediction accuracy in reverse docking.
  • To address the issue of scoring function biases in ligand-protein interactions.
  • To enhance the reliability of reverse docking for drug discovery.

Main Methods:

  • Utilized the Glide scoring function in a reverse docking approach.
  • Evaluated performance on a library of 58 protein-ligand complexes with known binding conformations.
  • Introduced a protein-characteristic-based correction term to the scoring function.

Main Results:

  • Initial Glide scores correctly identified only 57% of ligand-protein relationships.
  • Identified systematic over- or under-estimation of scores for specific proteins ('interprotein noises').
  • The correction term improved target prediction accuracy by 27%, reaching 72%.

Conclusions:

  • Scoring functions in reverse docking require optimization to overcome interprotein biases.
  • Protein-specific corrections can significantly enhance the accuracy of target prediction.
  • Further efforts are needed to characterize and normalize biases in all docking scores for improved drug discovery pipelines.