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Computational Drug Target Screening through Protein Interaction Profiles.

Santiago Vilar1,2, Elías Quezada3, Eugenio Uriarte2

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|November 16, 2016
PubMed
Summary

We developed a computational method using Target Interaction Profile Fingerprints (TIPFs) to identify new drug-target interactions for drug discovery and repositioning. This approach successfully identified potential inhibitors for monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzymes.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Pharmacology

Background:

  • Discovering novel drug-target interactions is crucial for developing new therapeutics and understanding drug mechanisms.
  • Existing computational methods for virtual screening have limitations in large-scale drug discovery and repositioning.

Purpose of the Study:

  • To introduce a novel computational method for large-scale virtual screening to identify new drug-target interactions.
  • To discover new therapeutic targets for existing molecules and existing drugs.
  • To validate the method by identifying novel inhibitors for monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzymes.

Main Methods:

  • Development of Target Interaction Profile Fingerprints (TIPFs) using the ChEMBL database to assess drug similarity.
  • Generation of putative compound-target candidates by analyzing non-intersecting targets between compound pairs.
  • Molecular docking and experimental assays to validate predicted drug-target interactions for MAO-B and COX-1.

Main Results:

  • Ethoxzolamide and piperlongumine demonstrated inhibitory activity against human MAO-B (hMAO-B) with IC50 values of 25 μM and 65 μM, respectively.
  • SB-202190 and RO-316233 exhibited potent inhibitory activity against human COX-1 (hCOX-1) with IC50 values of 24 μM and 25 μM, comparable to known inhibitors.
  • Lapatinib and indirubin-3'-monoxime showed moderate hCOX-1 inhibition.

Conclusions:

  • The proposed TIPF-based computational method is effective for large-scale virtual screening and identifying novel drug-target interactions.
  • This method holds significant potential for accelerating lead discovery, drug repositioning, and assessing drug safety.
  • The study successfully identified novel inhibitors for MAO-B and COX-1, validating the predictive power of the computational approach.