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MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank

Sadettin Y Ugurlu1, David McDonald2, Ramazan Enisoglu3

  • 1School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

Scientific Reports
|February 14, 2025
PubMed
Summary

MEGA PROTAC enhances the prediction of ternary complex structures for proteolysis-targeting chimaeras (PROTACs). This computational method improves the quality and ranking of predicted structures, aiding in the rational design of PROTACs.

Keywords:
DockingMediated ternary complexPROTACProteolysis-targeting chimaerasRank aggregationSequential filtration

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Proteolysis-targeting chimaeras (PROTACs) are a promising therapeutic modality due to their unique pharmacological properties.
  • Rational PROTAC design is challenged by the difficulty in predicting optimal linker structures for productive ternary complex formation.
  • Existing computational methods for ternary complex prediction have limitations in accuracy and ranking performance.

Purpose of the Study:

  • To develop an enhanced computational method, MEGA PROTAC, for improving the prediction of ternary complex structures in PROTAC design.
  • To increase the accuracy and ranking of predicted ternary complex structures compared to existing state-of-the-art methods.

Main Methods:

  • MEGA PROTAC utilizes MEGADOCK for protein-protein complex (PPC) docking to define an initial exploration space.
  • A sequential filtration strategy with rank aggregation is employed to select candidate PPCs for grid search.
  • Grid search is performed for translation and rotation, followed by energy-based clustering and further filtration.

Main Results:

  • MEGA PROTAC demonstrated superior performance over the Bayesian Optimisation for Ternary Complex Prediction (BOTCP) method in 16 out of 22 test cases.
  • Achieved an 18% higher mean and 35% higher median DockQ score compared to BOTCP.
  • Showcased 75% superior ranking, reduced cluster numbers for optimal scores, and a twofold improvement in locating acceptable DockQ scores.

Conclusions:

  • MEGA PROTAC significantly enhances the prediction of ternary complex quality and ranking for PROTACs.
  • The method provides a more efficient and accurate approach to guiding rational PROTAC design.
  • MEGA PROTAC represents a valuable tool for accelerating the discovery of novel PROTAC therapeutics.