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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
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Integrating Physics-Based Simulations with Data-Driven Deep Learning Represents a Robust Strategy for Developing

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Deep-CovBoost, a novel computational pipeline, combines AI and simulations to optimize coronavirus protease inhibitors. This approach accelerates drug discovery by identifying potent compounds that enhance viral binding affinity.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Artificial intelligence in medicine

Background:

  • The coronavirus main protease is a critical target for antiviral drug development.
  • Existing inhibitors often do not fully exploit allosteric binding sites.

Purpose of the Study:

  • To develop and validate a computational pipeline (Deep-CovBoost) for structure-based optimization of coronavirus main protease inhibitors.
  • To identify novel inhibitors with enhanced binding affinity by targeting underexploited subpockets.

Main Methods:

  • Integration of deep learning with free energy perturbation (FEP) simulations.
  • Structure-based drug design and virtual screening of inhibitor analogs.
  • Molecular dynamics simulations for rigorous validation of binding affinity.

Main Results:

  • Deep-CovBoost successfully generated and prioritized novel inhibitor analogs.
  • Optimized compounds, including I3C-1, I3C-2, and I3C-35, demonstrated enhanced binding affinity.
  • Compounds effectively engaged the S4 and S5 subpockets of the main protease.

Conclusions:

  • The combination of AI and physics-based simulations accelerates antiviral lead optimization.
  • Deep-CovBoost is a promising approach for designing potent inhibitors against coronavirus targets.
  • Targeting underexploited subpockets can significantly improve inhibitor efficacy.