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The Problem You Are Trying to Solve

“I have a binder to my protein, but I want to design synergistic or allosteric modulators to improve target engagement and reduce reliance on traditional, trial-and-error SAR campaigns.”
Metabolite Inspired Drug Development & Allosteric Optimization
For some cases, a primary binder (orthosteric ligand), especially with activities in the 1-100uM range, is not enough to:
  • Achieve desired potency in complex biological systems
  • Improve selectivity over homologous proteins
  • Modulate protein function dynamically (activation vs inhibition)
  • Enhance stability or residence time
Allosteric modulators and metabolite-inspired designs offer powerful solutions, but experimentally screening for allosteric sites and combinations is time-consuming and expensive. Moreover, elucidating the detailed breakdown of the molecular mechanisms driving these dynamic interactions can be difficult to experimentally resolve.

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Solution

This workflow identifies potential allosteric sites, designs modulators inspired by other small molecule engagers like metabolites or secondary binding pockets, and computationally validates synergistic engagement before experimental investment.
The primary design chain is: Primary Binder Analysis → Allosteric Site Identification → Allosteric Modulator Design → Binding & Stability Validation → Synergy Evaluation. This enables rational exploration of allosteric control and multi-site engagement with minimal experimental burden.

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What Data Do I Need to Provide?

Required
  • Protein structure (PDB or predicted structure)
  • Primary binder structure (SMILES or PDB complex)
Recommended
  • Known compound or metabolite structures (if metabolite-inspired design is desired)
  • Known regulatory partners or secondary binders
  • Any experimental binding/activity data for calibration
Optional
  • Target conformational states of interest (active vs inactive)
  • Known mutational or regulatory hotspot regions

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Workflow

  1. Analyze the Primary Binder and Binding Mode
Start by deeply understanding how your existing ligand engages the protein. Users typically:
  • Visualize the orthosteric binding pose with Docking
  • Identify key residues and interaction motifs
  • Validate stability and observe dynamic contacts with Protein-Ligand MD
  • Determine which structural regions remain unoccupied
Key questions:
  • Which residues drive binding?
  • Are there flexible regions or distal pockets that move during MD?
  • Does binding induce conformational shifts?
This gives a high-confidence model of orthosteric engagement and dynamic behavior.
  1. Identify Potential Allosteric Sites
Allosteric sites often emerge through dynamics, flexibility, and conformational coupling. Users typically:
  • Run Protein-Water MD to observe natural pocket breathing, and feed the simulations into MDPocket engine where transient pockets can be elucidated for further analysis.
  • Analyze RMSF, SASA, and PCA outputs to identify flexible regulatory regions
  • Examine transient pocket formation during MD trajectories
Indicators of candidate allosteric regions:
  • Transient pockets forming distal to the active site
  • Correlated motion between distant domains
  • Stable but ligand-free cavities that open during simulation
This produces candidate allosteric pocket(s) for modulator targeting.
  1. Design Allosteric or Metabolite-Inspired Modulators
Once candidate pockets are identified, design molecules that engage them. Users can:
  • Run Virtual Screening / Docking on the allosteric pocket
  • Use De novo Library Generation for new chemical matter
  • Use Molecular Optimization to decorate metabolite-like scaffolds
  • Use Pharmacophore Analysis to match pocket features
If metabolite-inspired:
  • Upload metabolite SMILES
  • Identify shared motifs with endogenous ligands
  • Preserve functional groups important for recognition
  • An example of this would be kinase inhibitor designs replicating ATP structures that drive engagement for proteins that do downstream phosphorylation
This results in your shortlist of predicted allosteric binders.
  1. Validate Allosteric Binding Stability
Now confirm that predicted modulators bind stably and plausibly. Users run:
  • Initial assessments of the orthosteric ligand in the primary pocket can be done with docking, co-folding, or molecular dynamics simulations
  • ProteinLigand MD for the allosteric binder alone
  • Optionally simulate both orthosteric + allosteric ligand together. Revilico’s Boltz Co-Folding Engine allows for you to simulate multiple ligands binding within 1 structure, seeing how activity will change with just 1 ligand binding with the protein versus with both compounds.
  • Using downstream Protein Ligand MD for either system will help elucidate pockets opening up on the protein with longer term dynamic time scales.
What you’re evaluating:
  • Stable binding in the secondary pocket
  • No destabilization of protein core
  • Conformational shifts induced by allosteric binding
This provides your set of stability-validated allosteric binder candidates.
  1. Evaluate Synergistic Engagement
The key goal is enhanced target engagement or functional modulation. Users can:
  • Simulate dual-bound systems (orthosteric + allosteric ligand) with co-folding with pooled ligand pairs on one specific target and then running the ligands on Protein–Ligand MD simulations with long enough time spans to capture protein motion induced with ligands.
  • Compare conformational landscapes via PCA
  • Evaluate binding free energy shifts via:
    • MMPBSA and MMGBSA (rapid estimate)
    • ABFE/RBFE (higher rigor with alchemical transformations of the ligand)
Key evaluation questions:
  • Does the allosteric ligand stabilize the orthosteric binder?
  • Does the protein adopt a more favorable active/inactive conformation?
  • Is dual engagement thermodynamically favorable?
This quantifies synergy and mechanistic insight for the design of allosteric ligands.

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Results

  • Identified and validated allosteric pocket(s)
  • Designed metabolite-inspired modulators
  • Stability-confirmed binding models
  • Quantified synergistic target engagement
  • Reduced need for brute-force SAR experimentation

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Integration with Other Engines (Optional)

This workflow can connect to:
  • QSAR Modeling (learn patterns from orthosteric + allosteric activity data)
  • ADMET-AI (ensure new modulators remain developable)
  • Quantum Chemistry (HOMO–LUMO, Geometry Optimization) for reactivity/stability checks
  • Free Energy Perturbation for fine analog ranking
  • scRNA-Seq Analysis to assess downstream pathway effects of modulation

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Why Revilico?

Revilico enables metabolite-inspired and allosteric drug design by combining:
  • Structural insight (Docking + MD)
  • Dynamic conformational analysis
  • Generative chemistry capabilities
  • Thermodynamic validation (FEP)
  • Integrated interpretation tools
Instead of running large, unfocused SAR campaigns, you computationally guide allosteric discovery toward the most mechanistically promising and synergistic designs.