Binding Kinetics
Purpose
Analyze, predict, and optimize drug-target binding kinetics:
on-rates (kon), off-rates (koff), residence time (RT = 1/koff),
thermodynamic signatures (ΔH/ΔS), and structure-kinetics relationships (SKR).
When to Use This Skill
- Analyzing SPR sensorgrams (Biacore/Sierra)
- Fitting ITC thermograms for ΔH/ΔS/ΔG
- Computing residence time from MD simulations (τRAMD, metadynamics)
- Building QSAR models for koff/kon
- Interpreting kinetic selectivity vs equilibrium selectivity
- Prioritizing compounds by residence time, not just KD
Reference Files
| File |
Content |
references/kinetics-theory.md |
kon/koff/KD/RT definitions, kinetic selectivity, two-state binding, conformational selection vs induced fit, thermodynamic signatures |
references/spr-analysis.md |
SPR sensorgrams, 1:1 Langmuir fitting, two-state model, Rmax/Rtheor, bulk correction, Biacore data parsing, Python fitting |
references/itc-analysis.md |
ITC thermogram integration, n/KD/ΔH/ΔS/ΔG fitting, SEDPHAT equivalents in Python, van't Hoff, enthalpy-entropy compensation |
references/residence-time-md.md |
τRAMD (random acceleration MD), funnel metadynamics, WTmetaD koff estimation, HTMD τRAMD Python, unbinding pathway analysis |
references/kinetic-qsar.md |
Structure-kinetics relationships (SKR), features for koff/kon models, kinetic maps (LE vs kinetic efficiency), koff cliff detection |
Quick Routing
"Fit my SPR data" → spr-analysis.md
"Fit my ITC experiment" → itc-analysis.md
"Compute residence time from MD" → residence-time-md.md
"Build a model to predict koff" → kinetic-qsar.md
"Why does my drug work despite poor KD?" → kinetics-theory.md
Key Relationships
# Core kinetic relationships
KD = koff / kon # M (equilibrium dissociation constant)
pKD = -log10(KD) # analogous to pIC50
RT = 1 / koff # seconds (residence time)
t_half = ln(2) / koff # seconds (half-life of complex)
# Thermodynamics
ΔG = RT_gas * ln(KD) # kcal/mol (RT_gas = 0.592 at 298K)
ΔG = ΔH - T*ΔS # enthalpy-entropy decomposition
# Typical drug ranges
# kon: 10^4 – 10^7 M^-1 s^-1
# koff: 10^-5 – 10^-1 s^-1
# KD: nM – µM
# RT: 10 s – 10^5 s (hours)
Integration with ALKYL Skills
- Docking poses for kinetic path analysis:
docking skill
- MD trajectories for τRAMD:
force-fields skill + MDAnalysis
- SKR feature computation:
chem_props.py, chem_analyze.py
- MMPA for koff SAR:
mmpa skill
- Uncertainty in kinetic predictions:
uncertainty-qsar skill
1---2name: binding-kinetics3description: Binding Kinetics4---56# Binding Kinetics78## Purpose9Analyze, predict, and optimize drug-target binding kinetics:10on-rates (kon), off-rates (koff), residence time (RT = 1/koff),11thermodynamic signatures (ΔH/ΔS), and structure-kinetics relationships (SKR).1213## When to Use This Skill14- Analyzing SPR sensorgrams (Biacore/Sierra)15- Fitting ITC thermograms for ΔH/ΔS/ΔG16- Computing residence time from MD simulations (τRAMD, metadynamics)17- Building QSAR models for koff/kon18- Interpreting kinetic selectivity vs equilibrium selectivity19- Prioritizing compounds by residence time, not just KD2021## Reference Files2223| File | Content |24|------|---------|25| `references/kinetics-theory.md` | kon/koff/KD/RT definitions, kinetic selectivity, two-state binding, conformational selection vs induced fit, thermodynamic signatures |26| `references/spr-analysis.md` | SPR sensorgrams, 1:1 Langmuir fitting, two-state model, Rmax/Rtheor, bulk correction, Biacore data parsing, Python fitting |27| `references/itc-analysis.md` | ITC thermogram integration, n/KD/ΔH/ΔS/ΔG fitting, SEDPHAT equivalents in Python, van't Hoff, enthalpy-entropy compensation |28| `references/residence-time-md.md` | τRAMD (random acceleration MD), funnel metadynamics, WTmetaD koff estimation, HTMD τRAMD Python, unbinding pathway analysis |29| `references/kinetic-qsar.md` | Structure-kinetics relationships (SKR), features for koff/kon models, kinetic maps (LE vs kinetic efficiency), koff cliff detection |3031## Quick Routing3233**"Fit my SPR data"** → `spr-analysis.md`3435**"Fit my ITC experiment"** → `itc-analysis.md`3637**"Compute residence time from MD"** → `residence-time-md.md`3839**"Build a model to predict koff"** → `kinetic-qsar.md`4041**"Why does my drug work despite poor KD?"** → `kinetics-theory.md`4243## Key Relationships4445```python46# Core kinetic relationships47KD = koff / kon # M (equilibrium dissociation constant)48pKD = -log10(KD) # analogous to pIC5049RT = 1 / koff # seconds (residence time)50t_half = ln(2) / koff # seconds (half-life of complex)5152# Thermodynamics53ΔG = RT_gas * ln(KD) # kcal/mol (RT_gas = 0.592 at 298K)54ΔG = ΔH - T*ΔS # enthalpy-entropy decomposition5556# Typical drug ranges57# kon: 10^4 – 10^7 M^-1 s^-158# koff: 10^-5 – 10^-1 s^-159# KD: nM – µM60# RT: 10 s – 10^5 s (hours)61```6263## Integration with ALKYL Skills64- Docking poses for kinetic path analysis: `docking` skill65- MD trajectories for τRAMD: `force-fields` skill + MDAnalysis66- SKR feature computation: `chem_props.py`, `chem_analyze.py`67- MMPA for koff SAR: `mmpa` skill68- Uncertainty in kinetic predictions: `uncertainty-qsar` skill
Kdevos12