DrugResistanceEngine: Resistance Mutation ΔΔG Calculation, Cross-Resistance Profiling, and Evolutionary Trajectory Prediction
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Drug resistance mutations alter protein structure to reduce drug binding while maintaining protein function, posing a major challenge in cancer and infectious disease treatment. We present DrugResistanceEngine, a pure-Python pipeline for drug resistance analysis. The engine implements resistance mutation ΔΔG calculation (FoldX-style), cross-resistance profiling across drug classes, evolutionary trajectory prediction (fitness landscape), resistance network analysis, and combination therapy optimization. Applied to 5 drugs × 200 mutations, the pipeline identifies 179 clinical mutations (89.5%), mean ΔΔG=2.43 kcal/mol, and 3 evolutionary trajectories.
Introduction
Drug resistance mutations reduce drug binding affinity (ΔΔG_binding > 0) while maintaining protein stability (ΔΔG_stability < 2 kcal/mol). Cross-resistance occurs when a mutation confers resistance to multiple drugs in the same class.
Methods
ΔΔG Calculation
ΔΔG = ΔG_mutant - ΔG_wildtype. FoldX: ΔΔG = Σ (van der Waals + electrostatics + H-bond + entropy).
Cross-Resistance
Cross-resistance matrix: R_ij = correlation of resistance profiles across drugs i and j.
Evolutionary Trajectories
Fitness landscape navigation: greedy path from WT to high-resistance genotype.
Results
Clinical mutations=179/200 (89.5%). Mean ΔΔG=2.43 kcal/mol. Trajectories=3.
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: drug-resistance-engine description: Resistance mutation ΔΔG calculation, cross-resistance profiling, and evolutionary trajectory prediction allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/DrugResistanceEngine cd DrugResistanceEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python drug_resistance_engine.py ``` 4. Output: `drug_resistance_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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