Predict thermodynamic stability changes upon mutation — full saturation mutagenesis scan, thermal denaturation curves, and hotspot residue identification. All in pure Python.
From sequence to stability landscape — ΔΔG prediction, contact map estimation, and thermal denaturation all from first principles.
Estimate binding free energy changes for single and multiple mutations using a 19-feature model combining MJ potential, hydrophobicity, and secondary structure context.
Scan all 19 possible amino acid substitutions at every position. Rank by ΔΔG to identify stabilizing and destabilizing mutations across the entire sequence.
Estimate Tₘ from sequence composition and predict denaturation curves using a two-state model with Van't Hoff analysis.
Combine conservation scores with ΔΔG magnitude to pinpoint mutation hotspots — critical residues whose perturbation has outsized stability effects.
Derive coevolutionary-style contact maps from sequence alone using knowledge-based potentials — no MSA or homologs needed.
Heatmaps, bar charts, scatter plots, and denaturation curves — publication-ready figures with T4 Lysozyme, Barnase, Ubiquitin, and GFP demos.
Full saturation mutagenesis on T4 phage lysozyme (65 residues). G77A (ΔΔG = −1.4 kcal/mol) and C54T (ΔΔG = −0.5 kcal/mol) are known stabilizers — both ranked among the top predictions.
From raw sequence to stability landscape in four stages.
Requires Python 3.9+. No GPU, no external APIs, no training needed.