High-Performance Protein Stability Engine — predicting the energetic impact of amino acid mutations using custom molecular mechanics force fields and GPU acceleration.
Unlike black-box ML models that predict stability scores without explanation, this engine implements Molecular Mechanics (MM) — where protein energy is calculated directly from atomic coordinates using well-established physical potentials. The mutation stability change is defined as:
A positive ΔΔG means the mutation destabilises the protein (wild-type is more stable). A negative ΔΔG means stabilisation.
Models atomic repulsion (Pauli exclusion) and attraction (London dispersion). The 12-power term captures the "hard wall" of overlapping electron clouds; the 6-power term captures the gentle attractive well at optimal distances. Critical for detecting steric clashes from mutations.
Charge-charge interactions with a distance-dependent dielectric ε(r) = r to simulate implicit water screening without the cost of explicit molecules. Captures salt bridge formation/disruption by mutations.
Bond stretching, angle bending, and torsional/dihedral potentials prevent the protein from fragmenting during energy minimisation. Essential for structurally stable simulations. Parameters from AMBER ff14SB.
Linear Combination of Pairwise Overlaps calculates the Solvent Accessible Surface Area with fully analytical gradients — enabling derivative-based optimisation. The most expensive term; a SASA cache refreshes it every N iterations for a 5x speedup with negligible accuracy loss.
The engine implements a rigorous 4-step protocol to avoid this:
Import the crystal structure with all its inherent strain.
Relax the entire wild-type structure to a local energy minimum. For p53 (1TUP), this reduces energy from 105,698,651 kcal/mol → −19,666 kcal/mol. This clean, relaxed state is saved as the reference.
Perform in-silico mutagenesis on the relaxed structure using Dunbrack-dependent rotamer libraries, then minimise only atoms within a 10 Å cutoff of the mutation site using L-BFGS-B with full analytical gradients.
ΔΔG = E(mutant minimised) − E(wild-type relaxed). Each energy component (VDW, Coulomb, SASA) is reported separately for physical interpretability.
| Validation Case | Wild-Type Energy | Mutant Energy | ΔΔG | Result |
|---|---|---|---|---|
| p53 1TUP — K101A | −19,666.65 kcal/mol | −19,653.63 kcal/mol | +13.02 | Destabilising ✓ |
| Barnase — HIS102→SER | +29,669.83 kcal/mol | +29,577.75 kcal/mol | −92.08 | Stabilising ✓ |
Both results correlate with FoldX and experimental literature benchmarks.
| Phase | Technique | Impact |
|---|---|---|
| Phase 1 — Algorithmic | KD-Tree spatial indexing — atoms outside 12 Å interaction sphere pruned in O(log N) | Eliminates billions of irrelevant pairwise calculations |
| Phase 2 — Acceleration | Numba JIT compilation with CPU prange parallelism; CUDA kernel port for GPU offloading | 100x speedup for massive proteins on GPU |
| Phase 3 — Hardware | State-of-Array (SoA) memory layout — coordinates stored as contiguous float64 NumPy arrays | Full CPU cache line utilisation; eliminates object overhead |
| Bonus — SASA Cache | Recalculates surface area every N=5 minimisation iterations instead of every step | ~5x speedup in minimisation loop with negligible accuracy loss |
Three endpoints: POST /calculate (submit job), GET /status/{job_id} (real-time progress), GET /result/{job_id} (energy breakdown + stability verdict). JSON request/response. Single-mutation prediction in <15 seconds.
Fully containerised with all biophysical dependencies. Kubernetes manifests include HorizontalPodAutoscaling for burst-throughput screening of thousands of mutations per hour.
Live monitoring of calculation latency, error rates, and GPU memory utilisation. Production-grade observability at zero licensing cost.
Automated regression tests comparing runtimes against stored baselines for small (Barnase), medium (1TUP), and large (HSA) proteins. Energy gradient verified against numerical derivatives to tolerance 10⁻⁶.