The context: therapeutic mRNA design
The COVID-19 mRNA vaccines (BNT162b2 from Pfizer, mRNA-1273 from Moderna) demonstrated that synthetic mRNA is a viable therapeutic platform. But designing an optimal mRNA sequence is not trivial: the same protein can be encoded by trillions of different mRNA sequences (thanks to the degeneracy of the genetic code), and each has different biophysical properties.
The state of the art is LinearDesign (Zhang lab, Nature 2023), which simultaneously optimizes secondary structure (MFE, minimum free energy) and codon usage (CAI). It is an elegant algorithm. But it is missing a dimension.
The missing dimension: nearest-neighbor stacking
When two nucleotides sit next to each other in the mRNA, they interact through stacking forces (van der Waals, hydrophobic interactions). The energy of that interaction depends on the identity of both nucleotides — not just the one you are looking at. This is nearest-neighbor thermodynamics (SantaLucia 1998, Turner 2004).
The stacking ΔG profile sweeps along the mRNA position by position and computes that energy for each pair of adjacent nucleotides. It is local context information that metrics like GC3 (which only looks at the third codon position) cannot capture.
Stacking ΔG vs secondary structure MFE: R² = 0.144. They are independent dimensions. Nearest-neighbor stacking captures information that secondary structure does not contain, and vice versa.
Key results
Demonstrated complementarity: Stacking ΔG vs MFE has an R² of only 0.144 — they are nearly independent dimensions. Optimizing one does not automatically optimize the other.
Expression correlation: ΔG correlates with CSC (Codon Stability Coefficient) at the codon level (r = -0.501) and much more strongly at the intra-gene window level (r = -0.797). The thermodynamic signal predicts mRNA stability.
Universality: In 9 out of 9 genes analyzed, the AT → native → GC optimization maintains the pattern ΔG(AT) > ΔG(native) > ΔG(GC), with a mean effect of -0.416 kcal/mol.
Residual contribution: After controlling for GC3 (which already explains much), ΔG still contributes r = -0.378 (p = 6.8e-30), with ΔR² = +0.050. The +5% of explained variance is the fine-tuning when GC3 is already saturated — the fine adjustment that makes the difference between a good vaccine and an excellent one.
The dual optimizer
Two optimization modes implemented as proof-of-concept:
Greedy with lookahead: At each position, evaluates all possible synonymous codons and selects the one that minimizes ΔG locally, with a lookahead window to avoid local optima. Time: 0.1 seconds for the 1,273 codons of the spike protein.
Beam search with integrated MFE: Maintains multiple candidates in parallel (beam_width=5) and every 10 codons evaluates secondary structure with RNAfold. Integrates MFE into the beam pruning decision. Time: 13 seconds for the same sequence.
| Sequence | Mean ΔG | MFE/nt | GC% |
|---|---|---|---|
| Native SARS-CoV-2 | -1.181 | -0.117 | 30.1% |
| BNT162b2 (Pfizer) | -1.481 | -0.268 | 56.8% |
| mRNA-1273 (Moderna) | -1.542 | -0.313 | 62.0% |
| Greedy (ΔG only) | -1.593 | -0.345 | 63.9% |
| Beam search (ΔG+MFE) | -1.592 | -0.370 | 63.9% |
Both modes outperform both commercial vaccines in ΔG and MFE simultaneously. Beam search significantly improves MFE compared to greedy (-0.370 vs -0.345) with virtually identical ΔG — demonstrating that integrating secondary structure into the decision adds real value.
Positioning vs LinearDesign
LinearDesign optimizes MFE + CAI through dynamic programming. It is the undisputed state of the art. But it operates in a two-dimensional space.
QMetrika's optimizer adds a third dimension: nearest-neighbor stacking. Given that R²(ΔG, MFE) = 0.144, this dimension is mostly orthogonal — it is not redundant with what LinearDesign already optimizes. The proposal is not to replace LinearDesign, but to complement it with thermodynamic information it is not using.
The difference between a good vaccine and an excellent one may lie in a dimension that nobody is looking at. Nearest-neighbor stacking is that dimension.
Intellectual property
The ΔG engine and optimizer are covered by patent P202630522 (OEPM, April 2026). International PCT filing in April 2027. The optimizer code is not public — it is protected by the patent.
Next steps
Experimental validation (Oct 2026 - Mar 2027): 3-5 constructs in HEK293 — native, greedy-ΔG, beam-ΔG+MFE, GC-max control — measuring protein expression (luciferase reporter) and mRNA stability (half-life). The goal is to correlate predicted ΔG with measured expression and stability.
Pareto frontier: Sweep beam_width (3, 5, 10, 20, 50) and map the ΔG vs MFE trade-off. Is there an optimal point? Or is the frontier smooth and gene-dependent?