EnergyFingerprint: missense variant classification with thermodynamic profiles

The problem

Every human genome contains thousands of missense variants — single nucleotide substitutions that change an amino acid in the protein. Most are benign, but some cause serious disease. Genetic diagnostic laboratories need to classify them quickly and accurately, and existing predictors (CADD, REVEL, AlphaMissense) operate exclusively at the protein level: evolutionary conservation, 3D structure, physicochemical properties of the amino acid.

None of them looks at what lies beneath: the mRNA that encodes that protein. And the mRNA has its own physics — stacking energy between adjacent nucleotides, secondary structure, local stability — that a mutation can alter even when the amino acid change seems innocuous.

The idea: two orthogonal axes

EnergyFingerprint combines two signals into a single 11-channel × 128-nucleotide tensor per variant:

Biophysical axis (channels 1-4): mRNA stacking thermodynamic profiles computed with nearest-neighbor parameters (SantaLucia 1998, Turner 2004). They capture the Gibbs free energy (ΔG) between adjacent dinucleotides — local context information that simple metrics like GC3 cannot capture.

Evolutionary axis (channels 5-11): ESMC-300M protein language model (EvolutionaryScale, successor to ESM-1v). Masked marginal scoring: the mutated position is masked, and the log-likelihood ratio between the native and mutant amino acid is measured. It captures the evolutionary pressure that has acted on that position for millions of years.

The key is not that each axis is better than competitors on its own — it is that the two axes are orthogonal. One operates at the nucleotide level, the other at the protein level. Together they see what neither can see alone.

Results: 42 validated genes

July 2026 update: The panel has expanded from 8 to 42 genes (12 trainable + 30 zero-shot only), with a mean zero-shot AUC of 0.876 across 41 evaluable genes. The evolutionary model has been migrated from ESM-1v to ESMC-300M (CNN AUC 0.961 on BRCA1). Full results in the TECH-2 technical report.

The transferability gradient

The most revealing finding is not the absolute AUCs, but the pattern that emerges when training on BRCA1 and predicting on the remaining genes without retraining (zero-shot):

Cancer ~0.9 > ABC channel ~0.78 > Globin ~0.71 > TM membrane ~0.64 > IDR ~0.43

This gradient is not noise — it reflects the thermodynamic grammar of each protein family. Globular proteins (BRCA1, TP53, PTEN) have informative ΔG profiles because the selective pressure on mRNA stability is strong. Proteins with intrinsically disordered regions (MECP2) have flat ΔG profiles — the thermodynamic signal is weak because there is no selective pressure on the mRNA physics.

This connects directly to the E1 hypothesis in the roadmap: the transferability gradient could be a functional protein classifier based exclusively on mRNA physics.

Technical architecture

The CNN is deliberately small (~228K parameters) to avoid overfitting on genetic datasets where samples are scarce. It uses CrossEntropyLoss with 2 outputs (not BCEWithLogitsLoss with 1), class weights [1.0, n_neg/n_pos] for imbalanced datasets, and GradCAM1D for explainability — the model indicates which mRNA regions contributed to its prediction.

The shared engine (energy.py) computes the nearest-neighbor ΔG profiles. For proteins longer than 1022 amino acids, ESMC-300M requires windowing.

Competitors and differentiation

CADD integrates multiple genomic annotations into a single score. REVEL ensembles 13 predictors. AlphaMissense uses AlphaFold2 representations. All operate at the protein level.

EnergyFingerprint is the first to combine mRNA thermodynamic signal with evolutionary signal from a protein language model. The orthogonality of the axes suggests that the information it contributes is complementary, not redundant — and that opens the door to ensembles where EF adds value to any existing predictor.