Computational pharmacogenomics, research pipeline
Predicting how a genetic variant changes an enzyme's activity.
PepFold is a variant-structure pipeline for pharmacogenomics. It takes a coding variant in a drug-metabolizing gene, folds the protein, and predicts how much the variant shifts the enzyme's activity. The signal comes from two channels: a stability estimate that tracks how much protein is made, and a sequence-likelihood score that tracks activity and transfers across genes.
The problem PepFold is built around
Clinical pharmacogenomics guidelines assign one activity score per allele. A patient carrying a variant is labelled a poor, intermediate, normal, or ultrarapid metabolizer. That single label then sets the dose for every drug the enzyme touches.
The measured biology does not fit one number. The same variant can hit one drug and spare another:
OCT1 p.F244A
- metformin
- 15%
- sumatriptan
- 115%
- pirenzepine
- ~800%
activity vs wild-type
CYP2D6*10
- nortriptyline
- 1.3%
- dextromethorphan (O)
- 1–5%
- dextromethorphan (N)
- 47%
activity vs wild-type
UGT1A9 p.M33T
- propofol
- 0.5%
- mycophenolic acid
- 102%
activity vs wild-type
These are measured values from the literature, not PepFold outputs. Same protein, same mutation, up to three orders of magnitude apart depending on the drug. A single per-allele score cannot represent that. Neither can PepFold, yet.
What works today
- Predicting a variant's overall shift in enzyme activity, from structure and sequence, transferable between genes rather than fit to one.
- A stability channel (ThermoMPNN ΔΔG on folded structures) that tracks protein abundance, the part of loss-of-function that is the protein not being made.
- A sequence-likelihood channel (ESM masked-marginal) that carries the activity signal. Its calibration slope sits near 1. The output lands on a dose scale instead of only ordering variants.
What it does not do
- Predict the per-drug differences shown above. On the current features, the substrate-specific signal measures null, cleanly, across ten model families.
- The specificity is real in the data. It is not recoverable from whole-protein sequence embeddings plus ligand descriptors. The methodology page says where that null came from and which experiment would settle it.
- Nothing here is a clinical tool. It is computational research analysis, not for treatment decisions.
The structural pipeline
Public data in, a folded structure and two learned channels out. This is what feeds the predictor.
ClinVar
Variant annotation
UniProt / PDB
Target and structure mapping
ESMFold
Structure prediction
ThermoMPNN
ΔΔG stability channel
ESM-2
Sequence-likelihood channel
Validation in progress
The test is CYP2D6 and tamoxifen: predicting how coding variants shift the formation of endoxifen, the active metabolite. The held-out endpoint is firewalled out of training. The analysis was pre-registered and its protocol hashed. The result counts whichever way it lands.
It is not concluded. The signal is coherent on the part of the range the model can represent, and weaker across the full set. That is why the protocol stays frozen and the outcome stays open.
Get in touch
Questions, or working on pharmacogenomic variant effects yourself? Write.
[email protected]PepFold is a computational research tool. Outputs are not experimentally validated and are not for clinical decisions. Methodology · Legal