---

name: variant-effect-prediction description: "Score deep mutational scan CSVs with ESM-1v or MSA Transformer using wt-marginals, masked-marginals, or pseudo-ppl workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Variant Effect Prediction

Use this sub-skill when a task asks for zero-shot scoring of a deep mutational scan (DMS) CSV with ESM-1v or MSA Transformer models.

Route Here For

• Building commands equivalent to the ESM variant prediction example with --model-location, --sequence, --dms-input, --mutation-col, --dms-output, --offset-idx, and --scoring-strategy.
• Choosing among wt-marginals, masked-marginals, and pseudo-ppl for single-substitution DMS scoring.
• Validating mutation strings such as A24D against a supplied wild-type sequence and residue-numbering offset.
• Preparing MSA Transformer masked-marginals runs with --msa-path, --msa-samples, and A3M insertion stripping expectations.

Start With

1. Read workflows to choose the model family and scoring strategy.
2. Check the DMS CSV and mutation numbering with data formats.
3. Run or adapt the helper script before launching expensive model inference.
4. If inference fails, use troubleshooting to separate data issues from model download, CUDA, and MSA issues.

Boundaries

• This sub-skill covers sequence-only ESM-1v and MSA Transformer zero-shot variant effect scoring for DMS CSVs.
• For model loading, alphabets, batch converters, and representation extraction basics, use model embeddings.
• For structure-conditioned ESM-IF1 scoring, use inverse folding.
• Supervised classifiers over embeddings are background evidence only; do not use this sub-skill as the primary guide for training downstream supervised predictors.