A protein-language-model engine for directed evolution
Describe what you want in plain language. Turing fetches the gene, scores every mutation, and hands you a ranked library — ready to order.
The output
Every run produces something you can order, clone, or cut — mapped, scored, and simulated by the engine, not drawn by hand.
The conversational core
Turing drives every tool in plain language — fetch a gene, evolve it, design guides, check primers. It runs the right one, in the right order, and remembers what it found.
The method
Calibration
Every score is a prediction — verify it on the bench, and the next round comes back sharper. Log what you measured; Turing fits a model on YOUR results, tells you exactly which of its guesses were right, and re-ranks accordingly. Nothing here is a black box; each number traces to a published method.
Your individual sequences stay private — never shared, exposed, or reproduced. Only anonymous, aggregated signals improve the models (how it works). You keep every right to what you design.
The Commons
Every measurement you log — de-identified, never your sequence — sharpens the model for everyone who comes after you. A closed system only improves when its own team retrains it. This one compounds on its own.
Checked against Stiffler et al. 2015 Cell, Melamed et al. 2013 RNA, and McLaughlin et al. 2012 Nature — the same public benchmarks the field uses to grade protein language models. Every number here comes from a live run against that data, never hand-entered.