What it is
Designs a binder by running a structure predictor backwards: it starts from a soft, blurred sequence and nudges it one gradient step at a time until the fold network believes the result binds your target. The same machinery does two jobs — small de novo binders, and a paired heavy + light scFv (a single-chain antibody fragment) with all six binding loops designed together, which no other tool here does. Designs from this method have been taken to the bench against PDGFRB, EGFR, PD-L1, CD45 and CTLA4, reaching nanomolar affinity. ESMFold2 design, Candido et al., bioRxiv 2026, built on the ESMC protein language model.
When to use
- You want a paired heavy + light scFv with all six binding loops designed jointly against your target. No other tool here does this.
- Your target has gone quiet under RFdiffusion and you want a method that searches differently — a different prior often surfaces different binders.
- You want to compare designed loops across three humanised frameworks that have been to the bench (trastuzumab, atezolizumab, ocankitug).
- You want to calibrate your own wet-lab setup against one of the five targets from the paper before spending on a novel one.
Prerequisites
- Target sequence: pick one of five paper-validated presets or paste a single chain (30 to 800 aa).
- Pick minibinder mode or scFv mode. For scFv, pick a framework (trastuzumab, atezolizumab, or ocankitug).
- No PDB required. The gradient loop is sequence-only.
Input glossary
- Preset. De novo minibinder generates a free 60 to 200 aa scaffold with an isoelectric-point filter (pI < 6). scFv designs all six CDRs on a locked humanized framework. Same model, different binder factory.
- Target. Pick one of five paper-validated presets (CD45, CTLA4, EGFR, PD-L1, or PDGFR, with sequences from UniProt cropped to the relevant ectodomain) or paste your own protein sequence (30 to 800 aa, canonical amino acids only).
- Binder framework. scFv mode only. Locks the framework backbone and sequence; the gradient loop only mutates the six CDR regions. Trastuzumab (anti-HER2 IgG1, humanized), atezolizumab (anti-PD-L1 IgG1, humanized), or ocankitug (humanized IgG1). All three are clinically validated frameworks.
- Starting seed. Integer seed for the soft-sequence initialization. Different seeds yield different designs. When Seeds to run is greater than 1 this is the first seed in the sweep; the orchestrator runs
[seed, seed + n) in parallel.
- Seeds to run. Number of parallel seeds to sweep (1 to 64). Each seed gets its own H100 worker, all run in parallel, so a 16-seed sweep finishes in about the same wall-clock as one seed. Results from every seed merge into one globally-ranked table. Use this when you need to build a candidate library against a target. GPU time scales with seeds, not with batch size: each seed is a separate container, and a seed's designs share one.
- Batch size. Designs produced per gradient run (1 to 6). They share one H100 container, so a higher batch adds no container — but you pay for the GPU time your run uses, and a bigger batch runs longer: measured wall-clock grew a little faster than in proportion to the batch. (It used to claim a batch of 6 was free of both; two production runs and the GPU-seconds bill disproved that.) Default 3. Single-design runs often return
drop after the iPTM and pI gates. Bump to 6 for first-pass exploration when you can wait for the longer run; drop to 1 only when you already know the target gives clean hits.
Per-design table with designed sequence, iPTM, distogram iPTM proxy (or CDR distogram iPTM proxy for scFvs), final loss, isoelectric point, source seed, and predicted complex PDB. Strict-pass classification surfaces designs worth ordering (minibinder: iptm ≥ 0.75 AND pI < 6; scfv: cdr_distogram_iptm_proxy ≥ 0.5 AND iptm ≥ 0.75). Sweep mode (Seeds to run > 1) merges every seed's designs into one globally-ranked table.
References
