1 Target Information
Whatever you type, this step ends with one thing: a 3-D structure of the target, which it hands to Step 2. Three paths get there — name → id: a protein name is resolved against UniProt (hit the search button and pick a match); id → structure: a UniProt accession or PDB ID is looked up and its structure downloaded (best experimental PDB, else the AlphaFold DB model); sequence → structure: a bare sequence has nothing to look up, so it is folded here into a structure.
If the target has no experimental co-complex in the PDB, give its binding partner — a protein name or gene name, a UniProt accession, an amino-acid sequence, or an uploaded PDB / sequence file (the same input kinds as the target in Step 1). A name/sequence is turned into a structure with the selected Structure predictor (Boltz-2 / AF2); PepDesigner then predicts the target+partner complex and reads the hotspots off its interface with PyMOL. Needed whenever there is no experimental complex — otherwise there is no interface to find hotspots on. (Adds a GPU prediction run; de novo / auto mode only.)
2 Hotspot identification
3 Peptide design (BindCraft)
No. designs — how many de novo peptide candidates BindCraft should accept (passing AF2/Rosetta filters). More designs explore more of the space but take longer on the GPU.
Peptide length — binder length range (residues) BindCraft hallucinates. Longer binders fold more stably (more designs pass the Binder_RMSD ≤ 2.5 check); very short ones often fail to fold consistently, very long ones cost more GPU. 65–150 (a small protein binder, not a short peptide) is the default and matches BindCraft's own original design regime (see settings_target/PDL1.json) — if a run finds no accepted design, retries automatically try a bigger binder rather than a shorter one.
Max GPUs — upper bound on GPUs to shard across (leave blank to auto-use all eligible, one shard per design). BindCraft prefers idle (empty) GPUs — low utilization plus enough free VRAM — and only shares a busy GPU as a fallback when not enough free ones are available. The running step shows how many GPUs (and which) are in use, live.
Per-epitope design — run a separate BindCraft design per spatial epitope (from the PyMOL interface annotation) and merge the results, instead of one design against all hotspots. Targets each binding patch independently; GPU cost scales with the number of epitopes. Falls back to a single run when fewer than 2 epitopes are found.
4 MD & binding free energy
Number of hits — how many of the top-ranked candidates to carry forward into docking / MD and into the final report.
5 Closed-loop redesign
Loop iters — closed-loop optimization. 1 = single pass. >1 repeats redesign → AF2 refold → MD/MM-PBSA until no penalty residues remain or the cap is reached (each extra iteration adds GPU hours).
⚠ De novo design occupies a GPU for hours and runs MD per accepted design. Start it only when a GPU is free.
Pipeline progress
Start an analysis to see step-by-step progress here.
Results
Ranked candidates appear here when the run completes.