bioRxivBioinformaticsPreprint
BindCraft: one-shot design of functional protein binders
Martin PačesaiD, Lennart NickeliD, Christian SchellhaasiD, Joseph H. SchmidtiD, Ekaterina Pyatova, Lucas KisslingiD, Patrick BarendseiD, Jagrity ChoudhuryiD, Srajan KapooriD, Ana Alcaraz‐SernaiD, Yehlin ChoiD, Kourosh H. Ghamary, Laura VinuéiD, Brahm J. YachniniD, Andrew M. WollacottiD, Stephen BuckleyiD, Adrie H. WestphaliD, Simon LindhoudiD, Sandrine GeorgeoniD, Casper A. GoverdeiD, Georgios N. HatzopoulosiD, Pierre GönczyiD, Yannick D. MülleriD, Gerald SchwankiD, Daan C. SwartsiD, , Bruno E. CorreiaiD
In-depth summary
Claude Opus 5.5 · xhigh effort · 17 Oct 2026 · from the full text of version 3 (25 Apr 2025)
Hallucinating binders by backpropagating through AlphaFold2 weights gave, across 12 targets, binders in 10-100% of the designs tested per target, mostly at nanomolar affinity.
Physics-based binder design typically succeeds in under 0.1% of designs, and deep-learning pipelines built on RFdiffusion still need thousands to tens of thousands of candidates plus large in vitro screens, which puts de novo binders out of reach for labs without screening facilities. BindCraft optimises binder sequence and backbone directly through the AlphaFold2 network, allows the target to move during design, and is reported to need only a handful of purified designs per target.
Key results
ShownAcross 12 targets spanning cell-surface receptors, allergens, a de novo beta barrel, a centriole protein and the nucleic-acid-binding grooves of two nucleases, testing 6-53 designs per target produced binders, mostly at nanomolar affinity and without library screening or affinity maturation.
Binders found per designs purified: PD-1 13 of 53 (best apparent Kd below 1 nM, exact value not determined), PD-L1 7 of 9 (615 nM), IFNAR2 3 of 9 (260 nM), CD45 4 of 16 (14.7 nM), BBF-14 6 of 11 (20.9 nM), Der f7 4 of 10 (12.8 nM), Der f21 4 of 7 (793 nM), Bet v1 2 of 7 (120 nM); CrSAS-6 binder4 bound at 5.7 micromolar (monomer) and 4.2 micromolar (dimer). Stated range 10-100%, mean 46.3%. · Targeting cell surface receptors; Binder design beyond known and natural binding…; Blocking immunogenic epitopes of common…; Discussion
ShownCrystal structures of three designed complexes reproduced the predicted fold and binding mode, including a binder against a de novo beta barrel with no sequence homolog in the Protein Data Bank; cryoEM of the Cas9 complexes supported only docking-level validation.
Backbone Calpha RMSD to the design model: 1.7 A for BBF-14/binder4 and 1.7 A for Der f7/binder2 (crystal forms at 2.2 A and 3.0 A resolution); 3.1 A for Der f21/binder10 (2.6 A resolution), attributed to a different rotamer of an interface tyrosine. Deposited as 9HAC, 9HAD, 9HAE, 9HAF. For apo SpCas9 the maps showed binder density at REC1 but no atomic model could be built. · Binder design beyond known and natural binding…; Blocking immunogenic epitopes of common…; Modulating the function of large multi-domain…; Data availability
ShownBinders aimed at nucleic-acid-binding sites worked as artificial inhibitors: all six anti-REC1 designs bound apo SpCas9, the designed binders lowered Cas9 editing in cells, and two of twelve anti-CbAgo designs slowed guide-directed DNA cleavage.
SpCas9 binder3 and binder10 showed apparent affinity around 300 nM by SPR; co-transfected into HEK293T cells with Cas9 and an NSD2 sgRNA they cut editing more than the natural AcrIIC2, while AcrIIA2 and AcrIIA4 nearly abolished it. CbAgo kcat dropped from 0.004 /s to 5e-5 /s (80-fold) with binder2 and 9.8e-5 /s (40-fold) with binder3; binder2 bound at Kd 5 nM (BLI) and guide DNA displaced it. · Modulating the function of large multi-domain…; SpCas9 gene editing
ShownSplicing designed miniproteins into a capsid loop redirected AAV6 transduction toward cells carrying the targeted receptor, with candidates picked straight from the design pipeline in a cell assay rather than by biochemical prescreening.
HER2- and PD-L1-targeting binders were inserted between residues 497 and 498 of VR-V in an AAV6-cmv-GFP capsid mutated to remove heparin and sialic acid binding. A supernatant-transfer screen gave one HER2-directed and four PD-L1-directed variants with enhanced specificity for HEK293 cells stably overexpressing the receptor; an antibody against the same receptor site blocked transduction by the PD-L1 variant. · AAV re-targeting for cell-type specific gene…
SuggestedOne designed binder against the birch allergen Bet v1 partially blocked patient IgE from binding the allergen, though less completely than a three-antibody cocktail.
Blocking ELISA with sera from three birch-allergic patients: the REGN5713/5714/5715 cocktail blocked up to 90% of Bet v1 binding to IgE at low concentrations, while the single binder2 blocked up to 50% in two of the three donors. Binder2 bound Bet v1 at 120 nM (SPR), gave the expected 27.8 kDa complex by SEC-MALS, and competed with REGN5713 for its epitope. · Blocking immunogenic epitopes of common…; Birch allergen blocking assay
Methods, data and reproducibility
AlphaFold2 (AF2) multimer weights in ColabDesign hallucinate binder sequence and backbone together, backpropagating a weighted loss (pLDDT, i_pTM, predicted alignment error, contacts, radius of gyration, helicity) onto the sequence while the target is re-predicted each iteration; core and surface residues are then redesigned with soluble ProteinMPNN, re-predicted by AF2 monomer and filtered on AF2 and Rosetta scores.
- Design targets: PD-1, PD-L1, IFNAR2, CD45 extracellular domain, soluble claudin-1 analogues, de novo beta barrel BBF-14, CrSAS-6, Der f7, Der f21, Bet v1, SpCas9, CbAgo · multiple (human, dust mite, birch, C. reinhardtii, S. pyogenes, C. butyricum) · n = 12 targets
- Sera from birch-allergic donors with high anti-Bet v1 IgE titres, used in the blocking ELISA · human · n = 3 patients
- X-ray structures of designed binder-target complexes deposited in the Protein Data Bank · n = 4 structures · 9HAC, 9HAD, 9HAE, 9HAF
- Amplicon sequencing of NSD2 editing in HEK293T cells co-transfected with SpCas9 plus designed binders or natural anti-CRISPR proteins · human
- HEK293 lines stably expressing HER2 or PD-L1 for AAV transduction, plus Sf9 cells expressing CLDN1 or CLDN4 for the enterotoxin cytotoxicity assay · human
- Methods & tools
- BindCraft, AlphaFold2 multimer and monomer, ColabDesign, ProteinMPNN (soluble weights, MPNNsol), Rosetta FastRelax and InterfaceAnalyzer, PyRosetta, cryoSPARC v4.5.3, autoPROC, Phaser, Phenix.refine, ChimeraX, CRISPResso2
- Reagents & environment
- Design code is stated to be on GitHub under an MIT licence with a Colab notebook; analyses used cryoSPARC v4.5.3, CRISPResso2, Python 3.9 for SPR fitting and the R package minpack.lm. No BindCraft version or container is given.
- Assay conditions & compute
- Backpropagation needs a large-memory GPU: a 500-residue target-binder complex takes roughly 30 Gb, sometimes forcing large targets to be trimmed or split. Sampling about 300-3000 trajectories is suggested to accumulate 100 designs passing filters.
- Reproducibility gaps
- The GitHub, Colab notebook and structural-model links are missing from the extracted text, so no URLs can be listed. CryoEM maps were still awaiting deposition at the time of writing.
Limitations (6)
- Design requires a large-memory GPU (about 30 Gb for a 500-residue complex), so large proteins and complexes sometimes have to be trimmed or split. · stated by the authors
- Filtering with AF2 monomer in single-sequence mode can reject genuine high-affinity binders and still passes false positives; AF2 is also reported to be insensitive to the effect of point mutations, which matters at interfaces. · stated by the authors
- Designs are ranked by the i_pTM score, which behaves as a binary predictor of binding but does not track affinity, so candidates cannot be ordered by expected Kd. · stated by the authors
- The designs are synthetic and fairly large (60-240 residues), leaving immunogenicity and delivery as open questions for therapeutic use. · stated by the authors
- Success rates are expected to vary with the target and the chosen site, and the claudin binders failed to protect CLDN4-expressing cells, attributed to the enterotoxin's roughly 400-fold higher affinity for CLDN4. · stated by the authors
- Comparisons with RFdiffusion and AlphaProteo are narrative rather than a matched head-to-head experiment, and with 6-53 designs tested per target the per-target success rates rest on small counts. · noted in this summary
Read this if you design protein binders and want the per-target hit rates, affinities, structures and failure modes of an AlphaFold2-hallucination pipeline meant to work without high-throughput screening.
- Citations
- 111
Abstract
Protein-protein interactions (PPIs) are at the core of all key biological processes. However, the complexity of the structural features that determine PPIs makes their design challenging. We present BindCraft, an open-source and automated pipeline for de novo protein binder design with experimental success rates of 10-100%. BindCraft leverages the weights of AlphaFold21 to generate binders with nanomolar affinity without the need for high-throughput screening or experimental optimization, even in the absence of known binding sites. We successfully designed binders against a diverse set of challenging targets, including cell-surface receptors, common allergens, de novo designed proteins, and multi-domain nucleases, such as CRISPR-Cas9. We showcase the functional and therapeutic potential of designed binders by reducing IgE binding to birch allergen in patient-derived samples, modulating Cas9 gene editing activity, and reducing the cytotoxicity of a foodborne bacterial enterotoxin. Lastly, we utilize cell surface receptor-specific binders to redirect AAV capsids for targeted gene delivery. This work represents a significant advancement towards a "one design-one binder" approach in computational design, with immense potential in therapeutics, diagnostics, and biotechnology.
- organism: multiple species
- assay: protein structure
- assay: protein sequence
- research focus: protein design
- research focus: software / tool
- approach: deep learning
- approach: experimental assay
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