Binder Design

Binder Design Tool Selection

Which tool wins

No single tool is best for every target. Hit-rate is strongly target-dependent, so choose by target type, what you want to control, and available compute.

The clearest signal comes from head-to-head competitions where many methods design against the same target. On the Adaptyv Nipah de novo target, the public results show:

| Method | Tested | Binders | Hit-rate | |--------|--------|---------|----------| | Mosaic (gradient, multi-model) | 9 | 8 | 89% | | ProteinMPNN hybrid | 28 | 7 | 25% | | RFdiffusion | 60 | 13 | 22% | | BindCraft | 98 | 7 | 7% | | BoltzGen | 182 | 6 | 3% |

Mosaic had the highest hit-rate here, but on a small, expert-tuned sample. The ranking shifts on other targets, and that target-dependence is true of every method (BoltzGen, Boltz, BindCraft, Mosaic). You cannot know a priori which will win on a new target, so this is not a fixed leaderboard.

Because of that, choose a starting point by cost and effort to a binder, not by assuming a method has the best hit-rate. BoltzGen is the suggested default because it is turnkey and all-atom, so it gets you testable designs fastest with the least setup. Mosaic is the high-ceiling option when you can invest time tuning the objective. On a hard or important target, running more than one method in parallel is reasonable.

De novo binder design?
│
├─ Lowest cost/effort to testable designs → BoltzGen (default)
├─ Hard/important target, can invest tuning → Mosaic (gradient, multi-model)
├─ Ligand / small-molecule binding → BoltzGen (all-atom)
├─ Diversity / exploration → RFdiffusion + ProteinMPNN
├─ End-to-end with built-in validation → BindCraft
└─ Antibody / nanobody (VHH) → germinal skill (also mber, iggm in biomodals)

Tool comparison

| Tool | Strengths | Weaknesses | Best for | |------|-----------|------------|----------| | BoltzGen | All-atom, single-step, turnkey | One model in the loop; mid-range cost per design | Lowest-effort default, ligand binding | | Mosaic | Composable multi-model objective, won hard head-to-heads | Needs tuning, local JAX only | Hard or important targets, expert use | | BindCraft | End-to-end, built-in AF2 validation | Less diverse | Production campaigns | | RFdiffusion | High diversity | Requires ProteinMPNN; not in biomodals | Exploration, diversity | | Germinal | Antibody and nanobody formats | Finicky | scFv / VHH design |

Compute cost per design

Adaptyv's own tests of these models showed the following compute cost per accepted design, averaged across 7 targets (it varies several-fold by target):

| Method | Cost per design | |--------|-----------------| | RSO | ~$0.15 | | RFdiffusion | ~$0.25 | | Mosaic | ~$0.55 | | ESMFold2 inversion | ~$0.85 | | mBER | ~$1.40 | | Germinal | ~$1.60 | | BoltzGen | ~$1.80 | | BindCraft | ~$2.90 |

Per-design compute cost is not the same as cost to a binder, which also depends on the hit-rate on your target. The gradient methods (RSO, Mosaic) are cheap per design but need setup and tuning; BoltzGen and BindCraft cost more per design but are turnkey, so their advantage is low human effort rather than lowest compute cost.

Compute vs effort tradeoff

• Lowest human effort: BoltzGen needs no tuning and runs through biomodals. Good

first pass and good for ligand binding.

• Highest ceiling on a hard target: Mosaic, given time to design and tune the

objective. It runs locally on a JAX GPU rather than through biomodals, and is cheap per design.

• Whatever the generator, validate with boltz or chai and rank with ipsae.

Other biomodals-backed options: modal_rso.py (Rejection Sampling Optimization, an AlphaFold-based gradient method) for minibinders, and modal_mber.py for VHH nanobodies.

Example pipeline: BoltzGen → Chai → QC

BoltzGen provides all-atom design with built-in side-chain packing. This is one turnkey path; swap in Mosaic, RFdiffusion, or BindCraft depending on the target.

Target → BoltzGen → Validate → Filter
 (pdb)  (all-atom)   (chai)     (qc)

1. Target preparation

# Fetch structure from PDB
# Use pdb skill for guidance

• Trim to binding region + 10A buffer
• Remove waters and ligands
• Renumber chains if needed

2. Hotspot selection

• Choose 3-6 exposed residues
• Prefer charged/aromatic residues
• Cluster spatially (within 10-15A)

3. Design with BoltzGen

First, create a YAML config file (e.g., binder.yaml):

entities:
  - protein:
      id: B
      sequence: 70..100

  - file:
      path: target.cif
      include:
        - chain:
            id: A
      binding_types:
        - chain:
            id: A
            binding: 45,67,89

Then run:

modal run modal_boltzgen.py \
  --input-yaml binder.yaml \
  --protocol protein-anything \
  --num-designs 50

Why BoltzGen?

• All-atom output (no separate ProteinMPNN step needed)
• Better for ligand/small molecule binding
• Single-step design (backbone + sequence + side chains)

4. Alternative: RFdiffusion Pipeline

For maximum diversity or when backbone-only is preferred:

# Step 1: Backbone generation (RFdiffusion, run from the official repo)
python run_inference.py \
  inference.input_pdb=target.pdb \
  contigmap.contigs=[A1-150/0 70-100] \
  ppi.hotspot_res=[A45,A67,A89] \
  inference.num_designs=500

# Step 2: Sequence design
modal run modal_ligandmpnn.py \
  --input-pdb backbone.pdb \
  --params-str "--number_of_batches 16 --temperature 0.1"

5. Validation

modal run modal_chai1.py \
  --input-faa sequences.fasta \
  --out-dir predictions/

6. Filtering

Apply standard thresholds:

• pLDDT > 0.80
• ipTM > 0.50
• PAE_interface < 10
• scRMSD < 2.0 A

See protein-qc skill for details.

Number of designs

| Stage | Count | Purpose | |-------|-------|---------| | Backbone generation | 500-1000 | Diversity | | Sequences per backbone | 8-16 | Sequence space | | AF2 predictions | All | Validation | | After filtering | 50-200 | Candidates | | Experimental testing | 10-50 | Final selection |

Common mistakes

Wrong hotspots

• Using buried residues
• Too many hotspots (over-constrain)
• Wrong chain/residue numbers

Insufficient diversity

• Too few designs generated
• Low temperature in ProteinMPNN
• Not exploring multiple backbones

Poor target preparation

• Including full protein instead of binding region
• Missing important structural features
• Wrong protonation states

Timeline guide

| Step | Compute Time | |------|--------------| | RFdiffusion (500 designs) | 2-4 hours | | ProteinMPNN (8000 sequences) | 1-2 hours | | AF2 prediction (8000 sequences) | 12-24 hours | | Filtering and analysis | 1-2 hours |

Total: 1-2 days of compute

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: adaptyvbio
• Source: adaptyvbio/protein-design-skills
• License: MIT

Install and usage instructions live in the source repository linked above.