BY-Prot -- De Novo Protein Binder Design (PXDesign)

You are an expert at designing de novo protein binders using PXDesign via the pxdesign CLI. This skill covers YAML config construction, CLI invocation, output parsing, and result interpretation. PXDesign achieves 17-82% hit rates for de novo binder design.

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1. Prerequisites

Environment Variables (all four required)

Variable	Value	Purpose
PROTENIX_DATA_ROOT_DIR	$PROTEUS_PROT_DIR/release_data/ccd_cache	CCD chemical component dictionary cache
TOOL_WEIGHTS_ROOT	$PROTEUS_PROT_DIR/tool_weights	PXDesign model weights
CUTLASS_PATH	$HOME/cutlass	NVIDIA CUTLASS kernel library
CUDA_HOME	Path to CUDA toolkit (e.g. conda env with nvcc)	Required by DeepSpeed for GPU architecture detection

Tool Path

$PROTEUS_PROT_DIR -- all PXDesign files, weights, and release data live here.

PATH Requirements

PATH must include the protenix conda env's bin directory for JAX/XLA compilation. The Protenix evaluation filters use JAX, which requires ptxas (PTX assembler) on PATH. ptxas is typically in the protenix env, not the pxdesign env.

export PATH=/path/to/conda/envs/protenix/bin:$PATH

Hardware

Requires a CUDA-capable GPU with bf16 support. Recommended: A100 40GB+ for extended preset. Preview preset fits on 24GB GPUs.

GPU Architecture Note: Blackwell GPUs (sm_100+, e.g. RTX PRO 6000) must use --use_fast_ln False. The fastfold LayerNorm CUDA kernels only compile for sm_70/80/86/90 and will fail on sm_100+.

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Pre-flight Validation (MANDATORY before first design launch)

Run ALL checks before the first pxdesign command. Do NOT launch and debug one error at a time.

# 1. GPU architecture
GPU_ARCH=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader | head -1)
echo "GPU arch: sm_${GPU_ARCH/./}"
# If >= 10.0: MUST use --use_fast_ln False

# 2. CUDA toolkit
echo "nvcc: $(which nvcc 2>/dev/null || echo 'NOT FOUND — set CUDA_HOME')"
echo "ptxas: $(which ptxas 2>/dev/null || echo 'NOT FOUND — add protenix env to PATH')"

# 3. CUDA_HOME
echo "CUDA_HOME: ${CUDA_HOME:-NOT SET}"

# 4. Chain IDs in target CIF
python3 -c "
from Bio.PDB import MMCIFParser
s = MMCIFParser(QUIET=True).get_structure('t', 'TARGET.cif')
for chain in s[0]:
    print(f'  Chain {chain.id}: {len(list(chain.get_residues()))} residues')
"

# 5. Hotspot residues exist in chain
# Verify each hotspot residue number exists in the target chain

If ANY check fails, fix it before launching. After 1 failed pxdesign launch, switch to BoltzGen protein-anything as fallback.

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2. When to Use proteus-prot

User wants a BINDER that is NOT an antibody/nanobody
|
+-- Quick exploration or feasibility check?
|   --> preset: preview  (faster, fewer refinement steps)
|
+-- Production-quality designs for experimental testing?
    --> preset: extended  (full pipeline, higher quality)

Preset Comparison

Preset	Use Case	Speed	Quality
preview	Exploration, feasibility, quick iteration	Fast	Good -- suitable for triage
extended	Final designs for experimental validation	Slower	Best -- full refinement pipeline

Binder Length Guide

Target Size	Recommended binder_length	Notes
Small (<150 residues)	60-80	Shorter binders avoid steric clashes
Medium (150-400 residues)	80-120	Default 100 works well
Large (>400 residues)	100-150	Longer binders for larger interfaces
Flat epitope	+20 above default	More residues to create shape complementarity
Concave pocket	-20 below default	Compact binders fit pockets better

---

3. How to Run -- Write / Bash / Read Pattern

Step 1: Write YAML Config

Use the Write tool to create a YAML configuration file. See references/yaml-config-spec.md for the full specification.

Basic config (target with one chain, no hotspots):

target:
  file: "/path/to/target.cif"
  chains:
    A: "all"
binder_length: 100

Config with hotspots and crop ranges:

target:
  file: "/path/to/target.cif"
  chains:
    A:
      crop: ["1-116"]
      hotspots: [40, 50, 55, 99]
      msa: "./msa/chain_A"
    B: "all"
binder_length: 80

Write the config to a working directory:

Write tool -> /path/to/workdir/config.yaml

Step 2: Run PXDesign CLI via Bash

PATH=/path/to/conda/envs/protenix/bin:$PATH \
PROTENIX_DATA_ROOT_DIR=$PROTEUS_PROT_DIR/release_data/ccd_cache \
TOOL_WEIGHTS_ROOT=$PROTEUS_PROT_DIR/tool_weights \
CUTLASS_PATH=$HOME/cutlass \
CUDA_HOME=$CUDA_HOME \
pxdesign pipeline \
  --preset preview \
  -i /path/to/workdir/config.yaml \
  --N_sample 10 \
  --dtype bf16 \
  --use_fast_ln True \
  -o /path/to/workdir/output

All CLI flags:

Flag	Required	Default	Description
pipeline	Yes	--	Subcommand (always pipeline)
--preset	Yes	--	preview or extended
-i	Yes	--	Path to YAML config file
--N_sample	No	10	Number of design samples to generate
--dtype	No	bf16	Data type. Always use bf16
--use_fast_ln	No	True	Fast LayerNorm kernels. Use True for Ampere/Hopper (sm_80-sm_90). Use False for Blackwell (sm_100+)
-o	No	auto	Output directory. If omitted, PXDesign chooses one

Step 3: Read Output

The primary output is a CSV summary file at:

<output_dir>/design_outputs/<task_name>/summary.csv

If the expected path does not exist, search recursively for any summary.csv under the output directory.

Use the Read tool to load and parse the CSV. Key columns:

Column	Type	Description
rank	int	Design rank (1 = best)
name	str	Design identifier
sequence	str	Designed binder amino acid sequence
af2_opt_success	bool	AF2 optimization filter pass
af2_easy_success	bool	AF2-IG easy filter pass
ptx_success	bool	Protenix filter pass
ptx_basic_success	bool	Protenix basic filter pass
ptx_iptm	float	Protenix ipTM score (0-1, higher = better)
af2_binder_plddt	float	AF2 binder pLDDT confidence (0-1)
af2_complex_pred_design_rmsd	float	RMSD between predicted and designed complex (Angstroms)

Results are sorted by ptx_iptm descending. Present the top candidates in a table, highlighting which filters each design passed.

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4. YAML Config Quick Reference

Full specification in references/yaml-config-spec.md.

Required Fields

• target.file -- path to target structure (.cif or .pdb)
• target.chains -- dict of chain IDs to include
• binder_length -- integer, number of residues for the designed binder

Hotspot Format

Hotspot residues are specified as integers (residue numbers) under the per-chain config. The chain letter is determined by the YAML key:

chains:
  A:
    hotspots: [40, 50, 55]   # residues 40, 50, 55 on chain A

When the user provides hotspots in "A40, A50, B10" format, parse the chain letter from the first character and the residue number from the rest, then group by chain.

Crop Ranges

Crop ranges restrict which residues of a chain are included. Format is a list of "start-end" strings:

chains:
  A:
    crop: ["1-116", "200-250"]   # only include residues 1-116 and 200-250

---

5. Filter Thresholds

PXDesign applies built-in validation filters. See references/filter-thresholds.md for complete details.

Quick reference:

Filter	Key Thresholds	Stringency
AF2-IG easy	ipAE<10.85, ipTM>0.5, pLDDT>0.8, RMSD<3.5A	Standard
AF2-IG strict	ipAE<7.0, pLDDT>0.9, RMSD<1.5A	High
Protenix basic	ipTM>0.8, pTM>0.8, RMSD<2.5A	Standard
Protenix strict	ipTM>0.85, pTM>0.88, RMSD<2.5A	High

A design marked ptx_success=True passed Protenix strict. A design with ptx_basic_success=True but ptx_success=False passed basic but not strict.

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6. Common Mistakes

1. Missing environment variables. All four env vars must be set in the same Bash command. If any are missing, pxdesign will fail with import or file-not-found errors.

2. Using MCP function calls instead of CLI. This skill uses Write + Bash + Read. Always use the CLI directly -- do not call internal Python wrapper functions. The CLI is the only supported interface.

3. Wrong hotspot format in YAML. Hotspots are integers in the YAML, not strings like "A40". The chain letter comes from the YAML key, not the hotspot value. Wrong: hotspots: ["A40"]. Right: hotspots: [40].

4. Setting --use_fast_ln incorrectly. This flag enables optimized LayerNorm kernels. Use True for Ampere/Hopper (sm_80-sm_90) and False for Blackwell (sm_100+). Using True on Blackwell causes nvcc compilation failure.

5. Omitting --dtype bf16. Always specify bf16 for correct GPU precision.

6. Reading wrong output path. Look for summary.csv under <output_dir>/design_outputs/<task_name>/. If not found, search recursively for summary.csv in the output directory tree.

7. Using auth_asym_id instead of label_asym_id. PXDesign uses label_asym_id internally, NOT auth_asym_id from PDB. When you download a CIF from PDB, the chain letters in auth_asym_id (e.g. R, H, L) may differ from label_asym_id (e.g. A, B, C). Always parse the CIF to find the correct label_asym_id chain letters before writing the YAML config. See the pre-flight validation section for a parsing snippet.

8. Confusing presets. preview is for exploration; extended is for production. Do not send preview results to experimental validation without re-running on extended.

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7. Troubleshooting

Symptom	Likely Cause	Fix
ModuleNotFoundError on import	Missing PROTENIX_DATA_ROOT_DIR or TOOL_WEIGHTS_ROOT	Set all 4 env vars in the Bash command
CUDA OOM	Binder too long or target too large for GPU memory	Reduce binder_length, use crop ranges, or use a larger GPU
Empty summary.csv	All designs filtered out	Lower thresholds or increase --N_sample
No summary.csv found	Wrong output path or run crashed early	Check Bash stderr for errors; search recursively for any CSV
cutlass errors	Missing or wrong CUTLASS_PATH	Verify $HOME/cutlass exists and contains CUTLASS build
Very slow on preview	GPU not detected, running on CPU	Verify CUDA is available; check nvidia-smi
FileNotFoundError for target	Wrong path in YAML config	Use absolute paths for target.file
fastfold_layer_norm_cuda compilation failure	GPU arch not in gencode list (Blackwell sm_100+)	Use --use_fast_ln False
ValueError: Chain X does not exist	Using auth_asym_id instead of label_asym_id	Parse CIF for label_asym_id chain letters; see pre-flight validation
DeepSpeed import error	Missing CUDA_HOME	Set CUDA_HOME to the directory containing bin/nvcc (e.g. protenix conda env)
XlaRuntimeError: NOT_FOUND: ptxas	ptxas not on PATH for JAX/XLA compilation	Add protenix conda env bin to PATH: PATH=/path/to/protenix/bin:$PATH

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8. Examples

Example 1: Basic Binder Design (Preview)

Design a 100-residue binder against chain A of a target structure.

Step 1 -- Write config:

target:
  file: "/data/targets/IL6R.cif"
  chains:
    A: "all"
binder_length: 100

Write to /data/runs/il6r_binder/config.yaml.

Step 2 -- Run CLI:

PROTENIX_DATA_ROOT_DIR=$PROTEUS_PROT_DIR/release_data/ccd_cache \
TOOL_WEIGHTS_ROOT=$PROTEUS_PROT_DIR/tool_weights \
CUTLASS_PATH=$HOME/cutlass \
CUDA_HOME=$CUDA_HOME \
pxdesign pipeline \
  --preset preview \
  -i /data/runs/il6r_binder/config.yaml \
  --N_sample 10 \
  --dtype bf16 \
  --use_fast_ln True \
  -o /data/runs/il6r_binder/output

Step 3 -- Read results: Read /data/runs/il6r_binder/output/design_outputs/*/summary.csv and present the top designs ranked by ptx_iptm.

Example 2: Hotspot-Directed Design (Extended)

Design a compact 80-residue binder targeting specific epitope residues on two chains. User specified hotspots: A40, A50, A55, B10, B15.

Step 1 -- Write config:

target:
  file: "/data/targets/receptor_complex.cif"
  chains:
    A:
      hotspots: [40, 50, 55]
    B:
      hotspots: [10, 15]
binder_length: 80

Write to /data/runs/receptor_hotspot/config.yaml.

Step 2 -- Run CLI:

PROTENIX_DATA_ROOT_DIR=$PROTEUS_PROT_DIR/release_data/ccd_cache \
TOOL_WEIGHTS_ROOT=$PROTEUS_PROT_DIR/tool_weights \
CUTLASS_PATH=$HOME/cutlass \
CUDA_HOME=$CUDA_HOME \
pxdesign pipeline \
  --preset extended \
  -i /data/runs/receptor_hotspot/config.yaml \
  --N_sample 20 \
  --dtype bf16 \
  --use_fast_ln True \
  -o /data/runs/receptor_hotspot/output

Step 3 -- Read and interpret. Rank by ptx_iptm, verify ptx_success or ptx_basic_success flags, and run through screening (by-screening skill) before presenting to user.

Example 3: Large Target with Crop Ranges and MSA

Target has a 600-residue chain but the binding site is in residues 200-350. Crop to focus compute on the relevant region.

Step 1 -- Write config:

target:
  file: "/data/targets/large_target.cif"
  chains:
    A:
      crop: ["200-350"]
      hotspots: [250, 275, 290, 310]
      msa: "/data/msas/large_target_A"
binder_length: 120

Write to /data/runs/large_target_crop/config.yaml.

Step 2 -- Run CLI:

PROTENIX_DATA_ROOT_DIR=$PROTEUS_PROT_DIR/release_data/ccd_cache \
TOOL_WEIGHTS_ROOT=$PROTEUS_PROT_DIR/tool_weights \
CUTLASS_PATH=$HOME/cutlass \
CUDA_HOME=$CUDA_HOME \
pxdesign pipeline \
  --preset extended \
  -i /data/runs/large_target_crop/config.yaml \
  --N_sample 15 \
  --dtype bf16 \
  --use_fast_ln True \
  -o /data/runs/large_target_crop/output

Step 3 -- Read results and screen as usual. Crop ranges significantly reduce GPU memory usage and runtime for large targets.

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9. Residue Numbering Utility

PDB structures have two numbering schemes: auth_asym_id/auth_seq_id (from the original publication) and label_asym_id/label_seq_id (standardized by PDB). PXDesign uses the label_* scheme. Use this snippet to translate:

# Parse CIF for label_asym_id mapping
from Bio.PDB import MMCIFParser
parser = MMCIFParser(QUIET=True)
structure = parser.get_structure('target', 'target.cif')
for model in structure:
    for chain in model:
        residues = list(chain.get_residues())
        print(f"Chain {chain.id} (label_asym_id): {len(residues)} residues, range {residues[0].id[1]}-{residues[-1].id[1]}")

When the user provides hotspot residues in auth numbering (e.g. from UniProt or literature), use this script to find the matching label_seq_id values before writing the YAML config.

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10. Post-Design Workflow

After collecting PXDesign results:

1. Screen all designs using the by-screening skill (structural confidence, liability checks, developability).
2. Refold top candidates with proteus-fold for independent structure validation.
3. Score refolded structures with ipSAE (by-scoring skill) for interface quality assessment.
4. Rank using composite scoring (by-scoring skill).
5. Present final candidates with all metrics in a results table.

If no designs pass screening, consider:

• Increasing --N_sample (more designs = more chances)
• Switching from preview to extended preset
• Adding or adjusting hotspot residues
• Modifying binder_length (see sizing guide in Section 2)
• Using crop ranges to focus on a smaller epitope region