protenix

name	protenix
description	Structure prediction using Protenix v1 (AF3-class, 368M params). Use this skill when: (1) Predicting protein or complex structure from sequence, (2) Validating designed binders by refolding, (3) Generating confidence metrics (ipTM, pTM, pLDDT), (4) Multi-seed ensemble validation, (5) Predicting protein-ligand complexes. For design generation, use proteus-prot or boltzgen instead. For scoring existing predictions, use by-scoring. For full pipeline orchestration, use by-design-workflow.
category	tool
tags	["structure-prediction","protenix","fold","validation","confidence"]

proteus-fold — Structure Prediction Skill

Protenix v1 is an AF3-class structure prediction model (368M params). This skill teaches you to run predictions via the CLI using the Write → Bash → Read pattern.

---

1. Prerequisites

Requirement	Value
Tool path	$PROTEUS_FOLD_DIR
Env var	PROTENIX_ROOT_DIR=$PROTEUS_FOLD_DIR
GPU	Required (CUDA). bf16 precision by default
CLI binary	protenix (on PATH when env var is set)

---

2. When to Use

User wants to...
|
+-- Predict structure from sequence(s)
|   --> proteus-fold (this skill)
|
+-- Validate a designed binder (refold test)
|   --> proteus-fold with binder + target chains
|
+-- Get confidence metrics (ipTM, pLDDT, pTM)
|   --> proteus-fold, then read confidence JSON
|
+-- Predict protein-ligand complex
|   --> proteus-fold with ligand entity type
|
+-- Design a NEW binder
|   --> NOT this skill. Use proteus-prot or boltzgen

---

3. How to Run (Write → Bash → Read)

Step 1: Write input JSON

Use the Write tool to create the input JSON file:

[
  {
    "name": "my_prediction",
    "sequences": [
      {"proteinChain": {"sequence": "MKWVTFISLL...", "count": 1}},
      {"proteinChain": {"sequence": "DIQMTQSPSS...", "count": 1}}
    ],
    "modelSeeds": [42],
    "sampleCount": 1
  }
]

Write this to a working directory, e.g. /tmp/fold_run/input.json.

Step 2: Run CLI via Bash

PROTENIX_ROOT_DIR=$PROTEUS_FOLD_DIR protenix pred \
  -i /tmp/fold_run/input.json \
  -n protenix_base_default_v1.0.0 \
  --use_default_params true \
  --dtype bf16 \
  -o /tmp/fold_run/output

Step 3: Read output

Use Glob to find confidence files:

*_summary_confidence_sample_*.json

Then Read the JSON to extract metrics: iptm, ptm, plddt, ranking_score.

Structure files are *_sample_*.cif in the same output directory tree.

---

4. Models

Key	Full Model Name	Use Case
base_default	protenix_base_default_v1.0.0	Recommended. Production predictions, validation
base_20250630	protenix_base_20250630_v1.0.0	Latest checkpoint. Try if base_default underperforms
mini	protenix_mini_default_v0.5.0	Fast screening. Lower accuracy, 3-5x faster
tiny	protenix_tiny_default_v0.5.0	Debugging and pipeline testing only
mini_esm	protenix_mini_esm_v0.5.0	Mini with ESM embeddings. Better single-chain accuracy

Model selection:

• Default to base_default for all real predictions.
• Use mini only for rapid iteration / feasibility checks.
• Use tiny only for testing the pipeline, never for real predictions.
• Try base_20250630 when base_default gives borderline confidence.

---

5. CLI Parameters

Flag	Value	Required	Notes
-i	Path to input JSON	Yes	JSON array format (see Section 7)
-n	Model name string	Yes	Full name from Models table
--use_default_params	true	Yes	Always set to true
--dtype	bf16	Yes	bf16 precision (GPU required)
-o	Output directory path	No	Defaults to ./output/ if omitted

---

6. Output Format

Directory structure

output/
  my_prediction/
    seed_42/
      my_prediction_summary_confidence_sample_0.json
      my_prediction_sample_0.cif

Confidence JSON fields

Field	Type	Description
iptm	float or list[float]	Interface predicted TM-score (0-1). Key metric for complexes
ptm	float or list[float]	Predicted TM-score (0-1). Overall fold quality
plddt	float or list[float]	Per-residue confidence average (0-100)
ranking_score	float or list[float]	Composite ranking score. Use this to pick best sample

Note: Metrics may be single-element lists. Always index [0] or handle both forms.

Best sample selection

When running multiple samples (sampleCount > 1) or seeds, select the sample with the highest ranking_score across all seed/sample combinations.

Quality interpretation

Metric	Minimum	Good	Excellent
ipTM	> 0.5	> 0.7	> 0.85
pLDDT	> 70	> 80	> 90
pTM	> 0.5	> 0.7	> 0.85

---

7. Input JSON Specification

The input is a JSON array containing one prediction object. See references/input-json-spec.md for the complete specification.

Quick reference

[
  {
    "name": "prediction_name",
    "sequences": [ ...entity objects... ],
    "modelSeeds": [42],
    "sampleCount": 1
  }
]

Entity types in sequences array

Type	Format
Protein chain	{"proteinChain": {"sequence": "MKWV...", "count": 1}}
Ligand	{"ligand": {"smiles": "CCO", "count": 1}}

Plain strings in sequences are auto-wrapped as proteinChain entities.

---

8. Common Mistakes

Mistake	Consequence	Fix
Forgetting PROTENIX_ROOT_DIR env var	protenix command not found or model loading fails	Always set PROTENIX_ROOT_DIR=$PROTEUS_FOLD_DIR
Using model key instead of full name	CLI error: unknown model	Use full name: protenix_base_default_v1.0.0
Input JSON not wrapped in array	Parse error	Input must be [{...}], not {...}
Single seed for validation decisions	Overconfident conclusions	Use 3-5 seeds for validation: [42, 123, 456, 789, 1024]
Using mini/tiny for production	Poor accuracy, unreliable confidence	Use base_default or base_20250630
Omitting target chain in complex validation	ipTM will be meaningless (no interface)	Include ALL chains in the complex
Not checking for list-wrapped metrics	Code crash on float > 0.5 with a list	Always handle metric[0] or check type
Using MCP function names	Functions don't exist in CLI context	Use Write → Bash → Read pattern only

---

9. Troubleshooting

Symptom	Likely Cause	Solution
protenix: command not found	Missing env var	Set PROTENIX_ROOT_DIR=$PROTEUS_FOLD_DIR
CUDA out of memory	Sequence too long or too many samples	Reduce sampleCount, use mini model, or split chains
All confidence scores near zero	Malformed input JSON	Verify JSON structure matches spec exactly
ipTM = 0 but pLDDT is reasonable	Single chain predicted (no interface)	Ensure multiple chains are in sequences
Output directory empty	CLI error during run	Check stderr from Bash for error messages
Very slow prediction	Large complex + base model	Expected for >1000 residues. Use mini for quick check

---

10. Examples

Example 1: Single protein structure prediction

Step 1 — Write /tmp/fold_run/input.json:
[
  {
    "name": "lysozyme_pred",
    "sequences": [
      {"proteinChain": {"sequence": "KVFGRCELAA...", "count": 1}}
    ],
    "modelSeeds": [42],
    "sampleCount": 1
  }
]

Step 2 — Bash:
PROTENIX_ROOT_DIR=$PROTEUS_FOLD_DIR protenix pred \
  -i /tmp/fold_run/input.json \
  -n protenix_base_default_v1.0.0 \
  --use_default_params true \
  --dtype bf16 \
  -o /tmp/fold_run/output

Step 3 — Read confidence JSON and .cif structure file.

Example 2: Multi-chain complex (binder validation)

Validate a designed binder against its target by predicting the complex:

Step 1 — Write /tmp/fold_complex/input.json:
[
  {
    "name": "binder_target_complex",
    "sequences": [
      {"proteinChain": {"sequence": "MKTAYIAKQR...", "count": 1}},
      {"proteinChain": {"sequence": "DIQMTQSPSS...", "count": 1}}
    ],
    "modelSeeds": [42],
    "sampleCount": 3
  }
]

Step 2 — Bash:
PROTENIX_ROOT_DIR=$PROTEUS_FOLD_DIR protenix pred \
  -i /tmp/fold_complex/input.json \
  -n protenix_base_default_v1.0.0 \
  --use_default_params true \
  --dtype bf16 \
  -o /tmp/fold_complex/output

Step 3 — Glob for *_summary_confidence_sample_*.json files.
         Read each, pick sample with highest ranking_score.
         ipTM > 0.7 = confident complex. ipTM < 0.5 = likely not binding.

Example 3: Multi-seed ensemble validation

For critical validation decisions, use multiple seeds to assess prediction stability:

Step 1 — Write /tmp/fold_ensemble/input.json:
[
  {
    "name": "ensemble_validation",
    "sequences": [
      {"proteinChain": {"sequence": "MKTAYIAKQR...", "count": 1}},
      {"proteinChain": {"sequence": "EVQLVESGG...", "count": 1}}
    ],
    "modelSeeds": [42, 123, 456, 789, 1024],
    "sampleCount": 1
  }
]

Step 2 — Bash (same CLI command as above).

Step 3 — Read confidence JSON from each seed directory.
         Report: mean ipTM, std ipTM, per-seed breakdown.
         Low variance (std < 0.05) = robust prediction.
         High variance (std > 0.1) = uncertain, interpret with caution.

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11. Integration with Other Skills

• After proteus-fold: Use by-scoring for ipSAE from PAE matrices, by-screening for full liability battery.
• Before proteus-fold: Use by-database to fetch target sequences from PDB/UniProt.
• Workflow context: by-design-workflow orchestrates when to call proteus-fold in the overall pipeline.