| name | molclaw-chai1-predict |
| description | Predict protein structures with Chai-1 from sequence or FASTA input and return model scoring summaries. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
Chai-1 Protein Structure Prediction
Note:
- Local files are not directly accessible by the server. Please upload them to the server using
drugsda-file-transfer before execution.
- For PDB file inputs, it is recommended to preprocess them using
drugsda-fix_pdb before execution.
Usage
2. Chai-1 Prediction (Sequence/FASTA)
The description of tool chai1_predict.
Predict protein structures with Chai-1 from sequence or FASTA input, run inference (unless dry-run), and return per-model scoring summaries for downstream selection.
Args:
mode (str): One of 'sequence', 'fasta', or 'info'; API also accepts 'predict' as an alias of 'sequence'.
seq (str|None): Comma-separated protein sequence(s) for sequence mode, e.g., "MKFL...,AIQR...".
name (str|None): Comma-separated chain names corresponding to `seq`; defaults to chain_1, chain_2, ... if omitted.
fasta_path (str|None): Path to an input FASTA file for fasta mode.
samples (int): Number of models/samples to generate, must be >= 1. Default: 5.
dry_run (bool): If True, only prepare inputs and write `input.fasta` without running Chai-1 inference.
Return:
status (str): 'success' or 'error'.
msg (str): Human-readable summary or error message.
output_dir (str|None): Run artifact directory path.
model_scores (List[dict]|None): Per-model summaries with keys 'model_idx', 'cif_path', 'scores', and 'score_path'.
best_model (dict|None): Top model summary with keys 'model_idx', 'aggregate_score', and 'cif_path'.
How to use tool chai1_predict :
response = await client.session.call_tool(
"chai1_predict",
arguments={
"mode": "sequence",
"seq": "MKFLILLFNILCLFPVLAADNHGVS",
"name": "my_protein",
"samples": 5,
"dry_run": True
}
)
result = client.parse_result(response)
best_model = result["best_model"]
Example parameter sets
{
"mode": "predict",
"seq": "MKFLILLFNILCLFPVLAADNHGVS",
"name": "my_protein",
"dry_run": True
}
{
"mode": "fasta",
"fasta_path": "/abs/path/input.fasta",
"samples": 5,
"dry_run": True
}
{
"mode": "info"
}
⚠ Mandatory Output File Download (L3 Principle 14)
After calling this tool, you MUST download all output structure files from the MCP server to the local workspace using server_file_to_base64. A tool call is NOT considered complete until its output files have been downloaded and verified locally (ls -la <file> — size must be > 0).
import base64, os
response = await client.session.call_tool(
"server_file_to_base64",
arguments={"file_path": result["output_file"]}
)
dl = client.parse_result(response)
local_path = "stepNN_descriptive_name.ext"
with open(local_path, "wb") as f:
f.write(base64.b64decode(dl["base64_string"]))
assert os.path.getsize(local_path) > 0, f"Download failed: {local_path}"
Download policy: All structure output files are Category A (user-critical) — essential for user verification, downstream analysis, and reproducibility. When in doubt, download. Over-collection is always preferred over under-collection.
⚠ Confidence Metric Checkpoint (L3 Principle 12 Checkpoint A)
After Chai-1 prediction, check confidence metrics:
- pTM > 0.5: Reasonable fold prediction
- ipTM > 0.4: Complex prediction may be meaningful. ipTM < 0.4 suggests unreliable complex interface.
- pLDDT < 60: Unreliable prediction — flag in report
⚠ Numbering Scheme (L3 Principle 17)
Chai-1 predicted structures use 1-based sequential numbering from the input sequence — NOT UniProt numbering. If downstream analysis (ProLIF, per-residue decomposition) references specific residues, apply molclaw-residue-mapper first.
