AttnPacker Sidechain Packing

SkillDev tools

Predicts full-atom sidechain conformations from backbone PDBs using AttnPacker for structure preparation workflows.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the AttnPacker Sidechain Packing skill

What this skill tells your AI

The instructions your AI receives, as published by internscience/molclaw in skills/L1_tools/molclaw-pack-sidechains/SKILL.md and read by ahel’s review.

Note:

  • Local files are not directly accessible by the server. Please upload them to the server using molclaw-file-transfer before execution.
  • For PDB file inputs, it is recommended to preprocess them using molclaw-pdbfixer before execution.
  • Please refer to skill molclaw-scp-server to complete tool invocation.

Usage

1. Protein Sidechain Packing

The description of tool pack_sidechains.

Predict full-atom sidechain conformations from backbone PDBs for protein structure preparation workflows.
Args:
  input_pdb (str): Input PDB file path, required.
  device (str|None): Compute device such as cuda:0, default None (auto by source script).
  chunk_size (int): Inference chunk size for long proteins, default 500.
  no_post_process (bool): Skip rotamer post-processing for faster runtime, default False.
  max_optim_iters (int): Maximum optimization iterations in post-process, default 250.
  steric_wt (float): Steric clash penalty weight, default 1.0.
  optim_repeats (int): Post-process optimization repeats, default 2.
  dry_run (bool): Create a traceable run directory without running inference, default False.
Return:
  status (str): 'success', 'error', or 'partial_success'.
  msg (str): Human-readable execution message.
  input_pdb (str): Input PDB path used for this run.
  output_dir (str): Unique run directory under tool_result/pack_sidechains_result.
  output_pdb (str): Expected or generated output PDB path.
  device (str|None): Device value used for execution.
  chunk_size (int): Chunk size used.
  no_post_process (bool): Whether post-process was skipped.
  max_optim_iters (int): Max optimization iterations used.
  steric_wt (float): Steric weight used.
  optim_repeats (int): Optimization repeats used.
  dry_run (bool): Whether dry-run mode was used.
  error_type (str, optional): Exception type when status is 'error'.
  traceback (str, optional): Python traceback when status is 'error'.

How to use tool pack_sidechains :

response = await client.session.call_tool(
    "pack_sidechains",
    arguments={
        "input_pdb": "/path/to/input.pdb",
        "device": "cuda:0",
        "chunk_size": 500,
        "no_post_process": False,
        "max_optim_iters": 250,
        "steric_wt": 1.0,
        "optim_repeats": 2,
        "dry_run": False
    }
)
result = client.parse_result(response)
output_pdb = result["output_pdb"]

Example parameter sets
# 1) Main mode
{
    "input_pdb": "/path/to/input.pdb",
    "device": "cuda:0",
    "chunk_size": 500,
    "no_post_process": False,
    "max_optim_iters": 250,
    "steric_wt": 1.0,
    "optim_repeats": 2,
    "dry_run": False
}

# 2) Variant mode
{
    "input_pdb": "relative/path/to/test_backbone.pdb",
    "chunk_size": 500,
    "dry_run": True
}

Signals

GitHub stars
33
Forks
3
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
molclaw-pack-sidechains
Source
github.com/internscience/molclaw