molclaw-evobind-tool

name	molclaw-evobind-tool
description	Design linear or cyclic peptide binders from receptor FASTA sequences using EvoBind2 with structured result outputs.
license	MIT license
metadata	{"skill-author":"PJLab"}

EvoBind2 Peptide Binder Design

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. EvoBind2 Binder Design

The description of tool evobind_tool.

Design linear or cyclic peptide binders from a receptor sequence using EvoBind2 in structure-guided screening workflows.
Args:
  fasta (str): Receptor FASTA file path.
  peptide_length (int): Binder peptide length, default 10.
  num_designs (int): Number of independent design rounds, default 10.
  num_iterations (int): Monte Carlo iterations per round, default 100.
  max_recycles (int): AlphaFold2 recycle count, default 1.
  model_name (str): AlphaFold2 model in {model_1, model_2, model_3, model_4, model_5}, default model_1.
  target_residues (str): Receptor target residues as comma-separated 1-indexed positions or all, default all.
  cyclic (bool): Whether to enable cyclic peptide design, default False.
  msa_file (str|None): Optional precomputed MSA .a3m file path, default None.
  dry_run (bool): Whether to print planned commands without executing design rounds, default False.
  skip_env_check (bool): Whether to skip source workflow environment checks, default False.
Return:
  status (str): success, error, or partial_success execution status.
  msg (str): Human-readable execution summary.
  output_dir (str): Unique run directory under tool_result/evobind_tool_result.
  fasta (str): Resolved absolute FASTA input path.
  peptide_length (int): Effective peptide length used in this run.
  num_designs (int): Effective number of design rounds used in this run.
  num_iterations (int): Effective number of iterations used in this run.
  max_recycles (int): Effective recycle count used in this run.
  model_name (str): Effective model name used in this run.
  target_residues (str): Effective target residue specification used in this run.
  cyclic (bool): Effective cyclic flag used in this run.
  dry_run (bool): Effective dry-run flag used in this run.
  skip_env_check (bool): Effective environment-check skip flag used in this run.
  output_files (dict): Key output file paths including run logs and summary artifacts when available.
  metrics (dict): Parsed summary metrics such as candidate count and top-ranked scores when available.

How to use tool evobind_tool :

response = await client.session.call_tool(
    "evobind_tool",
    arguments={
        "fasta": "relative/path/to/receptor.fasta",
        "peptide_length": 10,
        "num_designs": 10,
        "num_iterations": 100,
        "max_recycles": 1,
        "model_name": "model_1",
        "target_residues": "all",
        "cyclic": False,
        "dry_run": False,
        "skip_env_check": False
    }
)
result = client.parse_result(response)
key_output = result["output_dir"]

Example parameter sets

# 1) Main mode
{
    "fasta": "relative/path/to/1ssc_receptor.fasta",
    "peptide_length": 10,
    "num_designs": 10,
    "num_iterations": 100,
    "max_recycles": 1,
    "model_name": "model_1",
    "target_residues": "all",
    "cyclic": False,
    "dry_run": False,
    "skip_env_check": False
}

# 2) Variant mode
{
    "fasta": "relative/path/to/target.fasta",
    "peptide_length": 12,
    "num_designs": 50,
    "num_iterations": 500,
    "max_recycles": 3,
    "model_name": "model_2",
    "target_residues": "10,15,20,25",
    "cyclic": True,
    "msa_file": "relative/path/to/receptor.a3m",
    "dry_run": True,
    "skip_env_check": True
}