원클릭으로
molclaw-evobind-tool
Design linear or cyclic peptide binders from receptor FASTA sequences using EvoBind2 with structured result outputs.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
메뉴
Design linear or cyclic peptide binders from receptor FASTA sequences using EvoBind2 with structured result outputs.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules.
Predict binding affinity between target protein sequence and small molecule SMILES using Boltz-2.
Predict protein structures with Chai-1 from sequence or FASTA input and return model scoring summaries.
Chroma toolkit skill covering chroma_monomer for single-chain generation, chroma_complex for multi-chain assembly generation, and chroma_symmetry for symmetry-constrained protein design.
Retrieve SMILES strings from PubChem database using compound names.
Generate entirely new drug-like molecules from scratch without any starting molecule, using REINVENT4's de novo prior.
| 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"} |
Note:
drugsda-file-transfer before execution.drugsda-fix_pdb before execution.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"]
# 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
}
After calling EvoBind, programmatically count the actual number of designs returned. Report the ACTUAL count (not the requested num_designs). If actual < requested, note the discrepancy.
Download ALL designed peptide structure files from the output directory. These are Category A files essential for downstream Chai-1 validation and ProLIF analysis.
EvoBind's ipTM is the design tool's own scoring metric. Independent validation with Chai-1 is mandatory before concluding that a design is successful. See L2 Skill 09 for the full validation protocol.