alanine_scanning_pipeline
Alanine Scanning Mutagenesis Pipeline - Alanine scanning: design scan, compute properties for each mutant, predict interactions, and compare. Use this skill for protein biochemistry tasks involving AlanineScanningDesigner ComputeProtPara PredictDrugTargetInteraction calculate protein sequence properties. Combines 4 tools from 3 SCP server(s).
Best use case
alanine_scanning_pipeline is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Alanine Scanning Mutagenesis Pipeline - Alanine scanning: design scan, compute properties for each mutant, predict interactions, and compare. Use this skill for protein biochemistry tasks involving AlanineScanningDesigner ComputeProtPara PredictDrugTargetInteraction calculate protein sequence properties. Combines 4 tools from 3 SCP server(s).
Teams using alanine_scanning_pipeline should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/alanine_scanning_pipeline/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How alanine_scanning_pipeline Compares
| Feature / Agent | alanine_scanning_pipeline | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Alanine Scanning Mutagenesis Pipeline - Alanine scanning: design scan, compute properties for each mutant, predict interactions, and compare. Use this skill for protein biochemistry tasks involving AlanineScanningDesigner ComputeProtPara PredictDrugTargetInteraction calculate protein sequence properties. Combines 4 tools from 3 SCP server(s).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Alanine Scanning Mutagenesis Pipeline
**Discipline**: Protein Biochemistry | **Tools Used**: 4 | **Servers**: 3
## Description
Alanine scanning: design scan, compute properties for each mutant, predict interactions, and compare.
## Tools Used
- **`AlanineScanningDesigner`** from `server-28` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent`
- **`ComputeProtPara`** from `server-29` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio`
- **`PredictDrugTargetInteraction`** from `server-29` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio`
- **`calculate_protein_sequence_properties`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool`
## Workflow
1. Design alanine scan mutants
2. Compute parameters for mutants
3. Predict interactions for mutants
4. Compare protein properties
## Test Case
### Input
```json
{
"sequence": "MKTIIALSYIFCLVFA"
}
```
### Expected Steps
1. Design alanine scan mutants
2. Compute parameters for mutants
3. Predict interactions for mutants
4. Compare protein properties
## Usage Example
> **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn).
```python
import asyncio
import json
from contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"server-28": "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent",
"server-29": "https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio",
"server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool"
}
async def connect(url, stack):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
read, write, _ = await stack.enter_async_context(transport)
ctx = ClientSession(read, write)
session = await stack.enter_async_context(ctx)
await session.initialize()
return session
def parse(result):
try:
if hasattr(result, 'content') and result.content:
c = result.content[0]
if hasattr(c, 'text'):
try: return json.loads(c.text)
except: return c.text
return str(result)
except: return str(result)
async def main():
async with AsyncExitStack() as stack:
# Connect to required servers
sessions = {}
sessions["server-28"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", stack)
sessions["server-29"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", stack)
sessions["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack)
# Execute workflow steps
# Step 1: Design alanine scan mutants
result_1 = await sessions["server-28"].call_tool("AlanineScanningDesigner", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Compute parameters for mutants
result_2 = await sessions["server-29"].call_tool("ComputeProtPara", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Predict interactions for mutants
result_3 = await sessions["server-29"].call_tool("PredictDrugTargetInteraction", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Compare protein properties
result_4 = await sessions["server-2"].call_tool("calculate_protein_sequence_properties", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())
```Related Skills
molecular_docking_pipeline
Molecular Docking Pipeline - Complete docking workflow: retrieve protein structure, predict binding pockets, prepare receptor, and dock ligand. Use this skill for structural biology tasks involving retrieve protein data by pdbcode run fpocket convert pdb to pdbqt dock quick molecule docking. Combines 4 tools from 2 SCP server(s).
interproscan_pipeline
InterProScan Analysis Pipeline - Run InterProScan for domain analysis, then enrich with UniProt data and STRING interactions. Use this skill for functional proteomics tasks involving interproscan analyze get uniprotkb entry by accession get functional enrichment query interpro. Combines 4 tools from 4 SCP server(s).
gene_to_drug_pipeline
Gene-to-Drug Discovery Pipeline - Full gene-to-drug pipeline: gene lookup, protein structure, binding pocket, virtual screening, and drug-likeness. Use this skill for translational medicine tasks involving get gene metadata by gene name pred protein structure esmfold run fpocket boltz binding affinity calculate mol drug chemistry. Combines 5 tools from 3 SCP server(s).
disease_compound_pipeline
Disease-Specific Compound Screening - Screen compounds for disease: get DLEPS score for disease relevance, predict ADMET, and check drug-likeness. Use this skill for drug discovery tasks involving calculate dleps score pred molecule admet calculate mol drug chemistry get compound by name. Combines 4 tools from 3 SCP server(s).
compound_to_drug_pipeline
Compound-to-Drug Analysis Pipeline - Full compound-to-drug pipeline: name-to-SMILES conversion, structure analysis, drug-likeness, and FDA drug lookup. Use this skill for drug development tasks involving NameToSMILES ChemicalStructureAnalyzer calculate mol drug chemistry get drug by name. Combines 4 tools from 4 SCP server(s).
alphafold_structure_pipeline
AlphaFold Structure Analysis Pipeline - AlphaFold pipeline: download predicted structure, predict pockets, extract sequence, and compute properties. Use this skill for computational biology tasks involving download alphafold structure run fpocket extract pdb sequence calculate pdb basic info. Combines 4 tools from 3 SCP server(s).
wind-site-assessment
Assess wind energy potential and perform site analysis using atmospheric science calculations.
web_literature_mining
Scientific Literature Mining - Mine scientific literature: PubMed search, arXiv search, web search, and Tavily deep search. Use this skill for scientific informatics tasks involving pubmed search search literature search web tavily search. Combines 4 tools from 2 SCP server(s).
virus_genomics
Virus Genomics Analysis - Analyze virus genomics: NCBI virus dataset, annotation, taxonomy, and literature search. Use this skill for virology tasks involving get virus dataset report get virus annotation report get taxonomy search literature. Combines 4 tools from 2 SCP server(s).
virtual_screening
Virtual Screening Pipeline - Virtual screening: search PubChem by substructure, compute similarity, filter by drug-likeness, and predict binding affinity. Use this skill for drug discovery tasks involving search pubchem by smiles calculate smiles similarity calculate mol drug chemistry boltz binding affinity. Combines 4 tools from 3 SCP server(s).
variant_pathogenicity
Variant Pathogenicity Assessment - Assess variant pathogenicity: Ensembl VEP prediction, ClinVar lookup, variation details, and gene phenotype associations. Use this skill for clinical genetics tasks involving get vep hgvs clinvar search get variation get phenotype gene. Combines 4 tools from 2 SCP server(s).
variant-population-frequency
Query gnomAD for variant allele frequency across populations. Uses FAVOR to convert rsID→variant_id first, then queries gnomAD.