internscience/scp/alanine-scanning-pipeline

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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).

Install

npx vskill@latest install internscience/scp/alanine-scanning-pipeline

SKILL.md

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 <YOUR_SCP_HUB_API_KEY> with your own SCP Hub API Key. You can obtain one from the SCP Platform.
python
import asyncio
import json
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, transport_type):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
read, write, _ = await transport.__aenter__()
ctx = ClientSession(read, write)
session = await ctx.__aenter__()
await session.initialize()
return session, ctx, transport
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():
# Connect to required servers
sessions = {}
sessions["server-28"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "sse")
sessions["server-29"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", "sse")
sessions["server-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")
# 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())

Security

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Source

Popularity

Repository stars
150
Repository forks
10
7-day trend score
25

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 22 Mar 2026, 7 months ago.

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